Task-Based Design Optimization of Modular Robots

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Task-Based Design Optimization of Modular Robots Reem Alattas University of Bridgeport, Department of Computer Science & Engineering Abstract This poster presents a novel system for task-based modular robotic design optimization. The proposed system uses Assemble Incidence Matrix (AIM) to represent the modular roboic configuration. Genetic Algorithm (GA) is used to solve the optimization problem by selecting the fittest robots. The fitness of every modular robotic design is measured in simulation. Then, the resulting solutions are implemented physically using Dtto modular robotic kit. The feasibility of this approach is demonstrated by several examples. System Architecture Dtto Modular Robot Dtto module is composed of double-cube modules inspired by M-TRAN III modular design. Every module comprises two identical semi-cylindrical boxes connected by a link. A walker built using 4 Dtto modules is illustrated in simulation and built physically. Genetic Algorithm Conclusion This poster presented a framework for optimizing task-based design of modular robotic systems. This system was applied to Dtto modular robots. Each robot evolved in simulation and the fittest robot was built physically. Each robotic configuration is represented using an Assembly Incidence Matrix (AIM) to build a foundation for optimization. Genetic Algorithm (GA) is used to evolve the robotic structures using crossover, mutation, and selection operators. The robotic structures evolve in simulation to simplify fitness measurement. The resulting robot structure can be implemented in reality. References [1] Dauphin, L., Petersen, H., Adjih, C. and Baccelli, E., 2017, February. Low-Cost Robots in the Internet of Things: Hardware, Software & Communication Aspects. In NextMote Workshop- Next Generation Platforms for the Cyber-Physical Internet. [2] Alattas, R.J., Patel, S. and Sobh, T.M., Comprehensive Survey of Evolutionary Morphological Soft Robotic Systems. [3] Farritor, S. and Dubowsky, S., 2001. On modular design of field robotic systems. Autonomous Robots, 10(1), pp.57-65.