Topic
Motion planning
About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.
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26 May 2015
TL;DR: A multiagent path planning algorithm based on sequential convex programming that finds locally optimal trajectories that runs 14% faster and finds feasible trajectories with higher probability than a decoupled implementation of previous SCP-based algorithms.
Abstract: This paper presents a multiagent path planning algorithm based on sequential convex programming (SCP) that finds locally optimal trajectories. Previous work using SCP efficiently computes motion plans in convex spaces with no static obstacles. In many scenarios where the spaces are non-convex, previous SCP-based algorithms failed to find feasible solutions because the convex approximation of collision constraints leads to forming a sequence of infeasible optimization problems. This paper addresses this problem by tightening collision constraints incrementally, thus forming a sequence of more relaxed, feasible intermediate optimization problems.We show that the proposed algorithm increases the probability of finding feasible trajectories by 33% for teams of more than three vehicles in non-convex environments. Further, we show that decoupling the multiagent optimization problem to a number of single-agent optimization problems leads to significant improvement in computational tractability. We develop a decoupled implementation of the proposed algorithm, abbreviated dec-iSCP. We show that dec-iSCP runs 14% faster and finds feasible trajectories with higher probability than a decoupled implementation of previous SCP-based algorithms. The proposed algorithm is real-time implementable and is validated through hardware experiments on a team of quadrotors.
152 citations
29 May 1995
TL;DR: Under a simple assumption about the configuration space, it is shown that it is possible to perform a preprocessing step following which queries can be answered quickly and pose and give solutions to related problems on graph connectivity in the evasiveness model, and art gallery theorems.
Abstract: : The subject of this paper is the analysis of a randomized preprocessing scheme that has been used for query processing in robot motion planning. The attractiveness of the scheme stems from its general applicability to virtually any motion planning problem, and its empirically observed success. In this paper we initiate a theoretical basis for explaining this empirical success. Under a simple assumption about the configuration space, we show that it is possible to perform a preprocessing step following which queries can be answered quickly. En route, we pose and give solutions to related problems on graph connectivity in the evasiveness model, and art gallery theorems.
152 citations
TL;DR: A path planning algorithm is presented for uninhabited aerial vehicles trying to geolocate an emitter using passive payload sensors to generate a sequence of waypoints for each vehicle that minimizes localization uncertainty.
Abstract: A path planning algorithm is presented for uninhabited aerial vehicles (UAVs) trying to geolocate an emitter using passive payload sensors. The objective is to generate a sequence of waypoints for each vehicle that minimizes localization uncertainty. The path planning problem is cast as a nonlinear programming problem using an approximation of the Fisher information matrix (FIM) and solved at successive waypoints to generate vehicle trajectories. The effectiveness of the proposed algorithm is illustrated with simulation examples.
152 citations
01 Mar 1992
TL;DR: Two problems for path planning of a mobile robot are considered and a collision-free path is found by a variation of the A* algorithm in an environment of moving obstacles.
Abstract: Two problems for path planning of a mobile robot are considered. The first problem is to find a shortest-time, collision-free path for the robot in the presence of stationary obstacles in two dimensions. The second problem is to determine a collision-free path (greedy in time) for a mobile robot in an environment of moving obstacles. The environment is modeled in space-time and the collision-free path is found by a variation of the A* algorithm. >
152 citations
TL;DR: The present research on mobile robotics addresses the problems which are mainly on path planning algorithm and optimization in static as well as dynamic environments with a focus on meta-heuristic methods.
152 citations