Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications.

This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm.

This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.

This paper gives a brief introduction to the starting points of an experimental project to study dolphin communicative behaviour using distributional semantics, with methods implemented for the large scale study of human language.

The pursuit of multi-agent exploration is driven by its capacity to enhance operational robustness and efficiency in complex, dynamic environments, paving the way for advancements in autonomous systems and robotics. This thesis explores the development and assessment of decentralised planning algorithms within multi-agent systems, using the CARLA simulation environment. A methodology combining simulation-based testing and theoretical analysis was employed to evaluate the efficiency, and scalability of various decentralised planning strategies. 

The study systematically analysed three different exploration strategies for multi-agent systems: Greedy, MinPos, and Hungarian Assignments, across various configurations concerning the number of agents and communication demands. The Hungarian Assignment strategy demonstrates the highest efficiency in area coverage and coordination, especially as the number of agents increases. Meanwhile, the Greedy Assignment strategy requires the least communication bandwidth, indicating its potential for scenarios with limited communication capabilities. The MinPos Assignment, while facilitating better spatial distribution of agents than the Greedy Assignment, showed a moderate increase in communication demands and did not significantly outperform the Greedy Assignment in terms of efficiency.

This work contributes to the field by providing insights into the trade-offs between exploration efficiency and communication overhead in multi-agent systems. Future work could explore synchronisation mechanisms, collision-avoidance strategies, and further decentralisation of the system's components.

Over the years, the Artificial Intelligence and Integrated Computer Systems (AIICS) Division at Linköping University has developed a high-level architecture for collaborative robotic systems that includes a delegation system capable of defining complex missions to be executed by a team of agents. This architecture has been used as a part of a research arena for developing and improving public safety and security using ground, aerial, surfaceand underwater robotic systems. Recently, the division decided to purchase a Boston Dynamics Spot robot to further progress into the public safety and security research area.The robot has a robotic arm and navigation functionalities such as map building, motion planning, and obstacle avoidance. This thesis investigates how the Boston Dynamics Spot robot can be integrated into the high-level architecture for collaborative robotic systems from the AIICS division. Additionally, how the robot’s functionalities can be extended so that it is capable of determining which actions it should take to achieve high-level behavioursconsidering its capabilities and current state. In this context, higher-level behaviours include picking up and delivering first aid supplies, which can be beneficial in specific emergency situations. The study was divided and done in an iterative approach.The system was tested in various scenarios that represent its intended future use. The result demonstrated the robot’s ability to plan and accomplish the desired high-level behaviours. However, there were instances when achieving the desired behaviours proved challenging due to various limiting factors, including limitations posed by the robot’s internal controller.

A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.

Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

This thesis studies motion planning for future autonomous vehicles with main focus on passenger cars. By having automatic steering and braking together with information about the environment, such as other participants in the traffic or obstacles, it would be possible to perform autonomous maneuvers while taking limitations of the vehicle and road–tire interaction into account. Motion planning is performed to find such maneuvers that bring the vehicle from the current state to a desired future state, here by formulating the motion-planning problem as an optimal control problem. There are a number of challenges for such an approach to motion planning; some of them are how to formulate the criterion in the motion planning (objective function in the corresponding optimal control problem), and how to make the solution of motion-planning problems efficient to be useful in online applications. These challenges are addressed in this thesis.

As a criterion for motion-planning problems of passenger vehicles on doublelane roads, it is investigated to use a lane-deviation penalty function to capture the observation that it is dangerous to drive in the opposing lane, but safe to drive in the original lane after the obstacle. The penalty function is augmented with certain additional terms to address also the recovery behavior of the vehicle. The resulting formulation is shown to provide efficient and steady maneuvers and gives a lower time in the opposing lane compared to other objective functions. Under varying parameters of the scenario formulation, the resulting maneuvers are changing in a way that exhibits structured characteristics.

As an approach to improve efficiency of computations for the motion-planning problem, it is investigated to segment motion planning of the full maneuver into several smaller maneuvers. A way to extract segments is considered from a vehicle dynamics point of view, and it is based on extrema of the vehicle orientation and the yaw rate. The segmentation points determined using this approach are observed to allow efficient splitting of the optimal control problem for the full maneuver into subproblems.

