In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to external perturbations. Rejecting perturbations is an essential capability of bipedal robots and has been widely studied in the locomotion literature. However, adversarial disturbances and aggressive turning can lead to negative lateral step width (i.e., crossed-leg scenarios) with unstable motions and self-collision risks. These motion planning problems are computationally difficult and have not been explored under a hierarchically integrated task and motion planning method. We explore a planning and decision-making framework that closely ties linear-temporal-logic-based reactive synthesis with trajectory optimization incorporating the robot’s full-body dynamics, kinematics, and leg collision avoidance constraints. Between the high-level discrete symbolic decision-making and the low-level continuous motion planning, behavior trees serve as a reactive interface to handle perturbations occurring at any time of the locomotion process. Our experimental results show the efficacy of our method in generating resilient recovery behaviors in response to diverse perturbations from any direction with bounded magnitudes.
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Temporal Logic Guided Locomotion Planning and Control in Cluttered Environments
We present planning and control techniques for non-periodic locomotion tasks specified by temporal logic in rough cluttered terrains. Our planning approach is based on a discrete set of motion primitives for the center of mass (CoM) of a general bipedal robot model. A deterministic shortest path problem is solved over the Bu ̈chi automaton of the temporal logic task specification, composed with the graph of CoM keyframe states generated by the motion primitives. A low-level controller based on quadratic programming is proposed to track the resulting CoM and foot trajectories. We demonstrate dynamically stable, non-periodic locomotion of a kneed compass gait bipedal robot satisfying complex task specifications.
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- Award ID(s):
- 1924978
- NSF-PAR ID:
- 10184228
- Date Published:
- Journal Name:
- 2020 American Control Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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