Reactive Planning and Control of Planar Spring-Mass Running on Rough Terrain

• Published in: IEEE transactions on robotics Vol. 28; no. 3; pp. 567 - 579
• Main Authors: Arslan, Ömür, Saranli, U.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptative systems; Algorithms; Analytical models; Applied sciences; Computational modeling; Computer science; control theory; systems; Control system analysis; Control theory. Systems; Exact sciences and technology; Footstep planning; Kinematics; Legged locomotion; Measurement; Morphology; Planning; reactive control; Robotics; Robots; robust control; sequential composition; Simulation; spring-mass running; Trajectory; Crosscountry vehicle; Moving robot; dynamic control; Low speed; Control program; reactive control; Modeling; Dead beat control; Running locomotion; Dynamic method; Robotics; Uncertain system; Kinematics; Robustness; Dynamic model; Footstep planning; sequential composition; Spring mass system; Robust control; Locomotion; Walking; Multiagent system; Domain of attraction; Planning; Signal to noise ratio; spring―mass running
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2011.2178134

An important motivation for work on legged robots has always been their potential for high-performance locomotion on rough terrain. Nevertheless, most existing control algorithms for such robots either make rigid assumptions about their environments or rely on kinematic planning at low speeds. Moreover, the traditional separation of planning from control often has negative impact on the robustness of the system. In this paper, we introduce a new method for dynamic, fully reactive footstep planning for a planar spring-mass hopper, based on a careful characterization of the model dynamics and the design of an associated deadbeat controller, used within a sequential composition framework. This yields a purely reactive controller with a large domain of attraction that requires no explicit replanning during execution. We show in simulation that plans constructed for a simplified dynamic model can successfully control locomotion of a more complete model across rough terrain. We also characterize the performance of the planner over rough terrain and show that it is robust against both model uncertainty and measurement noise without replanning.