Having a method to segment maneuvers, this thesis further studies methods to allow parallel computation of these maneuvers. One investigated method is based on Lagrange relaxation and duality decomposition. Smaller subproblems are formulated, which are governed by solving a low-complexity coordination problem. Lagrangian relaxation is performed on a subset of the dynamic constraints at the segmentation points, while the remaining variables are predicted. The prediction is possible because of the observed structured characteristics resulting from the used lane-deviation penalty function. An alternative approach is based on adoption of the alternating augmented Lagrangian method. Augmentation of the Lagrangian allows to apply relaxation for all dynamic constraints at the segmentation points, and the alternating approach makes it possible to decompose the full problem into subproblems and coordinating their solutions by analytically solving an overall coordination problem. The presented decomposition methods allow computation of maneuvers with high correspondence and lower computational times compared to the results obtained for solving the full maneuver in one step.

Segmenting a motion-planning problem into smaller subproblems could be beneficial in terms of computational complexity. This observation is used as a basis for a new sub-maneuver decomposition approach investigated in this paper in the context of optimal evasive maneuvers for autonomous ground vehicles. The recently published alternating augmented Lagrangianmethod is adopted and leveraged on, which turns out to fit the problem formulation with several attractive properties of the solution procedure. The decomposition is based on moving the coupling constraints between the sub-maneuvers into a separate coordination problem, which is possible to solve analytically. The remaining constraints and the objective function are decomposed into subproblems, one for each segment, which means that parallel computation is possible and benecial. The method is implemented and evaluated in a safety-critical double lane-change scenario. By using the solution of a low-complexity initialization problem and applying warm-start techniques in the optimization, a solution is possible to obtain after just a few alternating iterations using the developed approach. The resulting computational time is lower than solving one optimization problem for the full maneuver.

Detta arbete beskriver en kvalitativ fallstudie som utförts för att undersöka hur ett företags produktportfölj samt varumärke kan skrivas in i gränssnittsriktlinjer för att framtida gränssnitt ska designas i enhetlighet med existerande produktportfölj samt varumärke. Fallstudien utfördes inom robotindustrin där en gränssnittsriktlinje utvecklades. Gränssnittet som var föremål för studien fanns på en handhållen enhet vilken används för att programmera och manövrera en robot. En arbetsgång i 13 steg togs fram, vilken anses kunna appliceras även på andra områden än robotindustrin för att skapa gränssnittsriktlinjer där både varumärke och produktportfölj tydligt framgår. En analys av de framtagna riktlinjerna efter utförd fallstudie, styrkte även denna uppfattning. Dock visade en andra analys att riktlinjerna verkar ha samma problem som visats i tidigare studier, nämligen att den största svårigheten är att få riktlinjer använda samt att finna en struktur i dokumentet och en nivå på riktlinjerna som passar in i användarnas behov. Studien har också visat på ett sätt att binda samma relevanta delar av både gränssnittsteorier, varumärkesteorier och teori kring interaktionsdesign.

Robots in our society are commonly perceived as subordinate servants with a lower social status than humans. This often leads to humans prioritizing themselves during conflict situations. This becomes problematic when robots start to directly represent humans as proxies if people do not think of the human operator behind them. This could be considered a cognitive bias of human representation in HRI. To explore the extent of this problem, we conducted a user study featuring several conflict situations. Participants granted more priority to the robot when the human representation was visible. This paper explores the societal consequences and emerging inequities such as potentially deprioritizing humans by deprioritizing a robot in certain situations. Possible strategies to address potential negative consequences are discussed on a design level while acknowledging that a societal change in how we perceive and treat robots that represent humans might be necessary.

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants' acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot's evaluation and effectiveness in everyday situations.

Urban Search And Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.

In this paper we give a formal characterization of reactive control using action theories. In the process we formalize the notion of a reactive control program being correct with respect to a given goal, a set of initial states, and action theories about the agent/robot and about the exogenous actions. We give sufficiency conditions that guarantee correctness and use it to give an automatic method for constructing provenly correct control modules. We then extend our approach to action theories and control modules that have specialized sensing actions and that encode conditional plans. Finally we briefly relate our theory with the implementation of our mobile robot Diablo, which successfully competed in AAAI 96 and 97 mobile robot contests.

