Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles

Introduction:In control over a group of interacting smart electromechanical systems (SEMS), situations may arise when the operator’s instructions and/or the automatic control system at a higher level contradict the internal state of the controlled SEMS and/or the environment of choice. Such situations can be prevented by algorithms which check the fulfillment of conditions for the admissibility of movements. These algorithms can be based on modeling the SEMS behavior using logical-probabilistic or logicallinguistic descriptions of situations, and on non-scalar quality criteria when making decisions.Purpose:The development of algorithms for safe control over robots based on SEMS modules with phase constraints, under incomplete certainty of the environment.Results:Algorithms have been developed for safe control over three robots, using a mathematical description of situational control over a group of SEMS and the methodology of organizing the situational control over a group of mobile SEMS. The algorithms move the robots from certain current positions to specified terminal positions, avoiding their collisions with each other. In order to avoid collisions, the decision-making system in a robot’s central nervous system uses robot’s priorities based on the distance between the robots. An approach has been proposed to overcome uncertainty on the way (trajectory) of the robots. Uncertainties in the form of logical-probabilistic and logical-linguistic type constraints are considered. It is shown that these restrictions can be translated into a logical-interval form. This allows you to use standard mathematical programming procedures when searching for the optimal solution.Practical relevance:The obtained algorithms can be used for decision-making in the central nervous system and when controlling robots.

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Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles

Cited by 3 publications

References 16 publications

DCEGen: Dense Clutter Environment Generation Tool for Autonomous 3D Exploration and Coverage Algorithms Testing

DCEGen: Dense Clutter Environment Generation Tool for Autonomous 3D Exploration and Coverage Algorithms Testing

Geometry-Based Robust Control of a Multi-Agent System of Quadrotors

Safe control of SEMS in group interaction

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