7,376 research outputs found
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Many planning applications involve complex relationships defined on
high-dimensional, continuous variables. For example, robotic manipulation
requires planning with kinematic, collision, visibility, and motion constraints
involving robot configurations, object poses, and robot trajectories. These
constraints typically require specialized procedures to sample satisfying
values. We extend PDDL to support a generic, declarative specification for
these procedures that treats their implementation as black boxes. We provide
domain-independent algorithms that reduce PDDLStream problems to a sequence of
finite PDDL problems. We also introduce an algorithm that dynamically balances
exploring new candidate plans and exploiting existing ones. This enables the
algorithm to greedily search the space of parameter bindings to more quickly
solve tightly-constrained problems as well as locally optimize to produce
low-cost solutions. We evaluate our algorithms on three simulated robotic
planning domains as well as several real-world robotic tasks.Comment: International Conference on Automated Planning and Scheduling (ICAPS)
202
Parallelizing RRT on distributed-memory architectures
This paper addresses the problem of improving the performance of the Rapidly-exploring Random Tree (RRT) algorithm by parallelizing it. For scalability reasons we do so on a distributed-memory architecture, using the message-passing paradigm. We present three parallel versions of RRT along with the technicalities involved in their implementation. We also evaluate the algorithms and study how they behave on different motion planning problems
Exploring Graphs with Time Constraints by Unreliable Collections of Mobile Robots
A graph environment must be explored by a collection of mobile robots. Some
of the robots, a priori unknown, may turn out to be unreliable. The graph is
weighted and each node is assigned a deadline. The exploration is successful if
each node of the graph is visited before its deadline by a reliable robot. The
edge weight corresponds to the time needed by a robot to traverse the edge.
Given the number of robots which may crash, is it possible to design an
algorithm, which will always guarantee the exploration, independently of the
choice of the subset of unreliable robots by the adversary? We find the optimal
time, during which the graph may be explored. Our approach permits to find the
maximal number of robots, which may turn out to be unreliable, and the graph is
still guaranteed to be explored.
We concentrate on line graphs and rings, for which we give positive results.
We start with the case of the collections involving only reliable robots. We
give algorithms finding optimal times needed for exploration when the robots
are assigned to fixed initial positions as well as when such starting positions
may be determined by the algorithm. We extend our consideration to the case
when some number of robots may be unreliable. Our most surprising result is
that solving the line exploration problem with robots at given positions, which
may involve crash-faulty ones, is NP-hard. The same problem has polynomial
solutions for a ring and for the case when the initial robots' positions on the
line are arbitrary.
The exploration problem is shown to be NP-hard for star graphs, even when the
team consists of only two reliable robots
The 1990 progress report and future plans
This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers
Parallelizing RRT on large-scale distributed-memory architectures
This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance
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