2 research outputs found
TIE: Time-Informed Exploration For Robot Motion Planning
Anytime sampling-based methods are an attractive technique for solving
kino-dynamic motion planning problems. These algorithms scale well to higher
dimensions and can efficiently handle state and control constraints. However,
an intelligent exploration strategy is required to accelerate their convergence
and avoid redundant computations. Using ideas from reachability analysis, this
work defines a "Time-Informed Set", that focuses the search for time-optimal
kino-dynamic planning after an initial solution is found. Such a Time-Informed
Set (TIS) includes all trajectories that can potentially improve the current
best solution and hence exploration outside this set is redundant. Benchmarking
experiments show that an exploration strategy based on the TIS can accelerate
the convergence of sampling-based kino-dynamic motion planners.Comment: 8 pages, 11 figure
Section Patterns: Efficiently Solving Narrow Passage Problems in Multilevel Motion Planning
Sampling-based planning methods often become inefficient due to narrow
passages. Narrow passages induce a higher runtime, because the chance to sample
them becomes vanishingly small. In recent work, we showed that narrow passages
can be approached by relaxing the problem using admissible lower-dimensional
projections of the state space. Those relaxations often increase the volume of
narrow passages under projection. Solving the relaxed problem is often
efficient and produces an admissible heuristic we can exploit. However, given a
base path, i.e. a solution to a relaxed problem, there are currently no
tailored methods to efficiently exploit the base path. To efficiently exploit
the base path and thereby its admissible heuristic, we develop section
patterns, which are solution strategies to efficiently exploit base paths in
particular around narrow passages. To coordinate section patterns, we develop
the pattern dance algorithm, which efficiently coordinates section patterns to
reactively traverse narrow passages. We combine the pattern dance algorithm
with previously developed multilevel planning algorithms and benchmark them on
challenging planning problems like the Bugtrap, the double L-shape, an egress
problem and on four pregrasp scenarios for a 37 degrees of freedom shadow hand
mounted on a KUKA LWR robot. Our results confirm that section patterns are
useful to efficiently solve high-dimensional narrow passage motion planning
problems.Comment: 16 pages, 11 figures, Transaction on Robotic