Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances

The motion planning experiments are conducted in PyBullet, where a dual-arm robot pushes a cart to follow a reference trajectory defined by waypoints. To isolate the performance of the motion planning algorithms from contact dynamics, the cart's URDF is omitted. We formulate the local planning problem as an optimization problem—which is the most common and reasonable approach in the field of mobile manipulation—and compare our custom local planner model (TT model, denoted as *) and improved model (LF model, denoted as **) against typical local planner models from other works. Due to limited maneuverability, other models show larger tracking errors and even divergence, while ours remain flexible and robust.

The control experiments evaluate the controller’s ability to accurately execute the motion planner’s commands under varying payloads and external disturbances. Due to the cart’s large inertia and significant uncertainty in its dynamics (large variance in the dynamic parameters), conventional controllers struggle to eliminate steady-state error or introduce severe delays, leading to overshoot or oscillation. We design a disturbance rejection controller in the local coordinate frame, which achieves superior control performance compared to both PD controllers and typical adaptive controllers. 

The commands (desired orientation) are set to fluctuate within ±0.2 rads, while a 22.2 kg payload is applied as a disturbance to evaluate the controllers' adaptability.

To evaluate the proposed local planning and control framework in practical scenarios, we design a set of challenging navigation tasks—pushing the cart through narrow spaces, which is common in supermarkets or dense crowds. We generate global paths, represented as sparse waypoints, using Hybrid A* to guide the cart through these constrained environments, and track the waypoints using 3 baseline frameworks. The sharp reference trajectories resulting from frequent posture adjustments in tight spaces. The accumulation of planning and control errors leads to task failure for baseline methods, whereas our framework completes the tasks successfully, demonstrating superior maneuverability and robust control performance.