Print Email Facebook Twitter Risk-Aware Decentralized Multi-MAV Planning in Unknown and Dynamic Environments Title Risk-Aware Decentralized Multi-MAV Planning in Unknown and Dynamic Environments Author Wu, Siyuan (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Alonso Mora, J. (mentor) Chen, G. (mentor) Pek, Christian (graduation committee) Jafarian, M. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Vehicle Engineering | Cognitive Robotics Date 2023-08-28 Abstract Recent progress in multiple micro aerial vehicle (MAV) systems has demonstrated autonomous navigation in static environments. Yet, there are limited works regarding the autonomous navigation of multiple MAVs in dynamic and unknown environments. The challenge arises from the complexity of the motion planning problem, which requires the MAVs to coordinate with each other while avoiding dynamic obstacles. This thesis presents a novel risk-aware decentralized multi-agent motion planning framework to address this issue. For perception, we rely on a particle-based dynamic map, which utilizes particles to represent dynamic obstacles and predict future states of dynamic obstacles. Leveraging this map representation and the shared trajectory from other agents, we evaluate the future collision risk with dynamic obstacles and other agents in a coupled manner. During the planning phase, a risk-aware kino-dynamic A* algorithm tailored to the particle-based map representation is developed, ensuring dynamically feasible paths with risks under a given safe level. Subsequently, spatio-temporal safety corridors with maximum volume are optimized by inflating from path segments, taking map particles as constraints. These corridors act as constraints for the trajectory optimization problem, which is simplified to a convex optimization problem by using Bézier splines. The proposed method is thoroughly evaluated in simulation environments featuring various quantities and shapes of dynamic obstacles. Comparative results with state-of-the-art multi-agent planners that rely on precise obstacle observations demonstrate the efficiency and safety of the proposed method. Furthermore, the effectiveness of the proposed method is validated in a more realistic simulation environment with pedestrians, using depth cameras onboard for perception. Subject Motion PlanningDynamic EnvironmentMulti-Robot SystemMicro Aerial VehicleCollision AvoidanceAutonomous Navigation To reference this document use: http://resolver.tudelft.nl/uuid:02899807-8d2d-4643-92f5-c6b6773baa2d Part of collection Student theses Document type master thesis Rights © 2023 Siyuan Wu Files PDF Siyuan_MSc_Thesis_Report.pdf 19.01 MB Close viewer /islandora/object/uuid:02899807-8d2d-4643-92f5-c6b6773baa2d/datastream/OBJ/view