Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Artificial potential field based path planning for mobile robots using a virtual obstacle concept

Abstract: The artificial potential field (APF) based path planning methods have a local minimum problem, which can trap mobile robots before reaching it's goal. In this study, a new concept using a virtual obstacle is proposed to escape local minimums occurred in local path planning. A virtual obstacle is located around local minimums to repel a mobile robot from local minimums. A sensor based discrete modeling method is also proposed for modeling of the mobile robot with range sensors. This modeling method is adaptable for a real-time path planning because it provides lower complexity.

Finding an unpredictable target in a workspace with obstacles

Abstract: This paper introduces a visibility-based motion planning problem in which the task is to coordinate the motions of one or more robots that have omnidirectional vision sensors, to eventually "see" a target that is unpredictable, has unknown initial position, and is capable of moving arbitrarily feast. A visibility region is associated with each robot, and the goal is to guarantee that the target will ultimately lie in at least one visibility region. Both a formal characterization of the general problem and several interesting problem instances are presented. A complete algorithm for computing the motion strategy of the robots is also presented, and is based on searching a finite cell complex that is constructed on the basis of critical information changes. A few computed solution strategies are shown. Several bounds on the minimum number of needed robots are also discussed.

Current Issues in Sampling-Based Motion Planning

Abstract: In this paper, we discuss the field of sampling-based motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how sampling-based motion planning has developed. We then discuss a variety of important issues for sampling-based motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based motion planning.

Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning

Abstract: To efficiently solve challenges related to motion-planning problems with dynamics, this paper proposes treating motion planning not just as a search problem in a continuous space but as a search problem in a hybrid space consisting of discrete and continuous components. A multilayered framework is presented which combines discrete search and sampling-based motion planning. This framework is called synergistic combination of layers of planning ( SyCLoP) hereafter. Discrete search uses a workspace decomposition to compute leads, i.e., sequences of regions in the neighborhood that guide sampling-based motion planning during the state-space exploration. In return, information gathered by motion planning, such as progress made, is fed back to the discrete search. This combination allows SyCLoP to identify new directions to lead the exploration toward the goal, making it possible to efficiently find solutions, even when other planners get stuck. Simulation experiments with dynamical models of ground and flying vehicles demonstrate that the combination of discrete search and motion planning in SyCLoP offers significant advantages. In fact, speedups of up to two orders of magnitude were obtained for all the sampling-based motion planners used as the continuous layer of SyCLoP.

Path Planning for Virtual Human Motion Using Improved A* Star Algorithm

Abstract: Calculating and generating optimal motion path automatically is one of the key issues in virtual human motion path planning. To solve the point, the improved A* algorithm has been analyzed and realized in this paper, we modified the traditional A* algorithm by weighted processing of evaluation function, which made the searching steps reduced from 200 to 80 and searching time reduced from 4.359s to 2.823s in the feasible path planning. The artificial searching marker, which can escape from the barrier trap effectively and quickly, is also introduced to avoid searching the invalid region repeatedly, making the algorithm more effective and accurate in finding the feasible path in unknown environments. We solve the issue of virtual human's obstacle avoidance and navigation through optimizing the feasible path to get the shortest path.