170 research outputs found
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
db-A*: Discontinuity-bounded Search for Kinodynamic Mobile Robot Motion Planning
We consider time-optimal motion planning for dynamical systems that are
translation-invariant, a property that holds for many mobile robots, such as
differential-drives, cars, airplanes, and multirotors. Our key insight is that
we can extend graph-search algorithms to the continuous case when used
symbiotically with optimization. For the graph search, we introduce
discontinuity-bounded A* (db-A*), a generalization of the A* algorithm that
uses concepts and data structures from sampling-based planners. Db-A* reuses
short trajectories, so-called motion primitives, as edges and allows a maximum
user-specified discontinuity at the vertices. These trajectories are locally
repaired with trajectory optimization, which also provides new improved motion
primitives. Our novel kinodynamic motion planner, kMP-db-A*, has almost surely
asymptotic optimal behavior and computes near-optimal solutions quickly. For
our empirical validation, we provide the first benchmark that compares search-,
sampling-, and optimization-based time-optimal motion planning on multiple
dynamical systems in different settings. Compared to the baselines, kMP-db-A*
consistently solves more problem instances, finds lower-cost initial solutions,
and converges more quickly.Comment: Accepted at IROS 202
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
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