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Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithm.

Ravikiran Pasumarthi1,2, S M Bhagya P Samarakoon3, Mohan Rajesh Elara1

  • 1Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore.

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This study introduces a new method for optimizing modular robot assembly zones in complex environments. A multi-objective Genetic Algorithm minimizes robot travel distance, improving efficiency for tasks in exploration and outer space.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Reconfigurable modular robots are crucial for high-throughput tasks in exploration, logistics, and space.
  • Optimizing robot assembly positions in obstacle-rich environments presents significant challenges.
  • Minimizing travel distance and disparities is key for efficient multi-robot coordination.

Purpose of the Study:

  • To propose a novel approach for optimizing the assembly zone of modular robots.
  • To minimize total travel distance and individual distance disparities among robots.
  • To develop a generic kinematic model for holonomic locomotion and a new modular robot design.

Main Methods:

  • Utilized a multi-objective Genetic Algorithm (GA) for optimization.
  • Employed the A* algorithm for efficient path planning.
  • Introduced a generic kinematic model for holonomic locomotion and a novel modular robot design.

Main Results:

  • The GA effectively minimized total travel distance and individual distance disparities.
  • The A* algorithm ensured efficient navigation in heterogeneous obstacle environments.
  • Hardware experiments validated the kinematic model for holonomic navigation across configurations.

Conclusions:

  • The proposed method successfully determines optimal assembly zones for modular robots.
  • The GA-based approach outperformed pattern search and random selection in minimizing travel distances.
  • The developed kinematic model and robot design are effective for holonomic navigation and reconfiguration.