Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambiguities in qualitative probabilistic networks and searching the best path in stochastic transportation networks. This paper investigates a subclass of the state-space abstraction methods that are designed to approximately evaluate Bayesian networks. Taking advantage of particular sto...

We consider the problem of optimal path planning in different homotopy classes in a given environment. Though important in applications to robotics, homotopy path-planning in applications usually focuses on subsets of the Euclidean plane. The problem of finding optimal trajectories in different homotopy classes in more general configuration spaces (or even characterizing the homotopy classes of...

In this paper we address the problem of trajectory planning with imperfect state information. In many real-world domains, the position of a mobile agent cannot be known perfectly; instead, the agent maintains a probabilistic belief about its position. Planning in these domains requires computing the best trajectory through the space of possible beliefs. We show that planning in belief space can...

We argue that many AI planning problems should be viewed as process-oriented, where the aim is to produce a policy or behavior strategy with no termination condition in mind, as opposed to goal-oriented. The full power of Markov decision models, adopted recently for AI planning, becomes apparent with process-oriented problems. The question of appropriate optimality criteria becomes more critica...

Path planning is often a high-dimensional computationallyexpensive planning problem as it requires reasoning about the kinodynamic constraints of the robot and collisions of the robot with the environment. However, large regions of the environment are typically benign enough that a much faster low-dimensional planning combined with a local path following controller suffice. Planning with Adapti...

| This paper addresses dynamic trajectory planning which is deened as trajectory planning for a robot subject to dynamic constraints and moving in a dynamic workspace, i.e. with moving obstacles. To begin with, we propose the novel concept of state-time space as a tool to formulate dynamic trajectory planning problems. The state-time space of a robot is its state space augmented of the time dim...

This paper describes the foundations and algorithms of a new probabilistic roadmap (PRM) planner that is: single-query – instead of pre-computing a roadmap covering the entire free space, it uses the two input query configurations to explore as little space as possible; bi-directional – it explores the robot’s free space by building a roadmap made of two trees rooted at the query configurations...

We present an algorithm that solves the following motion-planning problem. Given an L-shaped body L and a 2-dimensional region with n point obstacles, decide whether there is a continuous motion connecting two given positions and orientations of L during which L avoids collision with the obstacles. The algorithm requires O(n 2 log 2 n) time and O(n 2) storage. The algorithm is a variant of the ...

The selection of what to do next is often the hardest part of resource-limited problem solving. In planning problems, there are typically many goals to be achieved in some order. The goals interact with each other in ways which depend both on the order in which they are achieved and on the particular operators which are used to achieve them. A planning program needs to keep its options open bec...