1,656 research outputs found
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Bi-directional search is a widely used strategy to increase the success and
convergence rates of sampling-based motion planning algorithms. Yet, few
results are available that merge both bi-directional search and asymptotic
optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The
objective of this paper is to fill this gap. Specifically, this paper presents
a bi-directional, sampling-based, asymptotically-optimal algorithm named
Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*)
algorithm to bi-directional search while preserving its key properties, chiefly
lazy search and asymptotic optimality through convergence in probability. BFMT*
performs a two-source, lazy dynamic programming recursion over a set of
randomly-drawn samples, correspondingly generating two search trees: one in
cost-to-come space from the initial configuration and another in cost-to-go
space from the goal configuration. Numerical experiments illustrate the
advantages of BFMT* over its unidirectional counterpart, as well as a number of
other state-of-the-art planners.Comment: Accepted to the 2015 IEEE Intelligent Robotics and Systems Conference
in Hamburg, Germany. This submission represents the long version of the
conference manuscript, with additional proof details (Section IV) regarding
the asymptotic optimality of the BFMT* algorith
Dynamic Path Planning and Replanning for Mobile Robots using RRT*
It is necessary for a mobile robot to be able to efficiently plan a path from
its starting, or current, location to a desired goal location. This is a
trivial task when the environment is static. However, the operational
environment of the robot is rarely static, and it often has many moving
obstacles. The robot may encounter one, or many, of these unknown and
unpredictable moving obstacles. The robot will need to decide how to proceed
when one of these obstacles is obstructing it's path. A method of dynamic
replanning using RRT* is presented. The robot will modify it's current plan
when an unknown random moving obstacle obstructs the path. Various experimental
results show the effectiveness of the proposed method
Bi-objective Motion Planning Approach for Safe Motions: Application to a Collaborative Robot
International audienceAccepted version freely available here: [ http://bit.ly/2qlyjJ6 ] Online version via SpringerLink: [ http://link.springer.com/article/10.1007/s10846-019-01110-1 ] Abstract: This paper presents a new bi-objective safety-oriented path planning strategy for robotic manipulators. Integrated into a sampling-based algorithm, our approach can successfully enhance the task safety by guiding the expansion of the path towards the safest configurations. Our safety notion consists of avoiding dangerous situations, e.g. being very close to the obstacles, human awareness, e.g. being as much as possible in the human vision field, as well as ensuring human safety by being as far as possible from human with hierarchical priority between human body parts. Experimental validations are conducted in simulation and on the real Baxter research robot. They revealed the efficiency of the proposed method, mainly in the case of a collaborative robot sharing the workspace with humans
Admissible Velocity Propagation : Beyond Quasi-Static Path Planning for High-Dimensional Robots
Path-velocity decomposition is an intuitive yet powerful approach to address
the complexity of kinodynamic motion planning. The difficult trajectory
planning problem is solved in two separate, simpler, steps: first, find a path
in the configuration space that satisfies the geometric constraints (path
planning), and second, find a time-parameterization of that path satisfying the
kinodynamic constraints. A fundamental requirement is that the path found in
the first step should be time-parameterizable. Most existing works fulfill this
requirement by enforcing quasi-static constraints in the path planning step,
resulting in an important loss in completeness. We propose a method that
enables path-velocity decomposition to discover truly dynamic motions, i.e.
motions that are not quasi-statically executable. At the heart of the proposed
method is a new algorithm -- Admissible Velocity Propagation -- which, given a
path and an interval of reachable velocities at the beginning of that path,
computes exactly and efficiently the interval of all the velocities the system
can reach after traversing the path while respecting the system kinodynamic
constraints. Combining this algorithm with usual sampling-based planners then
gives rise to a family of new trajectory planners that can appropriately handle
kinodynamic constraints while retaining the advantages associated with
path-velocity decomposition. We demonstrate the efficiency of the proposed
method on some difficult kinodynamic planning problems, where, in particular,
quasi-static methods are guaranteed to fail.Comment: 43 pages, 14 figure
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
Experience-Based Planning with Sparse Roadmap Spanners
We present an experienced-based planning framework called Thunder that learns
to reduce computation time required to solve high-dimensional planning problems
in varying environments. The approach is especially suited for large
configuration spaces that include many invariant constraints, such as those
found with whole body humanoid motion planning. Experiences are generated using
probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which
provides asymptotically near-optimal coverage of the configuration space,
making storing, retrieving, and repairing past experiences very efficient with
respect to memory and time. The Thunder framework improves upon past
experience-based planners by storing experiences in a graph rather than in
individual paths, eliminating redundant information, providing more
opportunities for path reuse, and providing a theoretical limit to the size of
the experience graph. These properties also lead to improved handling of
dynamically changing environments, reasoning about optimal paths, and reducing
query resolution time. The approach is demonstrated on a 30 degrees of freedom
humanoid robot and compared with the Lightning framework, an experience-based
planner that uses individual paths to store past experiences. In environments
with variable obstacles and stability constraints, experiments show that
Thunder is on average an order of magnitude faster than Lightning and planning
from scratch. Thunder also uses 98.8% less memory to store its experiences
after 10,000 trials when compared to Lightning. Our framework is implemented
and freely available in the Open Motion Planning Library.Comment: Submitted to ICRA 201
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