Real-time 3D collision avoidance for biped robots

Abstract

Abstract-The ability to avoid collisions is crucial for locomotion in cluttered environments. It is not enough to plan collision-free movements in advance when the environment is dynamic and not precisely known. We developed a new method which generates locally optimized trajectories online during the feedback control in order to dynamically avoid obstacles. This method successfully combines a local potential field method with a heuristic based on height and width of an obstacle to avoid collisions. The program's main feature is the integration of obstacles into the framework designed for self-collision avoidance presented in [1] and the collisions avoidance in taskspace. We show experimental results validating the method.

Real-time pattern generation among obstacles for biped robots

“…We have previously developed a library for efficiently representing a robot's geometry for self collision avoidance [28], which we have extended to include obstacles to enable general collision avoidance [4]. Since the distance calculations are a crucial part of the framework presented here, we give a brief overview in this section.…”

“…Step-planner and heuristic trajectory adaption method (SPTA) replaces the step-sequence-planner [5] and its modifications [4]. It is an A*-based footstep planner, which calculates feasible and optimal stance foot positions.…”

“…(2) Enabling the robot to react to changes in the sensed environment or to user inputs in real-time without requiring slow movements to account for long processing times. Based on our work presented in [4] we developed a framework which closes the gap between obstacle recognition, collision-free walking in 2D and whole-body 3D collision avoidance. The system combines several components running at different sampling rates and using different levels of detail in the robot and environment models to balance real-time requirements with the goal to exploit the robot's physical capabilities.…”

“…(2) An A*-based step-sequence planner which replaces the step-sequence-planner [5] and its modifications [4]. It determines footsteps using environment and lower leg representation via SSV objects.…”

“…(3) A reactive collision avoidance algorithm which locally optimizes the swing foot trajectory calculated by the walking pattern generator during the execution of the movement. It exploits all degrees of freedoms (DoFs) of the swing foot while taking into account joint constraints and potential selfcollisions [4]. Overall, our framework provides an efficient way to take into account obstacles in the whole-body control system.…”