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Real-time modification of collision-free paths

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TLDR
A novel algorithm for efficiently computing the distance between non-convex objects and a real-time algorithm for calculating a discrete approximation to the time-optimal parameterization of a path are developed and combined in a system that demonstrates the elastic band framework for a Puma 560 manipulator.
Abstract
The modification of collision-free paths is proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. A physically-based model of a flexible string-like object, called an elastic band, is used to determine the modification of a path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. The greater part of this thesis deals with the design and implementation of elastic bands, with emphasis on achieving real-time performance even for robots with many degrees of freedom. To achieve these goals, we propose the concept of bubbles of free-space---a region of free-space around a given configuration of the robot generated from distance information. We also develop a novel algorithm for efficiently computing the distance between non-convex objects and a real-time algorithm for calculating a discrete approximation to the time-optimal parameterization of a path. These various developments are combined in a system that demonstrates the elastic band framework for a Puma 560 manipulator.

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Citations
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Proceedings ArticleDOI

CHOMP: Gradient optimization techniques for efficient motion planning

TL;DR: This paper presents CHOMP, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories and relax the collision-free feasibility prerequisite on input paths required by those strategies.
Journal ArticleDOI

CHOMP: Covariant Hamiltonian optimization for motion planning

TL;DR: CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization, uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component.
Journal ArticleDOI

Elastic Strips: A Framework for Motion Generation in Human Environments

TL;DR: The elastic strip framework presented in this paper enables the execution of a previously planned motion in a dynamic environment for robots with many degrees of freedom, and encompasses methods to suspend task behavior when its execution becomes inconsistent with other constraints imposed on the motion.
Proceedings ArticleDOI

Multipartite RRTs for Rapid Replanning in Dynamic Environments

TL;DR: The multipartite RRT (MP-RRT), an RRT variant which supports planning in unknown or dynamic environments, is presented, by purposefully biasing the sampling distribution and re-using branches from previous planning iterations.
Journal ArticleDOI

Integrated online trajectory planning and optimization in distinctive topologies

TL;DR: A novel integrated approach for efficient optimization based online trajectory planning of topologically distinctive mobile robot trajectories by maintains and simultaneously optimizes a subset of admissible candidate trajectories of distinctive topologies and thus seeking the overall best candidate among the set of alternative local solutions.
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