In this paper, a novel robotic system called modular transformer (M-TRAN) is proposed. M-TRAN is a distributed, self-reconfigurable system composed of homogeneous robotic modules. The system can change its configuration by changing each module's position and connection. Each module is equipped with an onboard microprocessor, actuators, intermodule communication/power transmission devices and intermodule connection mechanisms. The special design of M-TRAN module realizes both reliable and quick self-reconfiguration and versatile robotic motion. For instance, M-TRAN is able to metamorphose into robotic configurations such as a legged machine and hereby generate coordinated walking motion without any human intervention. An actual system with ten modules was built and basic operations of self-reconfiguration and motion generation were examined through experiments. A series of software programs has also been developed to drive M-TRAN hardware, including a simulator of M-TRAN kinematics, a user interface to design appropriate configurations and motion sequences for given tasks, and an automatic motion planner for a regular cluster of M-TRAN modules. These software programs are integrated into the M-TRAN system supervised by a host computer. Several demonstrations have proven its capability as a self-reconfigurable robot.

This paper presents a design method and experiments for whole-body locomotion by a modular robot. There are two types of locomotion for modular robots: a repeating self-reconfiguration and whole-body motion such as walking or crawling. For whole-body locomotion, designing a control method is more difficult than for ordinary robots because a modular robotic system can form various configurations, each of which has many degrees of freedom. This study proposes a unified framework for automatically designing an efficient locomotion controller suitable for any module configuration. The method utilizes neural oscillators (central pattern generators, CPGs), each of which works as a distributed joint controller of each module, and a genetic algorithm to optimize the CPG network. We verified the method by software simulations and hardware experiments, in which our modular robotic system, named M-TRAN II, performed stable and effective locomotion in various configurations.

Previous work on self-reconfiguring modular robots has concentrated primarily on designing hardware and developing reconfiguration algorithms tied to specific hardware systems. In this paper, we introduce a generic model for lattice-based self-reconfigurable robots and present several generic locomotion algorithms that use this model. The algorithms presented here are inspired by cellular automata, using geometric rules to control module actions. The actuation model used is a general one, assuming only that modules can generally move over the surface of a group of modules. These algorithms can then be instantiated onto a variety of particular systems. Correctness proofs of many of the rule sets are also given for the generic geometry; this analysis can carry over to the instantiated algorithms to provide different systems with correct locomotion algorithms. We also present techniques for automated analysis that can be used for algorithms that are too complex to be easily analyzed by hand.