7 research outputs found
Perception-driven sparse graphs for optimal motion planning
Most existing motion planning algorithms assume that a map (of some quality)
is fully determined prior to generating a motion plan. In many emerging
applications of robotics, e.g., fast-moving agile aerial robots with
constrained embedded computational platforms and visual sensors, dense maps of
the world are not immediately available, and they are computationally expensive
to construct. We propose a new algorithm for generating plan graphs which
couples the perception and motion planning processes for computational
efficiency. In a nutshell, the proposed algorithm iteratively switches between
the planning sub-problem and the mapping sub-problem, each updating based on
the other until a valid trajectory is found. The resulting trajectory retains a
provable property of providing an optimal trajectory with respect to the full
(unmapped) environment, while utilizing only a fraction of the sensing data in
computational experiments.Comment: 2018 IEEE/RSJ International Conference on Intelligent Robots and
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
Planning with Adaptive Dimensionality for Mobile Manipulation
Abstract β Mobile manipulation planning is a hard problem composed of multiple challenging sub-problems, some of which require searching through large high-dimensional state-spaces. The focus of this work is on computing a trajectory to safely maneuver an object through an environment, given the start and goal configurations. In this work we present a heuristic search-based deterministic mobile manipulation planner, based on our recently-developed algorithm for planning with adaptive dimensionality. Our planner demonstrates reasonable performance, while also providing strong guarantees on completeness and suboptimality bounds with respect to the graph representing the problem