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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21 May 2001
TL;DR: The nonlinear velocity obstacle is introduced, which takes into account the shape, velocity and path curvature of the moving obstacle, which elevates the planning strategy to a second order method, compared to the first order avoidance using the linear v-obstacle.
Abstract: This paper generalizes the concept of velocity obstacles given by Fiorini et al. (1998) to obstacles moving along arbitrary trajectories. We introduce the nonlinear velocity obstacle, which takes into account the shape, velocity and path curvature of the moving obstacle. The nonlinear v-obstacle allows selecting a single avoidance maneuver (if one exists) that avoids any number of obstacles moving on any known trajectories. For unknown trajectories, the nonlinear v-obstacles can be used to generate local avoidance maneuvers based on the current velocity and path curvature of the moving obstacle. This elevates the planning strategy to a second order method, compared to the first order avoidance using the linear v-obstacle, and zero order avoidance using only position information. Analytic expressions for the nonlinear v-obstacle are derived for general trajectories in the plane. The nonlinear v-obstacles are demonstrated in a complex traffic example.
170 citations
01 Jan 2014
TL;DR: Modifications and improvements of A star algorithm focused primarily on computational time and the path optimality are introduced and it is possible to choose path planning method suitable for individual scenario.
Abstract: This article deals with path planning of a mobile robot based on a grid map. Essential assumption for path planning is a mobile robot with functional and reliable reactive navigation and SLAM. Therefore, such issues are not addressed in this article. The main body of the article introduces several modifications (Basic Theta*, Phi*) and improvements (RSR, JPS) of A star algorithm. These modifications are focused primarily on computational time and the path optimality. Individual modifications were evaluated in several scenarios, which varied in the complexity of environment. On the basis of these evaluations, it is possible to choose path planning method suitable for individual scenario. © 2014 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the Modelling of Mechanical and Mechatronic Systems MMaMS 2014. Keywords: path planning, A* algorithm, Basic Theta*, Phi*, Jump Point Search Nomenclature SLAM Simultaneous Localization and Mapping A* A star RSR Rectangular Symmetry Reduction JPS Jump Point Search
170 citations
TL;DR: In this approach, the robot makes use of depth information delivered by the vision system to accurately model its surrounding environment through image processing techniques and generates a collision-free optimal path linking an initial configuration of the mobile robot to a final configuration (Target).
169 citations
03 May 2010
TL;DR: Sampling-based Motion and Symbolic Action Planner leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions.
Abstract: To compute collision-free and dynamically-feasibile trajectories that satisfy high-level specifications given in a planning-domain definition language, this paper proposes to combine sampling-based motion planning with symbolic action planning. The proposed approach, Sampling-based Motion and Symbolic Action Planner (SMAP), leverages from sampling-based motion planning the underlying idea of searching for a solution trajectory by selectively sampling and exploring the continuous space of collision-free and dynamically-feasible motions. Drawing from AI, SMAP uses symbolic action planning to identify actions and regions of the continuous space that sampling-based motion planning can further explore to significantly advance the search. The planning layers interact with each-other through estimates on the utility of each action, which are computed based on information gathered during the search. Simulation experiments with dynamical models of vehicles carrying out tasks given by high-level STRIPS specifications provide promising initial validation, showing that SMAP efficiently solves challenging problems.
169 citations
TL;DR: In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly.
169 citations