Algorithms to solve probabilistic planning problems can be classified in probabilistic planners and replanners. Probabilistic planners invest significant computational effort to generate a closed policy, i.e., a mapping function from every state to an action, and these solutions never “fail” if the problem correctly models the environment. Alternatively, replanners computes a partial policy, i....

We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between pro...

Engineering complete planning domain descriptions is often very costly because of human-error or lack of domain knowledge. While many have studied knowledge acquisition, relatively few have studied the synthesis of plans when the domain model is incomplete (i.e., actions have incomplete preconditions or effects). Prior work has evaluated the correctness of plans synthesized by disregarding such...

Demining and unexploded ordnance (UXO) clearance are extremely tedious and dangerous tasks. The use of robots bypasses the hazards and potentially increases the efficiency of both tasks. A first crucial step towards robotic mine/UXO clearance is to locate all the targets. This requires a path planner that generates a path to pass a detector over all points of a mine/UXO field, i.e., a planner t...

In this article, we present a framework for planning an activity to be executed with the support of robotic navigation assistant. The two main components are and motion planner. planner composes sequence abstract activities, chosen from given set, synthesize plan. Each is associated point interest in environment probabilistic parameters that depend on plan, which characterized by simulations re...

Probabilistic Roadmaps (PRM) have been successfully used to plan complex robot motions in configuration spaces of small and large dimensionalities. However, their efficiency decreases dramatically in spaces with narrow passages. This paper presents a new method – smallstep retraction – that helps PRM planners find paths through such passages. This method consists of slightly “fattening” robot’s...

We describe a planner that participates in the Probabilistic Planning Track of the 2004 International Planning Competition. Our planner integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states.

Probabilistic path planning techniques have proven to be vital for finding and validating solutions for difficult industrial assembly tasks. Nevertheless, the failure of a path planner to find a solution to a task does not suggest how to correct the error. We suggest a methodology to identify possible bottlenecks and present an algorithm to analyze the extent to which the design must be modifie...

This paper presents a robot motion planning method, called PHM, that uses a random sampling scheme together with a potential-field approach based on harmonic functions. The combination of both results in an efficient path planner that is both resolution and probabilistic complete. On one hand, random sampling allows the use of the harmonic functions approach without the explicit knowledge of th...

This paper presents methods for navigating and localizing mobile robots in a known indoor environment. We introduce a restricted visibility concept called a scannable sector that can aid many existing navigation and localization algorithms. The scannable sectors are based on the physical characteristics of the environment and the limitations of the localization sensors used. We describe a compl...