We introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space C. Our algorithm differs from a Probabilistic Road Map algorithm (PRM) in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace O. We define a varying potential field f on ∂O as a Morse function and follow ~ ∇f . We then ...

This paper presents work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. The SLIP algorithm, an extension of Iterative Closest Point, combines motion detection fr...

Efficient robotic exploration of an unknown, sensor limited, global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated Exploration (IE), which strives to combine localization and exploration, must be solved in order to create...

Autonomous robot An autonomous robot is a robot that can perform tasks in unstructured environments with minimal human guidance. Planned path A planned path is a pre-determined, obstacle-free, trajectory that a robot can follow to reach its goal position from its starting position. Complete path planner A complete path planner is an algorithm that is guaranteed to find a path, if one exists. De...

We introduce probabilistic many-valued logic programs in which the implication connective is interpreted as material implication. We show that probabilistic many-valued logic programming is computationally more complex than classical logic programming. More precisely, some deduction problems that are P-complete for classical logic programs are shown to be co-NP-complete for probabilistic many-v...

This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hindsight optimization is an online technique that evaluates the onestep-reachable states by sampling future outcomes to generate multiple non-stationary deterministic planning problems which can then be solved using searc...

We focus on the aspect of sensing in reasoning about actions under qualitative and probabilistic uncertainty. We first define the action language E for reasoning about actions with sensing, which has a semantic foundation on the autoepistemic description logic ALCKNF , and which is given a formal semantics in a system of deterministic transitions between epistemic states. As an important featur...

Completion of partial plans is a subtask for many planning techniques such as plan reusing, replanning and accomplishing complex user goals. The new generation of fast planners such as Graphplan, Satplan and others, is characterized by very efficent planning algorithms which exploit techniques of multiple plans representation. Unfortunately it is fairly difficult to give Graphplan a partial pla...

The TWEAK system (Chapman 87) is a very nice synthesis of a long trend in planning and Artificial Intelligence, including STRIPS (Fikes & Nilsson 71). We derive a correctness criterion which is simpler that the one used for TWEAK since it does not involve modal operators and does not introduce neither white knights nor separation. This criterion is compared to the one of TWEAK and other related...

Traditional approaches to task planning assume the planner has access to all of the world information needed to develop a complete, correct plan|a plan which can then be executed in its entirety by a robot. We consider problems where some crucial information is missing at plan time but can be obtained from sensors during execution. We discuss the solution of these problems through deferred plan...