N-dimensional Convex Obstacle Avoidance using Hybrid Feedback Control (Extended version)
For roboticists, it provides a theoretically guaranteed solution to obstacle avoidance in high-dimensional spaces, though simulations only show 2D/3D results.
This paper presents a hybrid feedback control scheme for autonomous robot navigation in unknown n-dimensional environments with convex obstacles, guaranteeing safe global asymptotic convergence to a target. Simulations in 2D and 3D demonstrate effectiveness.
This paper addresses the autonomous robot navigation problem in a priori unknown n-dimensional environments containing disjoint convex obstacles of arbitrary shapes and sizes, with pairwise distances strictly greater than the robot's diameter. We propose a hybrid feedback control scheme that guarantees safe and global asymptotic convergence of the robot to a predefined target location. The proposed control strategy relies on a switching mechanism allowing the robot to operate either in the move-to-target mode or the obstacle-avoidance mode, based on its proximity to the obstacles and the availability of a clear straight path between the robot and the target. In the obstacle-avoidance mode, the robot is constrained to move within a two-dimensional plane that intersects the obstacle being avoided and the target, preventing it from retracing its path. The effectiveness of the proposed hybrid feedback controller is demonstrated through simulations in two-dimensional and three-dimensional environments.