This paper describes a new approach to probabilistic roadmap construction for path planning. The novel feature of the planner is that it uses a powerful local planner to produce highly connected roadmaps and path optimization to maintain the rapid query processing by a fast local operator. While most previous approaches obtain good roadmaps by advanced sampling methods, the presented approach c...

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...

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The ...

We present a stochastic planner based on Markov Decision Processes (MDPs) that participates to the probabilistic planning track of the 2006 International Planning Competition. The planner transforms the PPDDL problems into factored MDPs that are then solved with a structured modified value iteration algorithm based on the safest stochastic path computation from the initial states to the goal st...

System components are: knowledge-base the central ontological database perception extracts rectangular faces from RGB-D point clouds scene-generator builds up a set of Scenes waypoint-generator creates good observation poses contingent-planner uses the ontology data to generate smart inspection paths observation-action-planner handles sensor noise by re-running observation actions scenes-to-bel...

Few temporal planners handle both concurrency and uncertain durations, but these features commonly co-occur in realworld domains. In this paper, we discuss the challenges caused by concurrent, durative actions whose durations are uncertain. We present five implemented algorithms, including ∆DURprun, a planner guaranteed to find the optimal policy. An empirical comparison reveals that ∆DURexp, o...

In this paper we propose a new and integrated approach to solve planning problems with both symbolic and geometric aspects. Typical concerned domains are intricate manipulation and task planning problems involving multiple robots in three dimensional worlds. The approach involves a task planner that guides a probabilistic roadmap method used to capture the topology of the free space in various ...

In this work we propose an on-line learning method for learning action rules for planning. The system uses a probabilistic approach of a constructive induction method that combines a beam search with an example-based search over candidate rules to find those that more concisely describe the world dynamics. The approach permits a rapid integration of the knowledge acquired from experience. Explo...

We develop an algorithm to decouple a multi-robot path planning problem into subproblems whose solutions can be executed sequentially. Given an external path planner for general configuration spaces, our algorithm finds an execution sequence that minimizes the dimension of the highest-dimensional subproblem over all possible execution sequences. If the external planner is complete (at least up ...

Robot manipulators generally rely on complete knowledge of object geometry in order to plan motions and compute successful grasps. However, manipulating real-world objects poses a substantial modelling challenge. New instances of known object classes may vary from learned models. Objects that are not perfectly rigid may appear in new configurations that do not match any of the known geometries....