This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction model is designed by partitioning the environment into multiple belief regions and employed at the high-level navigation planner to estimate the dynamic obstacles' location. This additional location information of dynamic obstacles offered by belief abstraction enables less conservative long-horizon navigation actions beyond guaranteeing immediate collision avoidance. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate nonperiodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates the safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics.
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Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery
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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- Award ID(s):
- 1924978
- NSF-PAR ID:
- 10348405
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 1896 to 1902
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Bipedal locomotion over compliant terrain is an important and largely underexplored problem in the robotics community. Although robot walking has been achieved on some non-rigid surfaces with existing control methodologies, there is a need for a systematic framework applicable to different bipeds that enables stable locomotion over various compliant terrains. In this work, a novel energy-based framework is proposed that allows the dynamic locomotion of bipeds across a wide range of compliant surfaces. The proposed framework utilizes an extended version of the 3D dual spring-loaded inverted pendulum (Dual-SLIP) model that supports compliant terrains, while a bio-inspired controller is employed to regulate expected perturbations of extremely low ground-stiffness levels. An energy-based methodology is introduced for tuning the bio-inspired controller to enable dynamic walking with robustness to a wide range of low ground-stiffness one-step perturbations. The proposed system and controller are shown to mimic the vertical ground reaction force (GRF) responses observed in human walking over compliant terrains. Moreover, they succeed in handling repeated unilateral stiffness perturbations under specific conditions. This work can advance the field of biped locomotion by providing a biomimetic method for generating stable human-like walking trajectories for bipedal robots over various compliant surfaces. Furthermore, the concepts of the proposed framework could be incorporated into the design of controllers for lower-limb prostheses with adjustable stiffness to improve their robustness over compliant surfaces.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA46639.2022.9812068
