Oussama Kanoun

Prioritizing linear equality and inequality systems: Application to local motion planning for redundant robots

We present a novel method for prioritizing both linear equality and inequality systems and provide one algorithm for its resolution. This algorithm can be summarized as a sequence of optimal resolutions for each linear system following their priority order. We propose an optimality criterion that is adapted to linear inequality systems and characterize the resulting optimal sets at every priority level. We have successfully applied our method to plan local motions for the humanoid robot HPR-2. We will demonstrate the validity of the method using an original scenario where linear inequality constraints are solved at lower priority than equality constraints.

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

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.

A Local Collision Avoidance Method for Non-strictly Convex Polyhedra

This paper proposes a local collision avoidance method for non-strictly convex polyhedra with continuous velocities. The main contribution of the method is that non-strictly convex polyhedra can be used as geometric models of the robot and the environment without any approximation. The problem of the continuous interaction generation between polyhedra is reduced to the continuous constraints generation between polygonal faces and the continuity of those constraints are managed by the combinatorics based on Voronoi regions of a face. A collision-free motion is obtained by solving an optimization problem defined by an objective function which describes a task and linear inequality constraints which do geometrical constraints to avoid collisions. The proposed method is examined using example cases of simple objects and also applied to a humanoid robot HRP-2.

Whole-body motion planning for pivoting based manipulation by humanoids

This paper emphasizes on the capacity of a humanoid robot to perform tasks that are difficult for other types of robots. It deals with manipulation of bulky objects. Such tasks require complicated manipulations involving the whole-body and line coordination between legs, arms and torso motions. We introduce here a whole-body motion planner that allows a humanoid robot to autonomously plan a pivoting strategy that accounts for the various constraints: collision avoidance, legs-arms coordination and stability control. Based on a previous result by the authors [1] proving the small-time controllability of a pivoting system, the planner is proven to inherit from the probabilistic completeness of the sampling- based motion planning method it is built on. The geometric and kinematic capacity of the proposed planner is mainly demonstrated through simulations and experiments.

Pivoting based manipulation by a humanoid robot

In this paper we address whole-body manipulation of bulky objects by a humanoid robot. We adopt a “pivoting” manipulation method that allows the humanoid to displace an object without lifting, but by the support of the ground contact. First, the small-time controllability of pivoting is demonstrated. On its basis, an algorithm for collision-free pivoting motion planning is established taking into account the naturalness of motion as nonholonomic constraints. Finally, we present a whole-body motion generation method by a humanoid robot, which is verified by experiments.

“Give me the purple ball” - he said to HRP-2 N.14

This paper reports current experiments conducted on HRP-2 based research on robot autonomy. The contribution of the paper is not focused on a specific area but its objective is to highlight the critical issues that had to be solved to allow the humanoid robot HRP-2 to understand and execute the order ldquogive me the purple ballrdquo in an autonomous way. Such an experiment requires: simple object recognition and localization, motion planning and control, natural spoken language supervision, simple action supervisor and control architecture.

An optimization formulation for footsteps planning

We present a novel method to solve the problem of planning footsteps for a humanoid robot according to an arbitrary set of tasks. In this method, we consider the sequence of footsteps required to solve a task as a virtual kinematic chain that augments the state of the humanoid robot. We introduce this representation to formulate the footsteps planning as an iterative constrainted optimization problem where the footsteps are accounted for as additional degrees of freedom helping the robot in achieving its tasks. We demonstrate the efficiency and the generality of the method through three task scenarios for the humanoid robot HRP-2.

Regrasp planning for pivoting manipulation by a humanoid robot

A method of regrasp planning for humanoid robot manipulation is proposed. We adopt pivoting manipulation for the humanoid robot to move a bulky object without lifting in a stable and dexterous manner. In order to carry the object to a desired place, the humanoid should sometimes move through narrow areas surrounded by obstacles. We propose a roadmap multiplexing planning to allow the robot to leave the object near narrow places and to regrasp it from another position to continue carrying. We utilize visibility probabilistic roadmap (PRM) method as a preprocessing to capture the critical configurations for regrasping. Then a diffusion method is employed to plan the overall manipulation path including regrasping. The proposed method is verified through planning simulation including whole-body motions.

Planning Whole-body Humanoid Locomotion, Reaching, and Manipulation

In this chapter we address the planning problem of whole-body motions by humanoid robots. The approach presented benefits from two cutting edge recent advancements in robotics: powerful probabilistic geometric and kinematic motion planning and advanced dynamic motion control for humanoids. First, we introduce a two-stage approach that combines these two techniques for collision-free simultaneous locomotion and upper-body task. Then a whole-body motion generationmethod is presented for reaching, including steps based on generalized inverse kinematics. The third example is planning of whole-body manipulation of a large object by “pivoting”, by making use of the precedent results. Finally, an integrated experiment is shown in which the humanoid robot interacts with its environment through perception. The humanoid robot platform HRP-2 is used as the platform to validate the results.

Whole-Body Locomotion, Manipulation and Reaching for Humanoids

This paper deals with motion planning and dynamic control for humanoid robots. The first part addresses simultaneous locomotion and manipulation planning while the second part deals with reaching tasks. The validity of the proposed methods is verified by experiments using humanoid platform HRP-2.