This dissertation proposes autonomous robotic strategies for urban search and rescue (USAR) which are map-based semi-autonomous robot navigation and fully-autonomous robotic search, tracking, localization and mapping (STLAM) using a team of robots. Since the prerequisite for these solutions is accurate robot localization in the environment, this dissertation first presents a novel grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan and estimation of the robot pose, the proposed technique corrects the estimation error by matching the new scan to the globally defined grid map. To improve the accuracy of the correction, each grid cell of the map is represented by multiple normal distributions (NDs). The new scan to be matched to the map is also represented by NDs, which achieves the scan-to-map matching by the ND-to-ND matching. In the map-based semi-autonomous robot navigation strategy, a robot placed in an environment creates the map of the environment and sends it to the human operator at a distant location. The human operator then makes decisions based on the map and controls the robot via tele-operation. In case of communication loss, the robot semi-autonomously returns to the home position by inversely tracking its trajectory with additional optimal path planning. In the fully-autonomous robotic solution to USAR, multiple robots communicate one another while operating together as a team. The base station collects information from each robot and assigns tasks to the robots. Unlike the semi-autonomous strategy there is no control from the human operator. To further enhance the efficiency of their cooperation each member of the team specifically works on its own task. A series of numerical and experimental studies were conducted to demonstrate the applicability of the proposed solutions to USAR scenarios. The effectiveness of the scan-to-map matching with the multi-ND representation was confirmed by analyzing the error accumulation and by comparing with the single-ND representation. The applicability of the scan-to-map matching to the real SLAM problem was also verified in three different real environments. The results of the map-based semi-autonomous robot navigation showed the effectiveness of the approach as an immediately usable solution to USAR. The effectiveness of the proposed fully- autonomous solution was first confirmed by two real robots in a real environment. The cooperative performance of the strategy was further investigated using the developed platform- and hardware-in-the-loop simulator. The results showed significant potential as the future solution to USAR. / Ph. D.

This thesis details out a system developed with objective of conducting cooperative search operation in a partially-known environment, with a human operator, and two Unmanned Aerial Vehicles (UAVs) with nadir, and front on-board cameras. The system uses two phases of flight operations, where the first phase is aimed at gathering latest overhead images of the environment using a UAV’s nadir camera. These images are used to generate and update representations of the environment including 3D reconstruction, mosaic image, occupancy image, and a network graph. During the second phase of flight operations, a human operator marks multiple areas of interest for closer inspection on the mosaic generated in previous step, displayed via a UI. These areas are used by the path planner as visitation goals. The two-step path planner, which uses network graph, utilizes the weighted-A* planning, and Travelling Salesman Problem’s solution to compute an optimal visitation plan. This visitation plan is then converted into Mission waypoints for a second UAV, and are communicated through a navigation module over a MavLink connection. A UAV flying at low altitude, executes the mission plan, and streams a live video from its front-facing camera to a ground station over a wireless network. The human operator views the video on the ground station, and uses it to locate the target object, culminating the mission. / Master of Science / This thesis details out the work focused on developing a system capable of conducting search operation in an environment where prior information has been rendered outdated, while allowing human operator, and multiple robots to cooperate for the search. The system operation is divided into two phases of flight operations, where the first operation focuses on gathering the current information using a camera equipped unmanned aircraft, while the second phase involves utilizing the human operator’s instinct to select areas of interest for a close inspection. It is followed by a flight operation using a second unmanned aircraft aimed at visiting the selected areas and gathering detailed information. The system utilizes the data acquired through first phase, and generates a detailed map of the target environment. In the second phase of flight operations, a human uses the detailed map, and marks the areas of interest by drawing over the map. This allows the human operator to guide the search operation. The path planner generates an optimal plan of visitation which is executed by the second unmanned aircraft. The aircraft streams a live video to a ground station over a wireless network, which is used by the human operator for detecting the target object’s location, concluding the search operation.

