Morpho: Self-deformable Modular Robot Inspired by Cellular Structure [IROS 08]

 

We present a modular robot design inspired by the creation of complex structures and functions in biology via deformation. Our design is based on the Tensegrity model of cellular structure, where active filaments within the cell contract and expand to control individual cell shape, and sheets of such cells undergo large-scale shape change through the cooperative action of connected cells. Such deformations play a role in many processes, e.g. early embryo shape change and lamprey locomotion. Modular robotic systems that replicate the basic deformable multicellular structure have the potential to quickly generate large-scale shape change and create dynamic shapes to achieve different global functions.

Based on this principle, our design includes four different modular components: (1) active links, (2) passive links, (3) surface membranes, and (4) interfacing cubes. In hardware implementation, we show several self-deformable structures that can be generated from these components, including a self-deformable surface, expandable cube, and terrain-adaptive bridge. We present experiments to demonstrate that such robotic structures are able to perform real time deformation to adapt to different environments. In simulation, we show that these components can be configured into a variety of bio-inspired robots, such as an amoeba-like robot and a tissue-inspired material. We argue that self-deformation is well-suited for dynamic and sensing-adaptive shape change in modular robotics.

Sensing-based Shape formation on Modular Multi-robot Systems: A Theoretical Study [AAMAS 08] [IROS 08 workshop]

We present a theoretical study of decentralized control for sensing-based shape formation on modular multi-robot systems, where the desired shape is specified in terms of local sensor constraints between neighboring robot agents. We show that this problem can be formulated more generally as ``distributed constraint-maintenance" on a networked multi-agent system. It is strongly related to a class of multi-agent algorithms called ``distributed consensus", which includes several bio-inspired algorithms such as flocking and firefly synchronization. By exploiting this connection, we can theoretically analyze several important aspects of the decentralized shape formation algorithm and generalize it to more complex multi-agent scenarios. We show that the convergence time depends on (a) the number of robot agents and agent connection topology, (b) the complexity of the user-specified goal, and (c) the initial state of the robots. Using these results, we can provide precise statements on how the approach scales, and how quickly the system can adapt to perturbations. These results provide a deeper understanding of the contrast between centralized and decentralized multi-agent algorithms.

Self-organization of Environmentally-Adaptive Shapes on a Modular Robot [IROS 07] [Imagine 08]

 

Modular robots are a class of robotic systems composed of many identical, connected, programmable modules that can coordinate to change the shape of the overall robot. They have the potential to achieve a wide range of applications by reconfiguring their shapes to perform different functions. This requires robust and scalable control algorithms that can form a wide range of user-specified shapes, including shapes that adapt to the environment.

In the first stage of this project, we propose a decentralized algorithm for the reconfiguration of environmentally-adaptive shapes. We apply it to a chain-style modular robot, configured to form a flexible sheet structure. We show that the proposed algorithm is capable of achieving a wide class of environmentally-adaptive shapes, and the module control is simple, scalable, robust and provably correct. The algorithm is also self maintaining: the shape automatically adapts if the environment changes. We also demonstrate several novel applications which can be achieved within this framework via robot prototypes and simulations, such as a self-balancing table, terrain-adaptive bridge, and dynamic physical rendering device. In our experiments, we demonstrate the algorithm is highly responsive and robust in the face of real-world actuation and sensing noise.