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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Sioud, Riheb Bamoumen, Meryem Hamani, Nadia Univ Picardie Jules Verne EIJV LTI F-02100 St Quentin en Yvelines France

ISBN: (纸本)9783031807749;9783031807756

Efficient task assignment to multiple robots becomes a core challenge for smart warehouse operations. This work falls within the context of automation and task management of modern intelligent logistics. A new mathematical model for solving the open path multi-depot asymmetric traveling salesman problem is developed in this paper, aiming at finding an optimized task allocation scheme for the robots in an intelligent warehouse. Our model aims at determining optimal task sets for each robot while minimizing operational costs and in order to enhance productivity without requiring each robot to return to its starting depot. By using CPLEX, we solved the small and medium sized scenarios and demonstrated superior performance compared with other models in computation time and solution quality.

关键词： Smart warehouse Task assignment multi robot Optimization Open-path multi depot asymmetric traveling salesman problem (OP-MATSP) Integer linear programming (ILP)

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multi robot distance based formation using Parallel Genetic Algorithm

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Lopez-Gonzalez, A. Meda Campana, J. A. Hernandez Martinez, E. G. Paniagua Contro, P. Inst Politecn Nacl SEPI ESIME Zacatenco Ave IPN S-N Cdmx 07738 DF Mexico Univ Iberoamer DICAT Prolongac Paseo Reforma 880 Cdmx 01219 Mexico

In this paper an alternative method to achieve distance based formation is presented. The method uses Genetic Algorithms to find a suitable solution based on angle and distance, and an appropriate constant velocity to avoid collisions. The designed algorithm is extended to a parallel scheme to improve its performance and achieve Artificial Distributed Intelligence, in which the robots share, through solution migration, the best ways to converge to desired distances while avoiding collisions, finally reaching consensus on the solution. The algorithm is tested using simulations and real robots experiments. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Parallel Genetic Algorithm multi robot Consensus Formation Distributed Artificial Intelligence

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A survey on multi-robot coverage path planning for model reconstruction and mapping

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SN APPLIED SCIENCES 2019年第8期1卷 1-24页

作者： Almadhoun, Randa Taha, Tarek Seneviratne, Lakmal Zweiri, Yahya Khalifa Univ Sci & Technol Abu Dhabi U Arab Emirates Algorythmas Autonomous Aerial Lab Abu Dhabi U Arab Emirates Kingston Univ London Fac Sci Engn & Comp London SW15 3DW England

There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches;coverage planning strategies;coordination and decision-making processes;communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives.

关键词： Coverage path planning Viewpoint sampling multi robot Model reconstruction

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Decentralised Submodular multi-robot Task Allocation

Decentralised Submodular Multi-Robot Task Allocation

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Segui-Gasco, Pau Shin, Hyo-Sang Tsourdos, Antonios Seguí, V. J. Cranfield Univ Sch Aerosp Transport & Mfg Coll Rd Cranfield MK43 0AL Beds England Univ Politecn Valencia Dept Mech Engn & Mat Sci Escuela Politecn Super Alcoy Alcoy 03801 Spain

ISBN: (纸本)9781479999941

In this paper we present a decentralised algorithm to solve the Task Allocation problem. Given a set of tasks and a set of robots each with its own utility function, this problem concerns the non-overlapping allocation of tasks to agents maximising the sum of the robot's utility functions. Our algorithm provides a constant factor approximation of (1 - 1/e approximate to 63%) for positive-valued monotone submodular utility functions, and of (1/e approximate to 37%) for positive-valued non-monotone submodular utility functions. In our algorithm, robots only rely on local utility function and neighbour to neighbour communications to find the solution. The algorithm proceeds in two steps. Initially, the robots use Maximum Consensus-based algorithm to find a relaxed solution of the problem using the multilinear extension of their utility functions. Finally, they follow a randomised rounding procedure to exchange valuations on sets of tasks in order to round the relaxed solution. To conclude, we provide experimental evidence of our claims for both monotone and non-monotone submodular functions.

关键词： Decentralised Task Allocation multi robot Task Allocation Distributed robotics Submodular Welfare

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An Efficient Global Optimization Approach to multi robot Path Exploration Problem Using Hybrid Genetic Algorithm

An Efficient Global Optimization Approach to Multi Robot Pat...

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4th International Conference on Information and Automation for Sustainability

作者： Senthilkumar, K. S. Bharadwaj, K. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (纸本)9781424428991

This paper presents a novel scheme for Global Path Exploration to multi robots Environment using hybrid implementation of Evolutionary heuristic. This scheme is used to find an optimal path for each Mobile robot to move in a static environment expressed by a weighted graph with nodes and links. The interesting part of this scheme is that the chromosome structure is designed to cluster the Landmarks (nodes) in the environment. The rendezvous point for robots to meet at last is selected by using making centroid technique. We used a fixed length chromosome. Each robot has a starting point and a Rendezvous point under the assumption that the robot passes each point in the cluster only once. Experimental results are presented to illustrate the performance of the proposed scheme. The scheme was tested on a set of different problems with encouraging results.