Fossil-fuel vehicles are one of the main causes of CO2 emissions nowadays. As we are moving toward cleaner environment electrification of vehicles are becoming more and more popular. With the environment in mind, the recent improvement in battery technology and electronics has drawn a lot of attention to Brushless DC or BLDC  motors. Due to their high torque output and robust design, BLDC motors are a popular choice as the main propulsion unit in electric vehicles.  Permanent magnet synchronous motor, PMSM, is also a brushless dc motor with minor changes in design. So the word BLDC and PMSM is used interchangeably. In this thesis, two motor control algorithms were investigated. 6-Step control and Field oriented control or FOC. A three-phase inverter allows these motors to be driven by a battery. But when battery voltage goes down the speed of the motor also goes down. This thesis investigates a method to maintain the same speed at lower dc voltage. Also running of other motors than the control system was designed for. The control system performs well in simulation for two of the motor tested with the FOC algorithm. Simulation results show that the control system can track speed and current references with minimum error. Speed controller and current controllers control each parameter independently to control the motor. Low battery simulations provide useful data that shows how the field weakening technique makes it possible to achieve higher speed at low dc voltages.

Temporal projection, the process of predicting what will happen when a robot executes its plan, is essential for autonomous service robots to successfully plan their missions. This paper describes a causal model of the behavior exhibited by the mobile robot RHINO when running concurrent reactive plans for performing office delivery jobs. The model represents aspects of robot behavior that cannot be represented by most action models used in AI planning: it represents the temporal structure of continuous control processes, several modes of their interferences, and various kinds of uncertainty. This enhanced expressiveness enables XFRM, a robot planning system, to predict, and therefore forestall, various kinds of behavior flaws including missed deadlines whilst exploiting incidental opportunities. The proposed causal model is experimentally validated using the robot and its simulator.

Rich geometric models of the environment are needed for robots to carry out their missions. However a robot operating in a large environment would require a compact representation. In this article, we present a method that relies on the idea that a plane appears as a line segment in a 2D scan, and that by tracking those lines frame after frame, it is possible to estimate the parameters of that plane. The method is divided in three steps: fitting line segments on the points of the 2D scan, tracking those line segments in consecutive scan and estimating the parameters with a graph based SLAM (Simultaneous Localisation And Mapping) algorithm.

In this paper we consider a problem of scanningan outdoor area with a team of heterogeneous Unmanned AirVehicles (UAVs) equipped with different sensors (e.g. LIDARs).Depending on the availability of the UAV platforms and themission requirements there is a need to either minimise thetotal mission time or to maximise certain properties of thescan output, such as the point cloud density. The key challengeis to divide the scanning task among UAVs while taking intoaccount the differences in capabilities between platforms andsensors. Additionally, the system should be able to ensure thatconstraints such as limit on the flight time are not violated.We present an approach that uses an optimisation techniqueto find a solution by dividing the area between platforms,generating efficient scan trajectories and selecting flight andscanning parameters, such as velocity and flight altitude. Thismethod has been extensively tested on a large set of randomlygenerated scanning missions covering a wide range of realisticscenarios as well as in real flights.

The need for faster and more reliable logistics solutions is rapidly increasing. This is due to higher demands on the logistical services to improve quality,  quantity, speed and reduce the error tolerance. An arising solution to these increased demands is automated solutions in warehouses, i.e., automated material  handling. In order to provide a satisfactory solution, the vehicles need to be smart and able to solve unexpected situations without human interaction. 

The purpose of this thesis was to investigate if obstacle detection and avoidance in a semi-unknown environment could be achieved based on the data from a 2D LIDAR-scanner. The work was done in cooperation with the development of a new load-handling vehicle at Toyota Material Handling. The vehicle is navigating from a map that is created when the vehicle is introduced to the environment it will be operational within. Therefore, it cannot successfully navigate around new unrepresented obstacles in the map, something that often occurs in a material handling warehouse.

The work in this thesis resulted in the implementation of a modified occupancy grid map algorithm, that can create maps of previously unknown environments if the position and orientation of the AGV are known. The generated occupancy grid map could then be utilized in a lattice planner together with the A* planning algorithm to find the shortest path. The performance was tested in different scenarios at a testing facility at Toyota Material Handling. 

