In the dynamic and challenging maritime domain, Search and Rescue (SAR) operations are critical for ensuring the safety of life at sea. Adverse weather conditions often hinder traditional SAR efforts, leading to significant delays or cancellation of search missions. This paper introduces an autonomous search system utilizing Unmanned Aerial Vehicles. The system combines decision-making techniques for automatic mission generation and a flexible machine-learning framework that allows for easy training and deployment of models to automatically process data gathered during SAR operations. One of the system’s main features is the ease of use in mission planning, where high-level mission goals can be specified via a user interface in the form of data requests. The paper presents the results of the experimental evaluations of the system and showcases its deployment in actual field-test experimentation.

The rapid growth of the Unmanned Aerial Vehicle industry has increased the demand for robust control systems for commercial quadcopters, whose dynamic models are often unknown, to ensure reliable performance under adverse conditions. In this context, this paper proposes Deep Neural Network-Based LQR Adaptive Control (DNN-LQR-AC), an Adaptive Control (AC) strategy implemented using the Robot Operating System (ROS) software framework. DNN-LQR-AC relies on a simplified linearized dynamic model and combines an LQR controller with an adaptive term, which is updated in real-time using Deep Neural Networks (DNNs). The proposed solution also features cubic spline-based trajectory generation to provide continuous reference trajectories for the controller. Experimental validation using a Hardware-in-the-Loop approach on a DJI Matrice 100 quadcopter demonstrates that DNN-LQR-AC outperforms PID, LQR, and LQR-AC controllers, achieving superior position control in trajectory tracking under varied wind conditions, highlighting its applicability in real-world scenarios.

This paper addresses the problem of vision-based object geolocation using Unmanned Aerial Vehicles in Search and Rescue settings. It focuses on the task of automatically and accurately geolocating objects of different classes, focusing on human bodies, to provide a map of the detected objects as salient locations. Such maps can be used by responders to plan rescue operations or by other robotic platforms where geolocation is necessary, such as with delivery of medical supplies. The proposed solution strategy leverages recent developments in the field of Convolutional Neural Networks for vision-based object detection with a method for fusing detections. Occupancy probabilities of locations in the environment containing objects of specific classes, or lack thereof, are also computed. This is achieved by taking advantage of a novel sensor model for fusing vision-based detections using both positive and negative observations. The method is validated in simulation as well as with real field experiments.

The rapid expansion of Unmanned Aircraft (UA) usage has increased the need for reliable collision avoidance systems. This paper presents a trust-based, sensor-free collision avoidance system for UAs, leveraging the Drone Remote Identification Protocol (DRIP) to establish trust between aircraft. The proposed system uses a geometric-based cooperative avoidance method to optimize efficiency and a fail-safe repulsion avoidance mechanism for enhanced safety. The proposed system's effectiveness is evaluated through a series of simulations and real-world tests, focusing on metrics such as safety, flight distance, flight time, and acceleration requirements. The results are promising, indicating that the trust-based approach may successfully balance efficiency and safety, providing insights into potential use cases for DRIP.

The public safety and security application domain is an important research area that provides great benefits to society. Within this application domain, governmental and non‐governmental agencies, such as blue light organizations (e.g., police or firefighters), are often tasked with essential life‐saving activities when responding to fallouts of natural or man‐made disasters, such as earthquakes, floods, or hurricanes. 

Recent technological advances in artificial intelligence and robotics offer novel tools that first responder teams can use to shorten response times and improve the effectiveness of rescue efforts. Modern first responder teams are increasingly being supported by autonomous intelligent systems such as ground robots or Unmanned Aerial Vehicles (UAVs). However, even though many commercial systems are available and used in real deployments, many important research questions still need to be answered. These relate to both autonomous intelligent system design and development in addition to how such systems can be used in the context of public safety applications. 

This thesis presents a collection of functionalities for autonomous intelligent systems in public safety scenarios. Contributions in this thesis are divided into two parts. In Part 1, we focus on the design of navigation frameworks for UAVs for solving the problem of autonomous navigation in dynamic or changing environments. In particular, we present several novel ideas for integrating motion planning, control, and perception functionalities within robotic architectures to solve navigation tasks efficiently. 

In Part 2, we concentrate on an important service that autonomous intelligent systems can offer to first responder teams. Specifically, we focus on base functionalities required for UAV‐based rapid ad hoc communication infrastructure deployment in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication nodes that include routers and are used to establish ad hoc Wireless Mesh Networks (WMNs). We consider fundamental problems related to WMN network design, such as calculating node placements, and propose efficient novel algorithms to solve these problems. 

Considerable effort has been put into applying the developed techniques in real systems and scenarios. Thus, the approaches presented in this thesis have been validated through extensive simulations and real‐world experimentation with various UAV systems. Several contributions presented in the thesis are generic and can be adapted to other autonomous intelligent system types and application domains other than public safety and security. 

In the context of collaborative robotics, distributed situation awareness is essential for  supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

This paper describes an overview of a generic framework for collaboration among humans and multiple heterogeneous robotic systems based on the use of a formal characterization of delegation as a speech act. The system used contains a complex set of integrated software modules that include delegation managers for each platform, a task specification language for characterizing distributed tasks, a task planner, a multi-agent scan trajectory generation and region partitioning module, and a system infrastructure used to distributively instantiate any number of robotic systems and user interfaces in a collaborative team. The application focusses on 3D reconstruction in alpine environments intended to be used by alpine rescue teams. Two complex UAV systems used in the experiments are described. A fully autonomous collaborative mission executed in the Italian Alps using the framework is also described.

In this work we are investigating reactive avoidance techniques which can be used on board of a small quadcopter and which do not require absolute localisation. We propose a local map representation which can be updated with proprioceptive sensors. The local map is centred around the robot and uses spherical coordinates to represent a point cloud. The local map is updated using a depth sensor, the Inertial Measurement Unit and a registration algorithm. We propose an extension of the Dynamic Window Approach to compute a velocity vector based on the current local map. We propose to use an OctoMap structure to compute a 2-pass A* which provide a path which is converted to a velocity vector. Both approaches are reactive as they only make use of local information. The algorithms were evaluated in a simulator which offers a realistic environment, both in terms of control and sensors. The results obtained were also validated by running the algorithms on a real platform.