Many important applications in the real world that can be modelled as combinatorial optimization problems are actually dynamic in nature. However, research on dynamic optimization focuses on continuous optimization problems, and rarely targets combinatorial problems. Moreover, dynamic combinatorial problems, when addressed, are typically tackled within an application context. <br /><br /> In this thesis, dynamic combinatorial problems are addressed collectively by adopting an evolutionary based algorithmic approach. On the plus side, their ability to manipulate several solutions at a time, their robustness and their potential for adaptability make evolutionary algorithms a good choice for solving dynamic problems. However, their tendency to converge prematurely, the difficulty in fine-tuning their search and their lack of diversity in tracking optima that shift in dynamic environments are drawbacks in this regard. <br /><br /> Developing general methodologies to tackle these conflicting issues constitutes the main theme of this thesis. First, definitions and measures of algorithm performance are reviewed. Second, methods of benchmark generation are developed under a generalized framework. Finally, methods to improve the ability of evolutionary algorithms to efficiently track optima shifting due to environmental changes are investigated. These methods include adapting genetic parameters to population diversity and environmental changes, the use of multi-populations as an additional means to control diversity, and the incorporation of local search heuristics to fine-tune the search process efficiently. <br /><br /> The methodologies developed for algorithm enhancement and benchmark generation are used to build and test evolutionary models for dynamic versions of the travelling salesman problem and the flexible manufacturing system. Results of experimentation demonstrate that the methods are effective on both problems and hence have a great potential for other dynamic combinatorial problems as well.

Many important applications in the real world that can be modelled as combinatorial optimization problems are actually dynamic in nature. However, research on dynamic optimization focuses on continuous optimization problems, and rarely targets combinatorial problems. Moreover, dynamic combinatorial problems, when addressed, are typically tackled within an application context. <br /><br /> In this thesis, dynamic combinatorial problems are addressed collectively by adopting an evolutionary based algorithmic approach. On the plus side, their ability to manipulate several solutions at a time, their robustness and their potential for adaptability make evolutionary algorithms a good choice for solving dynamic problems. However, their tendency to converge prematurely, the difficulty in fine-tuning their search and their lack of diversity in tracking optima that shift in dynamic environments are drawbacks in this regard. <br /><br /> Developing general methodologies to tackle these conflicting issues constitutes the main theme of this thesis. First, definitions and measures of algorithm performance are reviewed. Second, methods of benchmark generation are developed under a generalized framework. Finally, methods to improve the ability of evolutionary algorithms to efficiently track optima shifting due to environmental changes are investigated. These methods include adapting genetic parameters to population diversity and environmental changes, the use of multi-populations as an additional means to control diversity, and the incorporation of local search heuristics to fine-tune the search process efficiently. <br /><br /> The methodologies developed for algorithm enhancement and benchmark generation are used to build and test evolutionary models for dynamic versions of the travelling salesman problem and the flexible manufacturing system. Results of experimentation demonstrate that the methods are effective on both problems and hence have a great potential for other dynamic combinatorial problems as well.

Nous élaborons une approche multi-agents pour la résolution des problèmes d’optimisation combinatoire nommée MAOM-COP. Elle combine des métaheuristiques, les systèmes multi-agents et l’apprentissage par renforcement. Les heuristiques manquent d’une vue d’ensemble sur l’évolution de la recherche. Notre objectif consiste à utiliser les systèmes multi-agents pour créer des méthodes de recherche coopératives. Ces méthodes explorent plusieurs métaheuristiques. MAOM-COP est composée de plusieurs agents qui sont l’agent décideur, les agents intensificateurs et les agents diversificateurs (agents croisement et agent perturbation). A l’aide de l’apprentissage, l’agent décideur décide dynamiquement quel agent à activer entre les agents intensificateurs et les agents croisement. Si les agents intensificateurs sont activés, ils appliquent des algorithmes de recherche locale. Durant leurs recherches, ils peuvent s’échanger des informations, comme ils peuvent déclencher l’agent perturbation. Si les agents croisement sont activés, ils exécutent des opérateurs de recombinaison. Nous avons appliqué MAOM-COP sur les problèmes suivants : l’affectation quadratique, la coloration des graphes, la détermination des gagnants et le sac à dos multidimensionnel. MAOM-COP possède des performances compétitives par rapport aux algorithmes de l’état de l’art. / We elaborate a multi-agent based optimization method for combinatorial optimization problems named MAOM-COP. It combines metaheuristics, multiagent systems and reinforcement learning. Although the existing heuristics contain several techniques to escape local optimum, they do not have an entire vision of the evolution of optimization search. Our main objective consists in using the multi-agent system to create intelligent cooperative methods of search. These methods explore several existing metaheuristics. MAOMCOP is composed of the following agents: the decisionmaker agent, the intensification agents and the diversification agents which are composed of the perturbation agent and the crossover agents. Based on learning techniques, the decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents. If the intensifications agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied MAOMCOP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOM-COP shows competitive performances compared with the approaches of the literature

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