The results showed that the occupancy grid provided an accurate description of the environment and that the lattice planning provided the shortest path, given constraints on movement and allowed closeness to obstacles. However, some performance enhancement can still be introduced to the system which is further discussed at the end of the report. 

The main conclusions of the project are that the proposed solution met the requirements placed upon the application, but could benefit from a more efficient usage of the mapping algorithm combined with more extensive path planning.

A general trajectory planner for optimal control problems is presented and applied to a robot system. The approach is based on timed elastic bands and nonlinear model predictive control. By exploiting the sparsity in the underlying optimization problems the computational effort can be significantly reduced, resulting in a real-time capable planner. In addition, a localization based switching strategy is employed to enforce convergence and stability. The planning procedure is illustrated in a robotics application using a realistic SCARA type robot.

Several simulation theories have been proposed as an explanation for how humans and other agents internalize an “inner world” that allows them to simulate interactions with the external real world – prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizing and planning. In the present work, we study internal simulations in a robot acting in a simulated human environment. A model of sensory–motor interactions with the environment is generated from human demonstrations and tested on a Robosoft Kompaï robot. The model is used as a controller for the robot, reproducing the demonstrated behavior. Information from several different demonstrations is mixed, allowing the robot to produce novel paths through the environment, toward a goal specified by top-down contextual information. The robot model is also used in a covert mode, where the execution of actions is inhibited and perceptions are generated by a forward model. As a result, the robot generates an internal simulation of the sensory–motor interactions with the environment. Similar to the overt mode, the model is able to reproduce the demonstrated behavior as internal simulations. When experiences from several demonstrations are combined with a top-down goal signal, the system produces internal simulations of novel paths through the environment. These results can be understood as the robot imagining an “inner world” generated from previous experience, allowing it to try out different possible futures without executing actions overtly. We found that the success rate in terms of reaching the specified goal was higher during internal simulation, compared to overt action. These results are linked to a reduction in prediction errors generated during covert action. Despite the fact that the model is quite successful in terms of generating covert behavior toward specified goals, internal simulations display different temporal distributions compared to their overt counterparts. Links to human cognition and specifically mental imagery are discussed.

As robots become more prevalent in society, they will also become part of service systems, and will be among the materials that designers work with. The body of literature on robots in service systems is scarce, in service research as well as in service design research, especially regarding how to understand robots in service, and how design for service is impacted. In this conceptual paper we aim to shed light on how social robots will affect service. We take a look at the current state of robots’ ability to interact socially with people and highlight some of the issues that need to be considered when including social robots as part of service.

In navigating the social world, people exhibit an intentional stance, in which they rely on assumptions that social behaviour is governed by underlying mental states, such as beliefs and desires. Due to fundamental differences between humans and robots, people’s attribution of the mental state of robots, such as what a particular robot knows and believes, is often precarious and leads to uncertainty in interactions, partly relating to issues with common ground. Additionally, people might hesitate to initiate interactions with robots, based on considerations of privacy and trust, or due to negative attitudes towards them. Designing for service systems where e.g. a robot is being introduced, requires knowledge and understanding of these issues from a design perspective. Service designers therefore need to consider not only the technical aspects of robots, but the specific issues that arise in interactions because of them.

An Automated Guided Vehicle (AGV) must follow protective regulations to avoidcrashing into people when autonomously driving in industries. These safety norms require AGVs to enable protective fields, which perform hard braking when objects enter aspecific area in front of the vehicle. Warning fields, or warning systems, are similar fieldsthat decrease the speed of the AGV before objects enter the protective fields to enable asteadier driving. Today at Toyota Material Handling Manufacturing Sweden (TMHMS),warning systems have been implemented but the systems are too sensitive to objects outside of the AGVs path.The purpose of this thesis is to develop a predictive warning system based on empiricaldata from previous driving scenarios. By storing previous positions, the warning systemcould estimate a trajectory based on simple statistics and deploy speed limiting decisionsif objects appear in the upcoming predicted path.The predictive warning system was compared to the current warning system and adeactivated warning system setup in driving performance and driving dynamics. Performance was measured by measuring time to finish an industry-like test track and dynamicswas subjectively rated from a group of experienced AGV developers from TMHMS. Results showed that a predictive warning system drove the test track faster and with betterdynamics than the current warning system and the no warning system setup.Key findings are that a predictive warning system based on empirical data performedbetter in most cases but has some extra requirements to function. Firstly, the method require the AGV to mostly drive on previously driven paths to produce good results. Secondly the warning system requires a somewhat powerful on board computer to handlethe computations. Finally, the warning system requires spatial awareness of pose for thevehicle, as well as structure and shape for deployed protective fields.

