Claudia Esteves

Humanoid motion planning for dynamic tasks

This paper addresses an integrated humanoid motion planning scheme including both advanced algorithmic motion planning technique and dynamic pattern generator so that the humanoid robot achieve tasks including dynamic motions. A two-stage approach is proposed for this goal. First, geometric and kinematic motion planner first computes collision-free paths for the humanoid robot. Then the dynamic pattern generator provides dynamically feasible humanoid motion including both locomotion and task execution such as object transportation or manipulation. If the generated dynamic motion causes collision due to dynamic movements, the planner go back to the planning stage to remove the collision by path reshaping. This iterative planning scheme enables robust planning against variation of task dynamics. Simulation results are provided to validate the proposed planning method

Planning 3-D Collision-Free Dynamic Robotic Motion Through Iterative Reshaping

We propose a general and practical planning framework for generating 3-D collision-free motions that take complex robot dynamics into account. The framework consists of two stages that are applied iteratively. In the first stage, a collision-free path is obtained through efficient geometric and kinematic sampling-based motion planning. In the second stage, the path is transformed into dynamically executable robot trajectories by dedicated dynamic motion generators. In the proposed iterative method, those dynamic trajectories are sent back again to the first stage to check for collisions. Depending on the application, temporal or spatial reshaping methods are used to treat detected collisions. Temporal reshaping adjusts the velocity, whereas spatial reshaping deforms the path itself. We demonstrate the effectiveness of the proposed method through examples of a space manipulator with highly nonlinear dynamics and a humanoid robot executing dynamic manipulation and locomotion at the same time.

Animation planning for virtual characters cooperation

This paper presents an approach to automatically compute animations for virtual (human-like and robot) characters cooperating to move bulky objects in cluttered environments. The main challenge is to deal with 3D collision avoidance while preserving the believability of the agents behaviors. To accomplish the coordinated task, a geometric and kinematic decoupling of the system is proposed. This decomposition enables us to plan a collision-free path for a reduced system, then to animate locomotion and grasping behaviors independently, and finally to automatically tune the animation to avoid residual collisions. These three steps are applied consecutively to synthesize an animation. The different techniques used, such as probabilistic path planning, locomotion controllers, inverse kinematics and path planning for closed kinematic chains are explained, and the way to integrate them into a single scheme is described.

Smooth Collision Avoidance: Practical Issues in Dynamic Humanoid Motion

In this paper we address smooth and collision-free whole-body motion planning for humanoid robots. A two-stage iterative planning framework is introduced where geometric motion planner and dynamic pattern generator interacts by exchanging the trajectory, to obtain 3D whole-body dynamic motions simultaneous tasks including locomotion, in complex environments. We propose a practical method for smooth motion reshaping to avoid collisions in generated dynamic motion. Based on motion editing techniques in computer graphics animation, smooth collision-avoiding motion is generated through trajectory deformation. The validity of the proposed reshaping method is verified by computer simulations and experiments using humanoid platform HRP-2

INTEGRATING WALKING AND VISION TO INCREASE HUMANOID AUTONOMY

Aiming at building versatile humanoid systems, we present in this paper the real-time implementation of behaviors which integrate walking and vision to achieve general functionalities. The paper describes how real-time — or high-bandwidth — cognitive processes can be obtained by combining vision with walking. The central point of our methodology is to use appropriate models to reduce the complexity of the search space. We will describe the models introduced in the different blocks of the system and their relationships: walking pattern, self-localization and map building, real-time reactive vision behaviors, and planning.

Motion planning for maintaining landmarks visibility with a differential drive robot

This work studies the interaction of non-holonomic and visibility constraints using a Differential Drive Robot (DDR) that has to keep static landmarks in sight in an environment with obstacles. The robot has a limited sensor, namely, it has a restricted field of view and bounded sensing range (e.g. a video camera). Here, we mean by visibility that a clear line of sight can be thrown between the landmark and the sensor mounted on the DDR. We first determine the necessary and sufficient conditions for the existence of a path such that our system is able to maintain one given landmark visibility in the presence of obstacles. This is done through a recursive, complete algorithm that uses motion primitives exhibiting local optimality, as they are locally shortest-lengths paths. Then, we extend this result to the problem of planning paths guaranteeing visibility among a set of landmarks, e.g. to observe a given sequence of landmarks or to observe at each point of the path at least one element of the landmarks set. We also provide a procedure that computes the robot controls yielding such a path.

Vision-guided motion primitives for humanoid reactive walking: Decoupled versus coupled approaches

This paper proposes a novel visual servoing approach to control the dynamic walk of a humanoid robot. Online visual information is given by an on-board camera. It is used to drive the robot towards a specific goal. Our work is built upon a recent reactive pattern generator that make use of model predictive control (MPC) to modify footsteps, center of mass and center of pressure trajectories to track a reference velocity. The contribution of the paper is to formulate the MPC problem considering visual feedback. We compare our approach with a scheme decoupling visual servoing and walking gait generation. Such a decoupled scheme consists of, first, computing a reference velocity from visual servoing; then, the reference velocity is the input of the pattern generator. Our MPC-based approach allows to avoid a number of limitations that appears in decoupled methods. In particular, visual constraints can be introduced directly inside the locomotion controller, while camera motions do not have to be accounted for separately. Both approaches are compared numerically and validated in simulation. Our MPC method shows a faster convergence.

HUMANOID LOCOMOTION PLANNING FOR VISUALLY GUIDED TASKS

In this work, we propose a landmark-based navigation approach that integrates (1) high-level motion planning capabilities that take into account the landmarks position and visibility and (2) a stack of feasible visual servoing tasks based on footprints to follow. The path planner computes a collision-free path that considers sensory, geometric, and kinematic constraints that are specific to humanoid robots. Based on recent results in movement neuroscience that suggest that most humans exhibit nonholonomic constraints when walking in open spaces, the humanoid steering behavior is modeled as a differential-drive wheeled robot (DDR). The obtained paths are made of geometric primitives that are the shortest in distance in free spaces. The footprints around the path and the positions of the landmarks to which the gaze must be directed are used within a stack-of-tasks (SoT) framework to compute the whole-body motion of the humanoid. We provide some experiments that verify the effectiveness of the proposed strategy on the HRP-2 platform.

Mechanical part assembly planning with virtual mannequins

This paper deals with mechanical part assembly planning. The goal is to automatically compute a collision-free path for both the part to be assembled and the mannequin manipulating it. Two approaches are proposed according to the difficulty of the problem. Both are based on a general probabilistic diffusion algorithm working in the configuration space of the considered system. The first approach consists in first planning a path for the part alone and then in checking the feasibility of the solution by adding the mannequin. The second one considers the part grasped and the mannequin as a single system. While the first approach performs quickly the second one is able to solve more constrained and difficult cases. Both solutions are based on the same path planning library allowing the user to easily evaluate the proposed solutions. Experimental results based on feedback experiences in automotive industry are presented

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.