SamplingBased Motion Planning Algorithms for Replanning and Spatial Load Balancing
Abstract
The common theme of this dissertation is samplingbased motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two samplingbased motion planners: the asymptotically optimal rapidlyexploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multiagent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed GrandparentConnection (GP) or FocusedRefinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multiagent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel SamplingBased Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goalmore »
 Authors:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1400115
 Report Number(s):
 LAUR1729319
 DOE Contract Number:
 AC5206NA25396
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Motion planning; Spatial load balancing; multiagent
Citation Formats
Boardman, Beth Leigh. SamplingBased Motion Planning Algorithms for Replanning and Spatial Load Balancing. United States: N. p., 2017.
Web. doi:10.2172/1400115.
Boardman, Beth Leigh. SamplingBased Motion Planning Algorithms for Replanning and Spatial Load Balancing. United States. doi:10.2172/1400115.
Boardman, Beth Leigh. 2017.
"SamplingBased Motion Planning Algorithms for Replanning and Spatial Load Balancing". United States.
doi:10.2172/1400115. https://www.osti.gov/servlets/purl/1400115.
@article{osti_1400115,
title = {SamplingBased Motion Planning Algorithms for Replanning and Spatial Load Balancing},
author = {Boardman, Beth Leigh},
abstractNote = {The common theme of this dissertation is samplingbased motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two samplingbased motion planners: the asymptotically optimal rapidlyexploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multiagent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed GrandparentConnection (GP) or FocusedRefinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multiagent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel SamplingBased Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the costtocome at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graphbased Spatial Load Balancing (GSLB) algorithm which fairly divides a nonconvex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.},
doi = {10.2172/1400115},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month =
}

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