This thesis explores the development and optimization of an autonomous drone system designed for patrolling national parks and assisting in search and rescue (SAR) missions. The primary objective is to create a versatile drone platform capable of operating efficiently and autonomously across various scenarios, including wildlife monitoring, anti-poaching patrols, and SAR operations. The research emphasizes optimizing flight paths to minimize distance and runtime using different search algorithms.

Key functionalities of the flight planning system include path patrol, area search, and border patrol. For path patrol, the system derives paths within a region from a larger dataset using a nearest neighbour approach, selecting the shortest possible path at each decision point. In area search, the problem is framed as the Travelling Salesman Problem (TSP), with the simulation environment creating a grid to ensure complete coverage. Multiple search algorithms were evaluated, ultimately combining the nearest neighbour and 2-opt algorithms for optimal performance.

The system integrates seamlessly with DJI’s FlightHub 2 for mission planning and execution, leveraging the capabilities of the DJI Mavic 3T drone. Real-time video analysis and object detection are conducted using a Jetson Orin Nano Developer kit, enhancing the drone's usability. This thesis demonstrates the potential of autonomous drones to improve efficiency and effectiveness in wildlife conservation and SAR missions, and created the groundwork for future improvements of the system. 

During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially occurring errors is included as unforeseeable events may happen over time. Therefore, anomaly detection (AD) delivers a practical solution, using only normal data to learn to detect unusual events. We introduce a dataset that allows training and benchmarking of anomaly detection methods for robotic applications based on machine data which will be made publicly available to the research community. As a typical robot task the dataset includes a pick-and-place application which involves movement, actions of the end effector, and interactions with the objects of the environment. Since several of the contained anomalies are not task-specific but general, evaluations on our dataset are transferable to other robotics applications as well. In addition, we present multivariate time-series flow (MVT-Flow) as a new baseline method for anomaly detection: It relies on deep-learning-based density estimation with normalizing flows, tailored to the data domain by taking its structure into account for the architecture. Our evaluation shows that MVT-Flow outperforms baselines from previous work by a large margin of 6.2% in area under receiving operator characteristic.

In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches, namely to consider the energy that is needed to deform the trajectory estimated by a SLAM approach into the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user an easy analysis and objective comparisons between different SLAM approaches.

The effects of wear to friction are studied based on constant-speed friction data collected from dedicated experiments during accelerated wear tests. It is shown how the effects of temperature and load uncertainties produce larger changes to friction than those caused by wear, motivating the consideration of these effects. Based on empirical observations, an extended friction model is proposed to describe the effects of speed, load, temperature and wear. Assuming availability of such model and constant-speed friction data, a maximum likelihood wear estimator is proposed.  A criterion for experiment design is proposed which selects speed points to collect constant-speed friction data which improves the achievable performance bound for any unbiased wear estimator. Practical issues related to experiment length are also considered. The performance of the wear estimator under load and temperature uncertainties is found by means of simulations and verified under three case studies based on real data.

This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback-Leibler distance. To decrease sensitivity to unknown disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The method was developed with interests in industrial robotics where a repetitive behavior is commonly found. The problem of wear monitoring in a robot joint is studied based on data collected from a test-cycle. Real data from accelerated wear tests and simulations are considered. Promising results are achieved where the method output shows a clear response to the wear increases.

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback–Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine.

