Reconfigurable modular robots have the ability to use different gaits and configurations to perform various tasks. A rolling gait is the fastest currently implemented gait available for traversal over level ground and shows dramatic improvements in efficiency. In this work, we analyze and implement a sensor-based feedback controller to achieve dynamic rolling for a loop robot. The robot senses its position relative to the ground and changes its shape as it rolls. This shape is such that its center of gravity is maintained to be in front of its contact point with the ground, so in effect the robot is continuously falling and thus accelerates forward. Using simulation and experimental results, we show how the desired shape can be varied to achieve higher terminal velocities. The highest velocity achieved in this work is 26 module lengths per second (1.6 m/s) which is believed to be the fastest gait yet implemented for an untethered modular robot. One of the major findings is that more elongated shapes achieve higher terminal velocities than rounder shapes. We demonstrate that this trend holds going up inclines as well as down. We show that rounder shapes have lower specific resistance and are thus more energy efficient. The control scheme is scalable to an arbitrary number of modules, shown here using eight to 14 modules.
1 March 2008 in The International Journal of Robotics Research
Recognizing useful modular robot configurations composed of hundreds of modules is a significant challenge. Matching a new modular robot configuration to a library of known configurations is essential in identifying and applying control schemes. We present three different algorithms to address the problem of (a) matching and (b) mapping new robot configurations onto a library of known configurations. The first method solves the problem using graph isomorphisms and can identify configurations that share the same underlying graph structure, but have different port connections amongst the modules. The second approach compares graph spectra of configuration matrices to find a permutation matrix that maps a given configuration to a known one. The third algorithm exploits the unique structure of the problem for the particular robots used in our research to achieve impressive gains in performance and speed over existing techniques, especially for larger configurations. With these three algorithms, this paper presents novel solutions to the problem of configuration recognition and sheds light on theoretical and practical issues for long-term advances in this important area of modular robotics. Results and examples are provided to compare the performance of the three algorithms and discuss their relative advantages.
Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on real modular robots and some example reconfiguration tasks are provided.