2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016
DOI: 10.1109/iros.2016.7759489
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Multi-robot path planning for budgeted active perception with self-organising maps

Abstract: Abstract-We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward,… Show more

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Cited by 34 publications
(19 citation statements)
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“…Efficient non-myopic solutions can be designed by exploiting problem-specific characteristics [4,6]. But in general, the problem is a POMDP, which is notoriously difficult to solve.…”
Section: Related Workmentioning
confidence: 99%
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“…Efficient non-myopic solutions can be designed by exploiting problem-specific characteristics [4,6]. But in general, the problem is a POMDP, which is notoriously difficult to solve.…”
Section: Related Workmentioning
confidence: 99%
“…The problem is motivated by tasks where a team of Dubins robots maximally observes a set of features of interest in an environment, given a travel budget [4]. Each feature can be viewed from multiple viewpoints and each viewpoint may be in observation range of multiple features.…”
Section: Experiments: Generalised Team Orienteeringmentioning
confidence: 99%
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“…A preliminary version of this paper appeared as Best et al (2016b). This extended version additionally contains: expanded algorithmic details; a generalised formulation for fixed start and/or end locations (similar to ); extended theoretical analysis of the runtime complexity and convergence; empirical analysis of the algorithm's convergence and anytime properties; empirical validation of an example observation model definition; a comparison to Dec-MCTS; a formulation for online scenarios; and extensive simulation experiments that demonstrate the feasibility of the approach for online replanning scenarios.…”
Section: Introductionmentioning
confidence: 99%