Self-Organizing Map for the Multi-Goal Path Planning with Polygonal Goals

Faigl, Jan; Přeučil, Libor

doi:10.1007/978-3-642-21735-7_11

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…Suitable sensing locations can be found by a sensor placement algorithm, for example, [ 29 – 32 ]. Then, a group of cooperating mobile robots can be used to decrease the required time to inspect the environment and thus the inspection task can be formulated as the MTSP [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

“…Such a path can be refined in a similar manner like the aforementioned node–city path. For a further details, see [ 20 , 21 ].…”

Section: Use Approachesmentioning

confidence: 99%

“…The addressed problem is considered as a variant of the Multiple Traveling Salesman Problem (MTSP) with minmax objective [ 19 ] in which we aim to minimize the longest tour. This variant has a suitable objective function for motivational inspection planning or search and rescue scenarios, where it is desired to search the given environment as quickly as possible, and the total mission time corresponds to the length of the longest path a robot has to travel [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…This presented work reports on an extension of the SOM for the MTSP with minmax objective proposed in [ 16 ] to a more general approach for the multirobot multigoal path planning problem to visit a given set of locations in the polygonal domain 𝒲 . The proposed approach is based on our previous work on approximation of the shortest path in 𝒲 for SOM-based solution of routing problems [ 18 , 20 , 21 ]. Therefore, we focus on an evaluation of the proposed extensions and an alternative inexpensive procedure for the competitive rule for the SOM-based MTSP-Minmax.…”

Section: Introductionmentioning

confidence: 99%

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An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

Faigl

2016

Computational Intelligence and Neuroscience

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In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

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Section: Related Workmentioning

confidence: 99%

“…Such a path can be refined in a similar manner like the aforementioned node–city path. For a further details, see [ 20 , 21 ].…”

Section: Use Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

Faigl

2016

Computational Intelligence and Neuroscience

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“…None of the studies in this field has ever suggested to share the processing resources of agents/robots to increase the processing efficiency of each individual in the system. However, there are several robotic applications which require high processing resources for the individual robots in a multirobot system, namely "mapping" [35], [36], "robotics vision" [37], "path planning" [38] and "large-scale signal processing" [39]. For example, in multi-robot exploration and mapping, usually robots share their local topological maps and each one of them merges them to generate a global map of the whole environment.…”

Section: Introductionmentioning

confidence: 99%

Robotic clusters: Multi-robot systems as computer clusters

Marjovi

Choobdar

Marques

2012

Robotics and Autonomous Systems

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Self-Organizing Map for the Prize-Collecting Traveling Salesman Problem

Faigl

Hollinger

2014

Advances in Intelligent Systems and Computing

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In this paper, we propose novel adaptation rules for the selforganizing map to solve the prize-collecting traveling salesman problem (PC-TSP). The goal of the PC-TSP is to find a cost-efficient tour to collect prizes by visiting a subset of a given set of locations. In contrast with the classical traveling salesman problem, where all given locations must be visited, locations in the PC-TSP may be skipped at the cost of some additional penalty. Using the self-organizing map, locations for the final solution may be selected during network adaptation, and locations where visitation would be more expensive than their penalty can be avoided. We have applied the proposed self-organizing map learning procedure to autonomous data collection problems, where the proposed approach provides results competitive with an existing combinatorial solver.

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Self-Organizing Map for the Multi-Goal Path Planning with Polygonal Goals

Cited by 10 publications

References 10 publications

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

Robotic clusters: Multi-robot systems as computer clusters

Self-Organizing Map for the Prize-Collecting Traveling Salesman Problem

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