Showing papers by "Jean-Paul Laumond published in 2011"

Planning foot placements for a humanoid robot: A problem of inverse kinematics

Abstract: We present a novel approach to plan foot placements for a humanoid robot according to kinematic tasks. In this approach, the foot placements are determined by the continuous deformation of a robot motion including a locomotion phase according to the desired tasks. We propose to represent the motion by a virtual kinematic tree composed of a kinematic model of the robot and articulated foot placements. This representation allows us to formulate the motion deformation problem as a classical inverse kinematics problem on a kinematic tree. We first provide details of the basic scheme where the number of footsteps is given in advance and illustrate it with scenarios on the robot HRP-2. Then we propose a general criterion and an algorithm to adapt the number of footsteps progressively to the kinematic goal. The limits and possible extensions of this approach are discussed last.

Linear dimensionality reduction in random motion planning

Abstract: In this paper we present a method to control random sampling in motion planning algorithms. The principle of the method is to use online the results of a probabilistic planner to describe the free space in which the planning takes place, by computing a principal component analysis (PCA). This method identifies the locally free directions of the free space. Given that description, our algorithm accelerates the progression along these favored directions. In this way, if the free space appears as a small volume around a sub-manifold of a high-dimensional configuration space, the method overcomes the usual limitations of probabilistic motion planning algorithms and finds a solution quickly. The presented method is theoretically analyzed and experimentally compared with known motion planners.

Tracking an omnidirectional evader with a differential drive robot

Abstract: In this paper we consider the surveillance problem of tracking a moving evader by a nonholonomic mobile pursuer. We deal specifically with the situation in which the only constraint on the evader's velocity is a bound on speed (i.e., the evader is able to move omnidirectionally), and the pursuer is a nonholonomic, differential drive system having bounded speed. We formulate our problem as a game. Given the evader's maximum speed, we determine a lower bound for the required pursuer speed to track the evader. This bound allows us to determine at the beginning of the game whether or not the pursuer can follow the evader based on the initial system configuration. We then develop the system model, and obtain optimal motion strategies for both players, which allow us to establish the long term solution for the game. We present an implementation of the system model, and motion strategies, and also present simulation results of the pursuit-evasion game.

Small-space controllability of a walking humanoid robot

Abstract: This paper presents a two-stage motion planner for walking humanoid robots. A first draft path is computed using random motion planning techniques that ensure collision avoidance. In a second step, the draft path is approximated by a whole-body dynamically stable walk trajectory. The contributions of this work are: (i) a formal guarantee, based on small-space controllability criteria, that the first draft path can be approximated by a collision-free dynamically stable trajectory; (ii) an algorithm that uses this theoretical property to find a solution trajectory. We have applied our method on several problems where whole-body planning and walk are needed, and the results have been validated on a real platform: the robot HRP-2.

A framework for manipulation and locomotion with realtime footstep replanning

Abstract: This paper focuses on realization of tasks with locomotion on humanoid robots. Locomotion and whole body movement are resolved as one unique problem. The same planner and controller are used for both stages of the movement. Final posture and footprint placements are found by resolving an optimization problem on the robot augmented by its footprints. Footstep replanning is done in realtime to correct perception and execution errors. The framework is demonstrated with the HRP-2 robot in a number of different scenarios.

An Inverse Optimal Control Approach to Human Motion Modeling

Abstract: In this paper, we present inverse optimal control as a promising approach to transfer biological motions to humanoid robots. Inverse optimal control serves to identify the underlying optimality criteria of human motions from measurements. Based on these results optimal control models are established that can be used to control robot motion. Inverse optimal control problems are hard to solve since they require the simultaneous treatment of a parameter identification problem and an optimal control problem. We propose a bilevel approach to solve inverse optimal control problems which efficiently combines a direct multiple shooting technique for the optimal control problem solution with a derivative free trust region optimization technique to guarantee the match between optimal control problem solution and measurements. We apply inverse optimal control to determine optimality principles of human locomotion path generation to given target positions and orientations, using new motion capture data of human subjects. We show how the established optimal control model can be used to enable the humanoid robot HRP-2 to autonomously generate natural locomotion paths.