Autonomous navigation with convergence guarantees in complex dynamic environments

This article addresses the obstacle avoidance problem for setpoint stabilization tasks in complex dynamic 2-D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed that integrates the favorable convergence characteristics of closed-form motion planning techniques with the intuitive representation of system constraints through Model Predictive Control (MPC). The method is analytically proven to accomplish collision avoidance and convergence under soft conditions. Simulation scenarios using a non-holonomic unicycle robot is provided to showcase the efficacy of the control scheme.