Real-time Dynamic Bipedal Avoidance

In real-world settings, bipedal robots must avoid collisions with people and their environment. Further, a biped can choose between modes of avoidance: (1) adjust its pose while standing or (2) step to gain maneuverability. We present a real-time motion planner and multibody control framework for dynamic bipedal robots that avoids multiple moving obstacles and automatically switches between standing and stepping modes as necessary. By leveraging a reduced-order model (i. e. Linear Inverted Pendulum Model) and a half-space relaxation of the safe region, the planner is formulated as a convex optimization problem (i. e. Quadratic Programming) that can be used for real-time application with Model-Predictive-Control (MPC). To facilitate mode switching, we introduce center-of-pressure related slack-variables to the convex planning optimization that both shapes the planning cost function and provides a mode switching criterion for dynamic locomotion. Finally, we implement the proposed algorithm on a 3D Cassie bipedal robot and present hardware experiments showing real-time bipedal standing avoidance, stepping avoidance, and automatic switching of avoidance modes.

Authors

• Tianze Wang
• Jason White
• Christian Hubicki

Keywords

• Three-dimensional displays
• Shape
• Heuristic algorithms
• Dynamics
• Switches
• Real-time systems
• Hardware
• Cost Function
• Path Planning
• Convex Optimization Problem
• Quadratic Programming
• Slack Variables
• Mode Switching
• Reduced-order Model
• Safe Region
• Inverted Pendulum
• Hardware Experiments
• Inverted Pendulum Model
• Number Of Steps
• Simulation Experiments
• Control Input
• Center Of Pressure
• Current Position
• Step Length
• Target State
• Obstacle Avoidance
• High-level Planner
• Rapidly-exploring Random Tree
• Rigid Body Dynamics
• Multiple Obstacles
• Cost Avoidance
• Bipedal Locomotion
• Collision-free Path
• Task Space
• Model Predictive Control Framework
• Bipedal Walking