This paper presents a randomized motion planner for kinodynamic asteroid avoidanceproblems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilistic-roadmap (PRM) techniques, the planner samples the state time space of a robot by picking control inputs at random in order to compute a roadmap that...

We present a partial-order probabilistic planning algorithm that adapts plan-graph based heuristics implemented in Repop. We describe our implemented planner, Reburidan, named after its predecessors Repop and Buridan. Reburidan uses plan-graph based heuristics to first generate a base plan. It then improves this plan using plan refinement heuristics based on the success probability of subgoals....

A simple sample-based planning method is presented which approximates connected regions of free space with volumes in Configuration space instead of points. The algorithm produces very sparse trees compared to point-based planning approaches, yet it maintains probabilistic completeness guarantees. The planner is shown to improve performance on a variety of planning problems, by focusing samplin...

This paper presents a method of computing efficient and naturallooking motions for humanoid robots walking on varied terrain. It uses a small set of high-quality motion primitives (such as a fixed gait on flat ground) that have been generated offline. But rather than restrict motion to these primitives, it uses them to derive a sampling strategy for a probabilistic, sample-based planner. Result...

Classical AI planners use loops over subgoals to move a stack of blocks by repeatedly moving the top block. Probabilistic planners and reactive systems repeatedly try to pick up a block to increase the probability of success in an uncertain environment. These planners terminate a loop only when the goal is achieved or when the probability of success has reached some threshold. The tradeoff betw...

The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based planners that can quickly solve large problems. However, existing methods employ a simplistic approach to determinization. In particular, they ignore the possibility of tailoring the determinization to the specific c...

The degree to which a planning system succeeds depends on its ability to meet critical deadlines as well as the correctness and completeness of its models which describe events and actions that change the world state. It is often unrealistic to expect either unlimited execution time or perfect models, so a planner must be able to make appropriate time vs. quality tradeoffs, then detect and resp...

Moving a crowd of robots or avatars from their current configurations to some destination area without causing collisions is a challenging motion-planning problem because the high degrees of freedom involved. Two approaches are often used for this type of problems: decoupled and centralized. The tradeoff of these two approaches is that the decoupled approach is considered faster while the centr...

The Graphplan planner has enjoyed considerable success as a planning algorithm for classical STRIPS domains In this paper we explore the extent to which its representation can be used for probabilistic planning In particular we consider an MDP style framework in which the state of the world is known but actions are probabilistic and the objective is to produce a nite horizon contingent plan wit...

This paper presents a motion planning algorithm capable of exploiting the experience gained in previous path computations in the same static workspace. The algorithm takes advantage of a parallel approach to speed up computation and compile a graph retaining useful knowledge about the environment. Experimental results assess the performance improvement of the experience-based planner over the p...