An MPC framework for efficient navigation of mobile robots in cluttered environments
This addresses efficient and safe navigation for mobile robots in dynamic settings, though it appears incremental as it builds on existing MPC methods.
The paper tackles the problem of mobile robot navigation in cluttered environments by proposing an MPC framework that integrates a shortest path planner, resulting in the robot reaching new targets within 2-3 seconds and responding to commands within 50-100 ms.
We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground robot, where a human operator dynamically assigns target locations that a robot should reach while avoiding obstacles. The robot reached new targets within 2-3 seconds and responded to new commands within 50 ms to 100 ms, immediately adjusting its motion even while still moving at high speeds toward a previous target.