|Ali Noormohammadi Asl|
Mobile Robot Motion Planning for Search and Exploration Under Uncertainty
The main contribution of this thesis is motion planning for specific goals such as environment exploration, search and coverage. However, the presence of uncertainties makes them challenging tasks. In order to achieve a reliable plan and decision, these uncertainties should be considered in the robot’s planning and decision making. Therefore, the path planning for the exploration and search is modeled as an asymmetric Traveling Salesman Problem (aTSP) in the belief space in which the robot should search a series of goal points. Toward reducing the complexity of the aforementioned problem, the Feedback-based Information Roadmap (FIRM) is exploited. Using FIRM, the intractable traveling salesman optimization problem in the continuous belief space is changed to a simpler optimization problem on the TSP-FIRM graph. The optimal policy of the robot is obtained by finding the optimal path between each two goal points and solving the aTSP and then the policy is executed online. Also, some algorithms are proposed to overcome the deviation from the path, kidnapping, finding new obstacles and becoming highly uncertain about the position which are possible situations in the online execution of the policy. Consequently, the robot should update its graph, map and policy online. The generic proposed algorithms are extended to the nonholonomic robots. In the online and offline phase, switching and LQG controllers as well as a Kalman filter for localization, are adopted. In order to show the applicability, performance and effectiveness of the proposed algorithms, a simulation and a real implementation are done in webots software using the e-Puck robot and in a floor of Electrical Engineering faculty on Melon robot, respectively.