Abstract
This Thesis proposes a novel approach for optimizing the assembly zone of modular robots in various obstacle environments, which is crucial for applications such as logistics, firefighting, and exploration. Leveraging multi-objective Genetic Algorithm (GA), the method minimizes total travel distance and individual distance disparities. Utilizing the A* algorithm for path planning ensures efficient navigation. Additionally, a generic kinematic model enabling holonomic locomotion with any reconfiguration and a new modular robot design are introduced. Hardware experiments have been conducted to validate the kinematic model’s applicability for holonomic navigation across different robot configurations.
Simulations and physical experiments demonstrated the effectiveness of the proposed method in determining assembly zones, with GA outperforming multi-objective pattern search and random selection in terms of total distance and individual distances travelled by the robots.