Modern camera-based vehicle safety systems heavily rely on machine learning and consequently require large amounts of training data to perform reliably. However, collecting and annotating the needed data is an extremely expensive and time-consuming process. In addition, it is exceptionally difficult to collect data that covers critical scenarios. This thesis investigates to what extent synthetic data can replace real-world data for these scenarios. Since only a limited amount of data consisting of such real-world scenarios is available, this thesis instead makes use of proxy scenarios, e.g. situations when pedestrians are located closely in front of the vehicle (for example at a crosswalk). The presented approach involves training a detector on real-world data where all samples of these proxy scenarios have been removed and compare it to other detectors trained on data where the removed samples have been replaced with various degrees of synthetic data. A method for generating and automatically and accurately annotating synthetic data, using features in the CARLA simulator, is presented. Also, the domain gap between the synthetic and real-world data is analyzed and methods in domain adaptation and data augmentation are reviewed. The presented experiments show that aligning statistical properties between the synthetic and real-world datasets distinctly mitigates the domain gap. There are also clear indications that synthetic data can help detect pedestrians in critical traffic situations

The paper presents a light-weight and low-cost airborne terrain mapping system. The developed Airborne LiDAR Scanner (ALS) sys- tem consists of a high-precision GNSS receiver, an inertial measurement unit and a magnetic compass which are used to complement a LiDAR sensor in order to compute the terrain model. Evaluation of the accuracy of the generated 3D model is presented. Additionally, a comparison is provided between the terrain model generated from the developed ALS system and a model generated using a commer- cial photogrammetric software. Finally, the multi-echo capability of the used LiDAR sensor is evaluated in areas covered with dense vegetation. The ALS system and camera systems were mounted on-board an industrial unmanned helicopter of around 100 kilograms maximum take-off weight. Presented results are based on real flight-test data.

Immersive environments offer new possibilities for exploring three-dimensional volumetric or abstract data. However, typicalmid-air interaction offers little guidance to the user in interacting with the resulting visuals. Previous work has explored the use of hapticcontrols to give users tangible affordances for interacting with the data, but these controls have either: been limited in their range andresolution; were spatially fixed; or required users to manually align them with the data space. We explore the use of a robot arm withhand tracking to align tangible controls under the user’s fingers as they reach out to interact with data affordances. We begin witha study evaluating the effectiveness of a robot-extended slider control compared to a large fixed physical slider and a purely virtualmid-air slider. We find that the robot slider has similar accuracy to the physical slider but is significantly more accurate than mid-airinteraction. Further, the robot slider can be arbitrarily reoriented, opening up many new possibilities for tangible haptic interaction withimmersive visualisations. We demonstrate these possibilities through three use-cases: selection in a time-series chart; interactiveslicing of CT scans; and finally exploration of a scatter plot depicting time-varying socio-economic data

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The formula progression procedure for Metric Temporal Logic (MTL), originally proposed by Bacchus and Kabanza, makes use of syntactic formula rewritings to incrementally evaluate MTL formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robot applications. Our main contribution is an extension of the progression procedure to handle partial state information. For each missing truth value, we efficiently consider all consistent hypotheses by branching progression for each such hypothesis. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise partial-state progression.

Modern robotic systems often consist of a growing set of information-producing components that need to be appropriately connected for the system to function properly. This is commonly done manually or through relatively simple scripts by specifying explicitly which components to connect. However, this process is cumbersome and error-prone, does not scale well as more components are introduced, and lacks flexibility and robustness at run-time. This paper presents an algorithm for setting up and maintaining implicit subscriptions to information through its semantics rather than its source, which we call semantic subscriptions. The proposed algorithm automatically reconfigures the system when necessary in response to changes at run-time, making the semantic subscriptions adaptive to changing circumstances. To illustrate the effectiveness of adaptive semantic subscriptions, we present a case study with two SoftBank Robotics NAO robots for handling the cases when a component stops working and when new components, in this case a second robot, become available. The solution has been implemented as part of a stream reasoning framework integrated with the Robot Operating System (ROS).

In the age of extensive industrial digitalization, industrial operators are facing increasing pressure from the growing amount of data being delivered to industrial control rooms. Humans limited perceptual system is the bottleneck when it comes to processing large volumes of information. Too many screens crammed with textual and numerical data make the user disoriented in information potentially leading to fatal mistakes and insufficient situation awareness. This paper explores possibilities of guiding operators attention to the most urgent information by means of visual aid systems. In this work, two concepts of such systems were developed and evaluated in a lab environment. The evaluation results indicate a clear benefit of using the developed concepts.

This paper describes the software and hardware system developed by the University of Freiburg team of search and rescue robots for the RoboCup Res- cue 2010 competition. This system is an extension to the software that finished in first place the 2005 and 2006 autonomy challenge, focusing on two key areas: autonomous navigation and manipulation. Our team, consisting mainly of students, originates from the former CS Freiburg team (RoboCupSoccer), the ResQ Freiburg team (RoboCupRescue Simulation), and RescueRobots Freiburg teams ’05 and ’06.

