1,204 research outputs found
Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs
Traversing environments with arbitrary obstacles poses significant challenges
for bipedal robots. In some cases, whole body motions may be necessary to
maneuver around an obstacle, but most existing footstep planners can only
select from a discrete set of predetermined footstep actions; they are unable
to utilize the continuum of whole body motion that is truly available to the
robot platform. Existing motion planners that can utilize whole body motion
tend to struggle with the complexity of large-scale problems. We introduce a
planning method, called the "Randomized Possibility Graph", which uses
high-level approximations of constraint manifolds to rapidly explore the
"possibility" of actions, thereby allowing lower-level motion planners to be
utilized more efficiently. We demonstrate simulations of the method working in
a variety of semi-unstructured environments. In this context,
"semi-unstructured" means the walkable terrain is flat and even, but there are
arbitrary 3D obstacles throughout the environment which may need to be stepped
over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation
201
Parallelizing RRT on large-scale distributed-memory architectures
This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance
Negotiating Large Obstacles with a Humanoid Robot via Multi-Contact Motion Planning
Incremental progress in humanoid robot locomotion over the years has achieved essential capabilities such as navigation over
at or uneven terrain, stepping over small obstacles and imbing stairls. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-contact motion planning, guided by physical human demonstrations. Our goal is to make humanoid locomotion
problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the multi-contacts at the upper and lower body limbs. We propose a contact stability measure, which simplies the contact search from demonstration and
contact transition motion generation for the multi-contact motion planning algorithm. The algorithm uses the whole-body motions generated via Quadratic Programming (QP) based solver methods. The multi-contact motion planning algorithm is applied for a challenging task of climbing over a relatively larger obstacle compared to the robot. We validate our
planning approach with simulations and experiments for climbing over a large wooden obstacle with COMAN, which is a complaint humanoid robot with 23 degrees of freedom (DOF). We also propose a generalization method, the \Policy-Contraction Learning Method" to extend the algorithm for generating new multi-contact plans for our multi-contact motion planner, that can adapt to changes in the environment. The method learns a general policy and the multi-contact behavior from the human demonstrations, for generating new multi-contact plans for the obstacle-negotiation
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
A randomized kinodynamic planner for closed-chain robotic systems
Kinodynamic RRT planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, cooperating arms manipulating an object, or legged robots keeping their feet in contact with the environ- ment. The state space of such systems is an implicitly-defined manifold, which complicates the design of the sampling and steering procedures, and leads to trajectories that drift away from the manifold when standard integration methods are used. To address these issues, this report presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to both generate ran- dom states, and to dynamically steer the system towards such states. The steering method is based on computing linear quadratic regulators from the atlas charts, which greatly increases the planner efficiency in comparison to the standard method that simulates random actions. The atlas also allows the integration of the equations of motion as a differential equation on the state space manifold, which eliminates any drift from such manifold and thus results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. We illustrate the performance of the approach in significantly complex tasks, including planar and spatial robots that have to lift or throw a load at a given velocity using torque-limited actuators.Peer ReviewedPreprin
- …