We present and implement an efficient algorithm for performing nearest-neighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd tree-based ANN algorithm, which was developed by Arya and Mount for Euclidean spaces. We argue the correctness of the algorithm and illustrate its efficiency through computed examples. We have applied the ...

This paper is devoted to path planning when the safety of the system considered has to be guaranteed in the presence of bounded uncertainty affecting its model. A new path planner addresses this problem by combining Rapidly-exploring Random Trees (RRT) and a set representation of uncertain states. An idealized algorithm is presented first, before a description of one of its possible implementat...

We present an experience-based pushmanipulation method, where the mobile robot learns through experimentation how the pushable real world objects with complex 3D structures move in response to various pushing actions. These experimentally acquired models are then used as building blocks for constructing achievable push plans via a Rapidly-exploring Random Trees variant planning algorithm we con...

We propose a randomized STRIPS planning algorithm called RRT-Plan. This planner is inspired by the idea of Rapidly exploring Random Trees, a concept originally designed for use in continuous path planning problems. Issues that arise in the conversion of RRTs from continuous to discrete spaces are discussed, and several additional mechanisms are proposed to improve performance. Our experimental ...

This paper is devoted to path planning when the safety of the system considered has to be guaranteed in the presence of bounded uncertainty affecting its model. A new path planner addresses this problem by combining Rapidly-exploring Random Trees (RRT) and a set representation of uncertain states. An idealized algorithm is presented first, before a description of one of its possible implementat...

In this paper, we survey sampling-based reachability algorithms that may be used for planning, control, and verification of complex systems. These were inspired by two robotics motion planning algorithms: Rapidly-exploring Random Trees (RRTs) and Probabilistic RoadMaps (PRMs). Herein, we review RRTs and PRMs. We review our adaptations and extensions to nonlinear control problems, hybrid systems...

Sampling-based approaches such as Probabilistic Roadmaps and Rapidly-exploring Random Trees are very popular in motion planning. Monte Carlo Random Walks (MRW) are a quite different sampling method. They were implemented in the Arvand family of planners, which have been successful in classical planning with its discrete state spaces and actions. The work described here develops an MRW approach ...

This paper proposes a sampling based planning algorithm to control autonomous vehicles. We propose an improved Rapidly-exploring Random Tree which includes the definition of Knearest points and propose a two-stage sampling strategy to adjust RRT in other to perform maneuver while avoiding collision. The simulation results show the success of the algorithm.