309 research outputs found
Sampling-Based Motion Planning: A Comparative Review
Sampling-based motion planning is one of the fundamental paradigms to
generate robot motions, and a cornerstone of robotics research. This
comparative review provides an up-to-date guideline and reference manual for
the use of sampling-based motion planning algorithms. This includes a history
of motion planning, an overview about the most successful planners, and a
discussion on their properties. It is also shown how planners can handle
special cases and how extensions of motion planning can be accommodated. To put
sampling-based motion planning into a larger context, a discussion of
alternative motion generation frameworks is presented which highlights their
respective differences to sampling-based motion planning. Finally, a set of
sampling-based motion planners are compared on 24 challenging planning
problems. This evaluation gives insights into which planners perform well in
which situations and where future research would be required. This comparative
review thereby provides not only a useful reference manual for researchers in
the field, but also a guideline for practitioners to make informed algorithmic
decisions.Comment: 25 pages, 7 figures, Accepted for Volume 7 (2024) of the Annual
Review of Control, Robotics, and Autonomous System
Multilevel Motion Planning: A Fiber Bundle Formulation
Motion planning problems involving high-dimensional state spaces can often be
solved significantly faster by using multilevel abstractions. While there are
various ways to formally capture multilevel abstractions, we formulate them in
terms of fiber bundles, which allows us to concisely describe and derive novel
algorithms in terms of bundle restrictions and bundle sections. Fiber bundles
essentially describe lower-dimensional projections of the state space using
local product spaces. Given such a structure and a corresponding admissible
constraint function, we can develop highly efficient and optimal search-based
motion planning methods for high-dimensional state spaces. Our contributions
are the following: We first introduce the terminology of fiber bundles, in
particular the notion of restrictions and sections. Second, we use the notion
of restrictions and sections to develop novel multilevel motion planning
algorithms, which we call QRRT* and QMP*. We show these algorithms to be
probabilistically complete and almost-surely asymptotically optimal. Third, we
develop a novel recursive path section method based on an L1 interpolation over
path restrictions, which we use to quickly find feasible path sections. And
fourth, we evaluate all novel algorithms against all available OMPL algorithms
on benchmarks of eight challenging environments ranging from 21 to 100 degrees
of freedom, including multiple robots and nonholonomic constraints. Our
findings support the efficiency of our novel algorithms and the benefit of
exploiting multilevel abstractions using the terminology of fiber bundles.Comment: Submitted to IJR
Multi-Agent Persistent Task Performance
A method to control a system of robots to persistently perform a task while operating under a constraint such as battery life is presented. Persistently performing a task is defined as continuously executing the task without a break or stopping due to low battery constraints or lack of capabilities of a particular agent. If an agent is no longer able to execute the task it must be replaced by one that can continue the execution of the task. This is achieved through the utilization of two distinctions of agent roles: workers and helpers. This method is focused on addressing problems that require task handoffs where a second robot physically replaces a robot that has run low on battery. The worker agents are assigned the tasks, and perform the tasks until the constraint prevents further performance. Once a worker agent has reached a low battery threshold a task handoff is performed with a helper agent. This method utilizes a proactive approach in performing these handoffs by predicting the time and place that a worker will reach a low battery threshold and need to perform a handoff. This decreases the time necessary to respond to a low battery in these problems compared to prior developed reactive methods. As a result the total time needed by the multi agent team to complete a set of tasks is decreased. In this paper, the method is demonstrated utilizing a physics based simulator to model the behavior of the multi agent team. Experiments are run over three standard problems requiring agent task handoffs: sentry, inspection, and coverage. These demonstrate the effectiveness of the method when compared against the existing reactive methods
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure
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