This paper presents how to compute "optimal" speed profile on a path that can be made by a path planner. "Optimality" is defined as the minimization of the trajectory total time under constraints on any number of its derivatives (speed, acceleration, jerk, ...). Then the resulting trajectory can be used in real system to have precise control over it.

This paper presents a work toward the old dream of the housekeeping robot. One humanoid robot will cooperate with the user to cook simple dishes. The system combines predefined tasks and dialogues to find a plan in which both robot and user help each other in the kitchen. The kitchen problem allows the demonstration of a large variety of actions, and then the necessity to find and to plan those actions. With this problem the task planner can be fully used to enhance the robot reasoning capacity. Furthermore the robot must also use motion planning to have general procedures to cope with the action planned. We focus on the planning problems and the interactions of these two planning methods

We propose a resolution scheme that is aimed to solve a wide range of multi-robot planning problems in a 3D geometrical environment. This complements our efforts in developing manipulation planning algorithms to deal with multiple robots and several movable objects problems. While the elementary planning step relies on Probabilistic Roadmap Methods (PRMs), the main contribution here is a reasoning level that adapts its control over the construction and extension of a number of roadmaps. The coherence between roadmaps and the hierarchical search process are done through a new type of graph built for a subset of objects or robots in the environment, called the Elementary Kinematic Graph. This paper describes the main ingredients of the proposed framework, and its first results.

We propose in this paper a resolution scheme that is aimed to be relevant for a large class of manipulation planning problems. This endeavor complements our efforts in developing manipulation planning algorithms. Indeed, we are convinced that a higher level of problems complexity, and particularly those involving multiple robots and multiple objects, will be accessible thanks to the introduction of a symbolic reasoning level. The resolution scheme relies on probabilistic roadmap methods (PRMs) and on a reasoning level that adaptively controls the construction and extension of a number of roadmaps. We consider this symbolic level as a step towards a systematic approach to integrate task planning and geometric planning in better conditions than through a gross, and somewhat, artificial hierarchical decomposition. This paper describes the main ingredients of the proposed framework, and its first results.

This paper reports on our efforts to extend the plan-merging paradigm, in order to improve its ability to adapt to various execution contexts. They concern the algorithmic as well as the architectural issues. The scheme can be parameterized, allowing to choose and even to update online robot priorities, and to take into account various constraints imposed by the application. These lead to different coordination policies. The architectural improvements allowed a better distribution of the computation load, leading to more complex applications, for instance, the coordination of multiple mobile manipulators. The result is a more flexible scheme that should widen its application fields. We describe the main ingredients of this new scheme and illustrate its implementation through two examples.