To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot’s motion. Each image vector is taken for a voting that best explains the robot’s motion. Image vectors vote according to a previously trained tile coding classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.

In this paper we introduce our team’s approach to the TechX Challenge, which is based on experiences gathered at RoboCup during the last seven years and recent efforts in robotic research. We particularly focus on Multi-Level Surface (MLS) maps based localization, behavior map based path planning and obstacle negotiation, robot motion planning using a probabilistic roadmap planner, vision and 3D laser supported target detection, which all will be more detailed in the following sections.

Human-robot collaboration (HRC) in manufacturing environments requires that physical safety can be guaranteed. Control methods that implicitly regulate the interaction forces between a controlled robot and its environment, such as impedance control, are often used for safety in HRC. However, these methods could be complemented by restricting the robot operational space for additional safety guarantees. In this context, obstacle avoidance might benefit from considering a prediction of the controlled-robot motion and/or the behavior of the human collaborator. To this end, we proposed to include linearized Safety Control Barrier Functions (SCBFs) in a linear Model Predictive Control (MPC) strategy for robot impedance variation online. The convex optimization problem that was obtained from our proposal presented two advantages compared to nonlinear MPC alternatives. First, optimality was ensured in our method under linearity assumptions on human guidance and linearized robot dynamics, whereas a controller synthesized by nonlinear MPC strategies would depend on the fundamental characteristics of the problem. Second, our method enabled implementation at a faster control frequency, thus allowing a rapid adaptation to changes occurring in the robot environment. Finally, experimental validation was performed using a Franka Emika Panda robot in a human-robot collaborative scenario, and the stability of the method was shown using Lyapunov theory.

Deafblindness is a unique disability characterized by a dual sensory reduction of both hearing and vision. For some Deafblind individuals, communication via touch may be their most accessible sensory channel. Multiple techniques that rely purely on touch exist within the Deafblind community. One method of interest referred to as "Deafblind Tactile Fingerspelling Alphabet " in Australia or "Deafblind Manual Alphabet " in the U.K., comprises twenty-six tactile symbols representing the letters of the Latin alphabet. This letter describes the HaptiComm, a device designed to reproduce the sensations generated during fingerspelling communication. The HaptiComm comprises an array of twenty-four strategically placed electrodynamic actuators specifically designed to produce distinct tactile sensations upon which the fingerspelling alphabet is constructed. The first experimental evaluation showed promising results suggesting further investigations related to the timing and the pace at which the stimuli are produced.

The main objective of this study is to investigate whether one can use recordings of human-to-human touch, such as a caress, to improve tactile apparent motion interfaces to make them feel more natural. We report here preliminary recordings of natural and continuous human-to-human caresses. To do this, six accelerometers were positioned on the receiving hand next to the stimulated area while a finger gently stroked the skin. The results suggest that we are able to capture signals from real human caresses that can be compared to signals produced by apparent motion stimuli. This is encouraging for our plan to continue the study in the second stage, which consists of tuning vibrotactile actuators to reproduce a similar pattern of vibrational responses in the accelerometers. In this way, the actuators mimic human behavior.

This master thesis contains modeling and nonlinear automatic control of an autonomous lawn mower. The vehicle shall be able to follow a magnetic field loop buried in the lawn on an arbitrary distance, including straddling. A unicycle model of the lawn mower has been derived based on previous work. The model holds for the assumption of non slip environment. Two diff erent control strategies have been implemented and evaluated in this thesis. One is based on feedback linearization  and with the feedback gain estimated using an LQ-controller. The other strategy is based on linearization at an equilibrium point  and also with the use of an LQ-controller. The only state that can be measured is the distance to the magnetic loop. In order to estimate the other states, an observer has been implemented using the linearized in an equilibrium point  model and a Kalman filter. The navigation of the autonomous lawn mower is performed using a magnetic loop buried in the lawn. The non-linearity of the magnetic field causes diffi culties when estimating the distance to the loop. One magnetic field strength corresponds to two distances to the magnetic loop. The nature of the magnetic field and the measurement noise causes the precision of the controller to be limited. The conclusion of this master thesis is that it is possible to navigate and follow the magnetic loop at a distance greater than 0.3 [m], but with overshoots during sharp turns. If the reference distance is smaller than that, the behavior of the robot will be oscillating when following the loop. The limitations of the controller performance is due to the lack of information which can be extracted from the measurements of the magnetic field.

