Scaling sampling-based motion planning to humanoid robots

Yiming Yang, Vladimir Ivan, Wolfgang Merkt, Sethu Vijayakumar
2016 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO)  
Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling-based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no
more » ... rior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots. We also present a benchmark between different motion planning algorithms evaluated on a variety of reaching motion problems. This allows us to find suitable algorithms for solving humanoid motion planning problems, and to identify the limitations of these algorithms. All authors are with School
doi:10.1109/robio.2016.7866531 dblp:conf/robio/YangIMV16 fatcat:yftbbngmcjbz3dfhg32ci5kus4