Michel Taïx

A motion planner for nonholonomic mobile robots

This paper considers the problem of motion planning for a car-like robot (i.e., a mobile robot with a nonholonomic constraint whose turning radius is lower-bounded). We present a fast and exact planner for our mobile robot model, based upon recursive subdivision of a collision-free path generated by a lower-level geometric planner that ignores the motion constraints. The resultant trajectory is optimized to give a path that is of near-minimal length in its homotopy class. Our claims of high speed are supported by experimental results for implementations that assume a robot moving amid polygonal obstacles. The completeness and the complexity of the algorithm are proven using an appropriate metric in the configuration space R/sup 2//spl times/S/sup 1/ of the robot. This metric is defined by using the length of the shortest paths in the absence of obstacles as the distance between two configurations. We prove that the new induced topology and the classical one are the same. Although we concentrate upon the car-like robot, the generalization of these techniques leads to new theoretical issues involving sub-Riemannian geometry and to practical results for nonholonomic motion planning. >

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

This paper presents a general method for planning collision-free whole-body 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.

Efficient motion planners for nonholonomic mobile robots

Deals with the problem of motion planning for a car-like robot (i.e. a nonholonomic mobile robot whose turning radius is lower bounded). The authors present a fast and exact planner based upon recursive subdivisions of a collision-free path generated by a lower-level geometric planner which ignores the motion constraints. The resultant trajectory is optimized to give a path which is of near-minimal length in its homotopy class. The claims of high speed are supported by experimental results for several implementations which assume different geometric models of the robot.<<ETX>>

A motion planner for car-like robots based on a mixed global/local approach

Deals with the problem of motion planning for a car-like robot (i.e. nonholonomic mobile robot whose turning radius is lower bounded). The main contribution is the introduction of a new metric in the configuration space R/sup 2/*S/sup 1/ of such a system. This metric is defined from the length of the shortest paths in the absence of obstacles. The authors study the relations between the new induced topology and the classical one. This study leads to new theoretical issues about sub-Riemannian geometry and to practical results for motion planning. In particular they prove an inclusion relation of neighbourhoods in both topologies, which is the basis of an efficient obstacle avoidance local method.<<ETX>>

Optimal motion planning for humanoid robots

This paper aims at combining state of the art developments of path planning and optimal control and to create the algorithmic foundations to tackle optimal control problems in cluttered environments. Our contribution is three-fold: first, we describe a simple method to automatically generate minimum bounding capsules around exact robot body geometries represented by meshes. Second, we use the bounding capsules to implement distance constraints for an optimal control problem solver and achieve (self-)collision avoidance. Finally, we propose a complete two-stage framework for optimal motion planning on complex robots. This framework is successfully applied to generate optimal collision-free trajectories both in simulation and on the humanoid robot HRP-2.

Robust motion planning for rough terrain navigation

Deals with motion planning for a mobile robot on rough terrain. Hait and Simeon (1996) proposed a geometrical planner for articulated robots which can take into account uncertainty of the terrain and of the position of the robot. This paper aims at improving the robustness of the trajectory using a landmark based approach. We consider regions of the terrain where natural landmarks are visible. We propose a two-step planning approach taking advantage of these regions to reduce position uncertainty. First a path is determined between the initial and goal configurations, based on simplified models of the constraints. Then a trajectory is planned along this path, verifying the validity and visibility constraints.

Human interaction with Motion Planning Algorithm

This paper presents an interactive motion planning system to compute free collision motion in a numerical model. The system is based on interaction between a user and a motion planning algorithm. On one hand the user moves the object with an interactive device and on the other hand a motion planning algorithm searches a solution in the configuration space. The interaction aims at improving the guidance of an operator during a robot motion task in a virtual environment with the help of an automatic path planning algorithm. Existing works use a two-step decomposition which limits the interaction between the user and the ongoing process. We propose a modification of a classic motion planning method, the Rapidly-exploring Random Tree to build an Interactive-RRT. This method is based on exchanging pseudo-forces between the algorithm and the user, and on data gathering (labels) from the virtual scene. Examples are shown to illustrate the Interactive motion planning system with different interactive devices (space mouse and haptic arm). We analyze the influence of the user’s dexterity to find a solution depending on various parameters of the algorithm and we show how we can adapt these parameters to a user.

Haptic Assembly and Disassembly Task Assistance using Interactive Path Planning

This paper describes a global interactive scheme including fast motion planning and real time guiding force for 3D CAD part assembly or disassembly tasks. For real time purpose, the motion planner is divided into different steps. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using those computed data, a second part tries to find a collision free path in real time. Once the path is found, an haptic artificial force is applied constraining the user on the path. The user can then influence the planner by not following the path and automatically order a new path research. The performance of this haptic assistance is measured on a test simulation based on an ALSTOM power components assembly simulation.

Generating human-like reaching movements with a humanoid robot: A computational approach☆

This paper presents a computational approach for transferring principles of human motor control to humanoid robots. A neurobiological model, stating that the energy of motoneurons is minimized and that dynamic and static efforts are processed separately, is considered. This paradigm is used to produce humanoid robots reaching movements obeying the rules of human kinematics. A nonlinear programming problem is solved to determine optimal trajectories. The optimal movements are then encoded by using a basis of motor primitives determined by principal component analysis. Finally, generalization to new movements is obtained by solving of a low-dimensional optimization problem in the operational space.

Small-space controllability of a walking humanoid robot

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.