Adaptive Robot Navigation with Collision Avoidance subject to 2nd-order Uncertain Dynamics
This work addresses collision avoidance for robots with uncertain dynamics, but it appears incremental as it builds on existing methods like potential fields and adaptive control.
The paper tackles robot motion planning with uncertain second-order dynamics by combining potential-based feedback controllers with adaptive control to ensure collision-free navigation to a goal, with simulation results verifying the theoretical approach.
This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for extending the control scheme to decentralized multi-robot systems. Finally, extensive simulation results verify the theoretical findings.