Optimizing Assembly Zones for Modular Robots
Author Information
Author(s): Pasumarthi Ravikiran, Samarakoon S. M. Bhagya, Elara Mohan Rajesh, Sheu Bing J.
Primary Institution: Singapore University of Technology and Design
Hypothesis
Can a multi-objective genetic algorithm effectively determine the optimum assembly zone for modular robots in obstacle-rich environments?
Conclusion
The proposed method successfully identifies assembly zones that minimize total travel distance and equalizes individual distances for modular robots.
Supporting Evidence
- The method outperformed multi-objective pattern search and random selection in terms of total distance and individual distances traveled by the robots.
- Hardware experiments validated the kinematic model's applicability for holonomic navigation across different robot configurations.
- Simulations demonstrated the effectiveness of the proposed method in determining assembly zones.
Takeaway
This study shows how robots can work together better by finding the best place to meet up, making it easier for them to do their jobs.
Methodology
The study utilized a multi-objective genetic algorithm to optimize assembly zones for modular robots, validated through simulations and hardware experiments.
Limitations
The current method only considers static obstacles and does not account for dynamic obstacles in the environment.
Digital Object Identifier (DOI)
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