APF-PF: Probabilistic Depth Perception for 3D Reactive Obstacle Avoidance
This addresses obstacle avoidance for agile robots like UAVs in noisy environments, but it is incremental as it combines existing methods (APF and PF) for a specific application.
The paper tackles 3D obstacle avoidance under partial observability by integrating an Artificial Potential Function controller with a Particle Filter for depth perception, achieving robust and reliable performance validated on a quadrotor UAV with onboard real-time computation.
This paper proposes a framework for 3D obstacle avoidance in the presence of partial observability of environment obstacles. The method focuses on the utility of the Artificial Potential Function (APF) controller in a practical setting where noisy and incomplete information about the proximity is inevitable. We propose a Particle Filter (PF) approach to estimate potential obstacle locations in an input depth image stream. The probable candidates are then used to generate an action that maneuvers the robot towards the negative gradient of potential at each time instant. Rigorous experimental validation on a quadrotor UAV highlights the robustness and reliability of the method when robot's sensitivity to incorrect perception information can be concerning. The proposed perception and control stack is run onboard the UAV, demonstrating the computational feasibility for real-time applications and agile robots.