Technical Report: Sensor-Based Reactive Symbolic Planning in Partially Known Environments
This addresses robotic assembly tasks in uncertain settings, but it is incremental as it builds on existing planning and reactive control techniques.
The paper tackles the problem of assembling passive objects in cluttered, partially known environments using a robot with local sensing, by combining deliberative symbolic planning with reactive control to ensure convergence and obstacle avoidance. The method is validated through formal proofs and numerical simulations.
This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations.