94 research outputs found
Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
Informed sampling-based planning algorithms exploit problem knowledge for
better search performance. This knowledge is often expressed as heuristic
estimates of solution cost and used to order the search. The practical
improvement of this informed search depends on the accuracy of the heuristic.
Selecting an appropriate heuristic is difficult. Heuristics applicable to an
entire problem domain are often simple to define and inexpensive to evaluate
but may not be beneficial for a specific problem instance. Heuristics specific
to a problem instance are often difficult to define or expensive to evaluate
but can make the search itself trivial.
This paper presents Adaptively Informed Trees (AIT*), an almost-surely
asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its
search to each problem instance by using an asymmetric bidirectional search to
simultaneously estimate and exploit a problem-specific heuristic. This allows
it to quickly find initial solutions and converge towards the optimum. AIT*
solves the tested problems as fast as RRT-Connect while also converging towards
the optimum.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2020,
6 + 2 pages, 5 figures, video available at https://youtu.be/twM723QM9T
Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps
Inspection planning, the task of planning motions for a robot that enable it to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans grows exponentially with the number of points of interest to inspect. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of successfully inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion-planning roadmap using a sampling-based algorithm and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We prove the resulting algorithm is asymptotically optimal under very general assumptions about the robot and the environment. We demonstrate IRIS’s efficacy on a simulated inspection task with a planar five DOF manipulator, on a simulated bridge inspection task with an Unmanned Aerial Vehicle (UAV), and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered human anatomy. In all these systems IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method
Motion Planning for Manipulation With Heuristic Search
Heuristic searches such as A* search are a popular means of finding least-cost
plans due to their generality, strong theoretical guarantees on completeness
and optimality, simplicity in implementation, and consistent behavior. In
planning for robotic manipulation, however, these techniques are commonly
thought of as impractical due to the high-dimensionality of the planning
problem. As part of this thesis work, we have developed a heuristic
search-based approach to motion planning for manipulation that does deal
effectively with the high-dimensionality of the problem. In this thesis,
I will present the approach together with its theoretical properties and show
how to apply it to single-arm and dual-arm motion planning with upright
constraints on a PR2 robot operating in non-trivial cluttered spaces. Then
I will explain how we extended our approach to manipulation planning for
n-arms with regrasping. In this work, the planner itself makes all of the
discrete decisions, including which arm to use for the pickup and putdown, whether
handoffs are necessary and how the object should be grasped at each step along
the way.
An extensive experimental analysis in both simulation and on a physical PR2
shows that, in terms of runtime, our approach is on par with some of the most
common sampling-based approaches. This includes benchmarking our planning
framework on two domains that we constructed that are common to manufacturing:
pick-and-place of fast moving objects and the autonomous assembly of small
objects. Between these applications, the planner exhibited fast planning times
and the ability to robustly plan paths into and out of tight working
environments that are common to assembly. The closing work of this thesis
includes an exhaustive study of the natural tradeoff that occurs between
planning efficiency versus solution quality for different values of the
heuristic inflation factor. A comparison of the solution quality of our planner
to paths computed by an asymptotically optimal approach given a great deal of
time for path optimization is included as well. Finally, a set of experimental
results are included that show that due to our approach\u27s deterministic
cost-minimization, similar input tends to lead to similarity in the output. This
kind of local consistency is important to the predictability of the robot\u27s
motions and contributes to human-robot safety
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