Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach

Abstract

This paper presents a general method for planning collision-free wholebody walking motions for humanoid robots. First, we present a randomized algorithm for constrained motion planning, that is used to generate collision-free statically balanced paths solving manipulation tasks. Then, we show that dynamic walking makes humanoid robots small-space controllable. Such a property allows to easily transform collision-free statically balanced paths into collision-free dynamically balanced trajectories. It leads to a sound algorithm which has been applied and evaluated on several problems where whole-body planning and walk are needed, and the results have been validated on a real HRP-2 robot. IntroductionDuring the last twenty years, impressive progress has been achieved in humanoid robot hardware and control. This leads to a rising need for software and algorithms improving the usability and autonomy of those robots. One important area of research focuses on the development of robust and general motion generation techniques for safe and autonomous operation in human environments, such as offices or homes.Motion planning for humanoid robots is challenging for several reasons. First, the computational complexity of classic motion planning algorithms is exponential in the number of Degrees of Freedom (DoFs) of the considered system, which is high for humanoid kinematic trees. Second, a humanoid robot is an under-actuated system: the DoFs that control the position and orientation * The authors are with CNRS, LAAS,

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

“…Similar constraint sets have been employed to control whole-body humanoid motions previously [59,19]. Dalibard et al [19] separately solve the kinematic planning problem using full body model and a point mass dynamic model.…”

“…Similar constraint sets have been employed to control whole-body humanoid motions previously [59,19]. Dalibard et al [19] separately solve the kinematic planning problem using full body model and a point mass dynamic model. Our approach combines these two problems together with a more descriptive dynamic model, and is able to generate motions that cannot be handled by this kind of two-stage planner (such as the running and jumping examples in Section 6.4).…”

Optimization-Based Control Approaches to Humanoid Balancing

“…Indeed, from a safety point of view balanced states are preferable as they are controllable [7], i.e. the system is able to handle disturbances as a path exists allowing to recover the desired state.…”

Fall Prediction for New Sequences of Motions

“…[4] Geometric paths can be planned by approximating a dynamic trajectory [4]. However, the drawback of this method is that "some feasible dynamic motions are inherently impossible to compute with this approach."…”

“…However, the drawback of this method is that "some feasible dynamic motions are inherently impossible to compute with this approach." [4] Though there are planners that will compute dynamically stable motion trajectories offline [5], [6], these planners require an accurate robot model and change the desired motion in terms of the timings and the trajectory. Changing the timings of the desired motion causes the motion to be no longer synchronized to the task and changing the motion trajectory changes the meaning expressed.…”