MH
M.K. Heliński
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Local Path Planning and Obstacle Avoidance for an Omnicopter
A 6D-DWA Algorithm for Real-time Omnidirectional Navigation
As omnidirectional UAVs become increasingly prevalent, computationally efficient path planning algorithms are required to facilitate their autonomous traversal of dynamic environments. This thesis presents a real-time local path planning algorithm for an omnicopter, adapted from the Dynamic Window Approach into a 6D-DWA implementation. To ensure computational efficiency, the local path planner incorporates environment voxelisation, a 10-sphere omnicopter structure approximation, and adaptive velocity sampling. The avoidance of dynamic obstacles, detected via depth camera, is facilitated through context-aware 6D-DWA weight adjustments and a separate integrated fast-evasion strategy. Simulation testing in Gazebo demonstrates that the 6D-DWA is a feasible local planning solution, consistently performing below the 0.2 s real-time threshold. Future research into algorithm hybridisation may further mitigate the system's limited foresight and enhance the replanning capabilities.
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As omnidirectional UAVs become increasingly prevalent, computationally efficient path planning algorithms are required to facilitate their autonomous traversal of dynamic environments. This thesis presents a real-time local path planning algorithm for an omnicopter, adapted from the Dynamic Window Approach into a 6D-DWA implementation. To ensure computational efficiency, the local path planner incorporates environment voxelisation, a 10-sphere omnicopter structure approximation, and adaptive velocity sampling. The avoidance of dynamic obstacles, detected via depth camera, is facilitated through context-aware 6D-DWA weight adjustments and a separate integrated fast-evasion strategy. Simulation testing in Gazebo demonstrates that the 6D-DWA is a feasible local planning solution, consistently performing below the 0.2 s real-time threshold. Future research into algorithm hybridisation may further mitigate the system's limited foresight and enhance the replanning capabilities.