One of the main aspects of switched affine systems that makes their stabilizability study intricate is the existence of (generally) infinitely many attainable equilibrium points in the state space. Thus, prior to designing the switched control, the user must specify one of these equilibrium points to be the goal or reference. This can be a cumbersome task, especially if this goal is partially given or only defined as a set of constraints. To tackle this issue, in this paper we describe algorithms that can determine whether a given goal is an equilibrium point of the system and also jointly search for equilibrium points and design stabilizing switching functions. Copyright (C) 2021 The Authors.

To decrease the costs and the time it takes to develop and test new products, computer simulations are very helpful. Product models can be simulated, and their behavior can be examined. This applies not only to hardware, but even to software products that are composed of several components, so that their cooperative behaviour is simulated in a virtual environment. Some components of this environment can later be replaced by physical, real world devices. Some other components can be just prototypes, which are later replaced by more complex and realistic software components. In any case the idea is to construct a model and simulate both software and hardware before the actual production starts. In the WITAS project there is a need to develop a system which contains helicopters, robots and various control software and hardware. In particular there is a need to simulate the dynamic behavior of an autonomous aircraft within a virtual environment. There is a need to simulate a service environment, where robots can interact with the landed helicopter.In this report a study of object-oriented modeling of mechanical systems using Modelica is presented with applications to autonomous helicopters and robots. Mechanical features of an autonomous helicopter have been modeled in order to verify the control system. As the result the control system has been tested and tuned. However, the flight still is not stable in the cases when flight mission directions change too often.

A robot which is able to grab, move, and release objects using automatic or manual control have been modeled. The problem of inverse geometry (and inverse kinematics) can be solved for robots using our approach. The robot models can be controlled interactively.

The geometry and dynamic structure of these systems has been designed in CAD tools and later integrated with control systems for steering these devices. The simulation has been performed in Modelica.

Autonomous lawn mowers and floor cleaning robots are today easily accessible and areutilizing well-studied Coverage Path Planning algorithms. They operate in single-floorenvironments that are small with simple geometry compared to general urban environments such as city parking garages, highway bridges or city crossings. A next step for autonomous cleaning is road sweeping of these complex urban environments. In this work,a new Coverage Path Planning approach, Sampled BA* & Inward Spiral , handling this taskwas compared with existing well-performing algorithms BA* and Inward Spiral. The proposed approach combines the strengths of existing algorithms and demonstrates state-of-the-art performance on three large-scale 3D environments. It generated paths with lessrotation, while keeping the length of the path on the same level. For a given starting point,the new approach had consistently lower cost (length + rotation) for all environments. Forrandom starting points, randomness in the new approach caused less robustness, givingsignificantly higher cost. To improve the performance of the algorithms and remove biasfrom manual tuning, the parameters were automatically tuned using Bayesian Optimization. This makes the evaluation more robust and the results stronger.

We compared a visual GPS, a tactile torso belt, and a 3D audio display in a waypoint navigation task for dismounted soldiers. Using these displays one at a time, the soldier’s main tasks were to walk as fast and straight as possible towards the waypoints while visually scanning the terrain for detection of target flags. The results showed that all three types of displays gave similar navigation precision and target detection performance. The visual display entailed a somewhat higher navigation speed than the tactile and 3D audio displays. Both the visual and 3D audio displays, however, were rated as directing attention away from the terrain more than the tactile display. Compared to the tactile display, a higher mental workload was reported for the 3D audio, which was also rated least suitable for operational use in navigation tasks. Yet, the soldiers’ ability to localize directions to waypoints with the 3D audio display may point to its potential use for radio communication and cueing of directions to threats. In an overall evaluation, eight soldiers preferred the tactile display and four the visual, while none preferred the 3D audio. We discuss the results mainly regarding shortcomings of the study and the possibility of developing and combining the three display types for the dismounted soldier. 

The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges.

First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario.

Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results.

Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.