关键词： multi robot path exploration Genetic algorithm Rendezvous point

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Development of Agent System for multi-robot Search 11

Development of Agent System for Multi-robot Search

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Omiya, Masashi Takimoto, Munehiro Kambayashi, Yasushi Nippon Inst Technol Dept Comp & Informat Engn 4-1 Gakuendai Miyashiro Saitama 3458510 Japan Tokyo Univ Sci Dept Informat Sci 2641 Yamazaki Noda Chiba 2788510 Japan

ISBN: (纸本)9789897583506

In this paper, we propose an agent system that controls multiple mobile robots. We describe the agent system as well as an example multi-robot system as an application of this agent system. The aim of the multi-robot system is providing efficient searches for a given target. Therefore, it is necessary to mutually communicate and cooperate among robots. Taking account of the delay of communication, cost, fault tolerance, and robustness, we have chosen mobile agent system for the information transmission method. We have also chosen ad-hoc method for the communication mode, where each robot communicates directly without going through the Internet. Even though agent systems are often implemented using Java language or Python language, we have chosen C++ language for developing our agent system with the hope for gaining performance efficiency. We have modularized each function and it does not depend on other modules. We have shown the feasibility of our multi-agent system by applying the system to a multi-robot system that implement particle swarm optimization.

关键词： Mobile Agent Ad-hoc Network multi robot Particle Swarm Optimization

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Evolving Neural Networks for multi robot Odor Search

Evolving Neural Networks for Multi Robot Odor Search

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IEEE International Conference on Autonomous robot Systems and Competitions (ICARSC)

作者： Macedo, Joao Marques, Lino Costa, Ernesto Univ Coimbra Inst Syst & Robot Coimbra Portugal Univ Coimbra CISUC Coimbra Portugal

ISBN: (纸本)9781509022557

The tasks of odor detection, plume tracking and odor source localization constitute an important, yet complex, real world problem. One possible solution for them is based on the use of a group of mobile robots whose controllers have to be defined. Artificial Neural Networks (ANN) have already been used as controllers, but the task of hand defining their topology and parameters can be very challenging and time consuming. In this paper, we propose an approach to evolve, rather than design, ANN-based controllers. Our approach relies on Genetic Programming (GP), a family of stochastic search procedures loosely inspired by the biological principles of Natural Selection and Genetics. We compare our approach with a classic one, inspired by the chemotaxis behavior of the E. coli bacteria. Our results show that this approach is able to outperform the chemotaxis in the experiments performed.

关键词： Genetic Programming Evolutionary robotics Artificial Neural Networks multi robot Odor Search

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Navigation in multi robot System using Cooperative Learning : A Survey

Navigation in Multi Robot System using Cooperative Learning ...

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International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT)

作者： Singh, Priyanka Tiwari, Ritu Bhattacharya, Mahua Indian Inst Informat Technol Management Robot Intelligent Syst Design Lab Gwalior India

ISBN: (纸本)9781509000821

multi-robot system became an important research area in the robotics and Artificial Intelligence. In the presence of obstacles, uncertainty and incomplete information multi-robot system is used to accomplish the task in more efficient way as compared to single robot system. Navigation of robots in its surrounding is essential to avoid unacceptable situation such as avoiding obstacles, trajectory planning. Cooperative behavior of multi-robots provides efficient way to avoid collision. It is a matter of concern that how a group of mobile robots should be controlled so that they can move in a cohesive way toward a single direction. For the problem of cooperation among robots, flocking strategy is a good solution. To learn cooperation among robots, various machine learning strategies have been developed. One of the machine learning techniques is called reinforcement learning. It is a very challenging issue in the area of robotics and artificial intelligence. Combining cooperative and learning strategy collision avoidance can be improved. Survey of multi-agent reinforcement learning and flocking control is presented in this paper.

关键词： Navigation multi robot Reinforcement Learning Flocking Strategy Collision Avoidance Predator

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multi robot Flexible Welding Control for Complex Environment Tasks 34

Multi Robot Flexible Welding Control for Complex Environment...

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Shengzhong Liu, Shuguang Zhang, Long Song, Tianle Wang, Zijian SINOPEC Zhongyuan Oilfield Serv Co Ltd Puyang 457001 Peoples R China Huangshan Univ Sch Mech & Elect Eng Huangshan 245041 Peoples R China Anhui Dorje Elect Co Huangshan 245200 Peoples R China

ISBN: (纸本)9781665478960

The existing master-slave relationship among multiple machines is generally fixed. In the process of performing tasks, the master-slave relationship between machines cannot be switched online, which limits the adaptability of multiple robots to complete complex tasks. Therefore, master-slave coordinated control has become an important research direction of flexible welding. Aiming at the application background of robot flexible welding of complex structural parts, a multi robot master-slave coordinated motion control system is designed, in which the centralized control architecture, periodic synchronous position mode and general multi robot master-slave coordinated motion control algorithm are adopted to realize the coordinated motion control of slave robots. The experimental results show that the multi robot master-slave coordinated motion control algorithm proposed in this paper is universal, the designed master-slave coordinated motion control process is reasonable and effective, the developed master-slave coordinated motion programming instruction set is convenient and efficient, and the on-line switching function of multi robot master-slave state is realized, which provides a technical basis for further improving the flexibility of robot welding system for complex structural parts.

关键词： multi robot flexible welding complex tasks

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multi robot Path Planning and Path Coordination Using Genetic Algorithms 17

Multi Robot Path Planning and Path Coordination Using Geneti...

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ACM Southeast Regional Conference

作者： Muthiah, Muthumeena Saad, Ashraf Armstrong State Univ Savannah GA 31419 USA

ISBN: (纸本)9781450350242

Planning optimal paths for multiple robots is computationally expensive. In this research, we provide a Genetic Algorithm implementation for multi robot path planning. Path planning for multiple mobile robots must devise a collision-free path for each robot. The paper presents a Genetic Algorithm multi robot path planner that we developed to provide a solution to the problem. Experimental results using m3pi robots confirm the usefulness of the proposed solution in a variety of scenarios such as multi robot navigation as well as scenarios that require coordination of multiple robots to achieve a common goal such as pushing a box or trapping a prey.

关键词： multi robot Genetic Algorithms Path Planning Path Coordination Collision Avoidance m3pi robots Communication Collaborative tasks

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