scholarly journals Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion

2019 ◽  
Author(s):  
Mohamad T. Shahab ◽  
Moustafa Elshafei
Keyword(s):  
Distributed Optimization ◽  
Energy Criterion ◽  
Robot Motion ◽  
Multi Robot

scholarly journals Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

2021 ◽  
Vol 6 (2) ◽  
pp. 2256-2263
Author(s):  
Hai Zhu ◽  
Francisco Martinez Claramunt ◽  
Bruno Brito ◽  
Javier Alonso-Mora
Keyword(s):  
Motion Planning ◽  
Dynamic Environments ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Robot

scholarly journals Combining Multi-Robot Motion Planning and Goal Allocation using Roadmaps

2021 ◽  
Author(s):  
Joao Salvado ◽  
Masoumeh Mansouri ◽  
Federico Pecora
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Robot

scholarly journals Effective metrics for multi-robot motion-planning

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1741-1759 ◽  
Author(s):  
Aviel Atias ◽  
Kiril Solovey ◽  
Oren Salzman ◽  
Dan Halperin
Keyword(s):  
Motion Planning ◽  
Success Rate ◽  
Shape Matching ◽  
Equivalence Classes ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
The Novel ◽  
Multi Robot

We study the effectiveness of metrics for multi-robot motion-planning (MRMP) when using rapidly-exploring random tree (RRT)-style sampling-based planners. These metrics play the crucial role of determining the nearest neighbors of configurations and in that they regulate the connectivity of the underlying roadmaps produced by the planners and other properties such as the quality of solution paths. After screening over a dozen different metrics we focus on the five most promising ones: two more traditional metrics, and three novel ones, which we propose here, adapted from the domain of shape-matching. In addition to the novel multi-robot metrics, a central contribution of this work are tools to analyze and predict the effectiveness of metrics in the MRMP context. We identify a suite of possible substructures in the configuration space, for which it is fairly easy: (i) to define a so-called natural distance that allows us to predict the performance of a metric, which is done by comparing the distribution of its values for sampled pairs of configurations to the distribution induced by the natural distance; and (ii) to define equivalence classes of configurations and test how well a metric covers the different classes. We provide experiments that attest to the ability of our tools to predict the effectiveness of metrics: those metrics that qualify in the analysis yield higher success rate of the planner with fewer vertices in the roadmap. We also show how combining several metrics together may lead to better results (success rate and size of roadmap) than using a single metric.

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