Application of Soft Computing Techniques to Rescue Operation Planning

Kubalík, Jiří; Kléma, Jǐŕı; Kulich, Miroslav

doi:10.1007/978-3-540-24844-6_139

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Soft-computing techniques have been also applied to the MTSP-Minmax, such as ant colony optimization [ 38 ], genetic algorithms [ 39 ], and also SOM in [ 16 ]. Particular soft-computing approaches for Euclidean instances of the MTSP-Minmax have been evaluated in [ 40 ] and our early results on robotic problems with obstacles in [ 41 ]. Therefore, in the presented work, we are focused on the evaluation of the SOM-based solution of the multirobot multigoal path planning and its comparison with the GENIUS algorithm [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

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%

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

Faigl

2016

Computational Intelligence and Neuroscience

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A Clustering Market-Based Approach for Multi-robot Emergency Response Applications

Trigui

Koubâa

Cheikhrouhou

et al. 2016

2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)

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Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

Giardini

Kalmár‐Nagy

2011

Mathematical Problems in Engineering

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The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem TSP : given a set of n possible goals/targets, the optimal strategy is sought that connects k ≤ n goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

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Application of Soft Computing Techniques to Rescue Operation Planning

Cited by 3 publications

References 2 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

A Clustering Market-Based Approach for Multi-robot Emergency Response Applications

Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

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