Unforeseen events are frequent in the real-world environments where robots are expected to assist, raising the need for fast replanning of the on-going policy to guarantee operational safety. Inspired by human behavioral studies of obstacle avoidance and route selection, this letter presents a hierarchical framework that generates reactive yet bounded obstacle avoidance behaviors through a multi-layered analysis. The framework leverages the strengths of learning techniques and the versatility of dynamic movement primitives to efficiently unify perception, decision, and action levels via environmental low-dimensional geometric descriptors. Experimental evaluation on synthetic environments and a real anthropomorphic manipulator proves the robustness and generalization capabilities of the proposed approach regardless of the obstacle avoidance scenario.
|Number of pages||8|
|Journal||IEEE Robotics and Automation Letters|
|Early online date||23 Jul 2019|
|Publication status||Published - Oct 2019|
- Collision avoidance
- reactive and sensor-based planning
- autonomous agents