Chih-Han Yu

Applicability of Shape Memory Alloy Wire for an Active, Soft Orthotic

Current treatments for gait pathologies associated with neuromuscular disorders may employ a passive, rigid brace. While these provide certain benefits, they can also cause muscle atrophy. In this study, we examined NiTi shape memory alloy (SMA) wires that were annealed into springs to develop an active, soft orthotic (ASO) for the knee. Actively controlled SMA springs may provide variable assistances depending on factors such as when, during the gait cycle, the springs are activated; ongoing muscle activity level; and needs of the wearer. Unlike a passive brace, an active orthotic may provide individualized control, assisting the muscles so that they may be used more appropriately, and possibly leading to a re-education of the neuro-motor system and eventual independence from the orthotic system. A prototype was tested on a suspended, robotic leg to simulate the swing phase of a typical gait. The total deflection generated by the orthotic depended on the knee angle and the total number of actuators triggered, with a max deflection of 35°. While SMA wires have a high energy density, they require a significant amount of power. Furthermore, the loaded SMA spring response times were much longer than the natural frequency of an average gait for the power conditions tested. While the SMA wires are not appropriate for correction of gait pathologies as currently implemented, the ability to have a soft, actuated material could be appropriate for slower timescale applications.

Morpho: A self-deformable modular robot inspired by cellular structure

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, terrain-adaptive bridge [C.-H. Yu et al., 2007]. 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.

Self-adapting modular robotics: A generalized distributed consensus framework

Biological systems achieve amazing adaptive behavior with local agents performing simple sensing and actions. Modular robots with similar properties can potentially achieve self-adaptation tasks robustly. Inspired by this principle, we present a generalized distributed consensus framework for self-adaptation tasks in modular robotics. We demonstrate that a variety of modular robotic systems and tasks can be formulated within such a framework, including (1) an adaptive column that can adapt to external force, (2) a modular gripper that can manipulate fragile objects, and (3) a modular tetrahedral robot that can locomote towards a light source. We also show that control algorithms derived from this framework are provably correct. In real robot experiments, we demonstrate that such a control scheme is robust towards real world sensing and actuation noise. This framework can potentially be applied to a wide range of distributed robotics applications.

A Self-adaptive Framework for Modular Robots in a Dynamic Environment: Theory and Applications

Biological systems achieve amazing adaptive behavior with local agents that perform simple sensing and actions. This has recently inspired the control strategies and design principles of modular robots. In this paper, we introduce a distributed control framework through which modular robots can achieve various self-adaptive tasks. By self-adaptive tasks, we imply tasks where the modular robot uses its distributed sensors to solve tasks and cope with environment changes. We show that modular robot self-adaptive tasks can be formulated as distributed constraint maintenance on a networked multi-agent system such that performing collective self-adaption can be simplified as satisfying local constraints. This formulation allows us to propose a control framework based on a class of multi-agent algorithms called distributed consensus. We further generalize this framework to capture a wide range of sensor—actuator networks in different distributed robotic systems. We prove various theoretical properties of the framework, including its scalability to network diameter and the number of modules. Based on our theoretical understanding, we demonstrate this framework with various tasks, including (1) self-adaptive structures that maintain their shapes in changing environments, (2) an adaptive column that can adapt to external force, and (3) a modular gripper that can manipulate fragile objects. This work provides a deep understanding of the theoretical properties of distributed consensus-type control and its applications to modular robots.

Self-organization of environmentally-adaptive shapes on a modular robot

Modular robots 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. Here we present a decentralized algorithm for self-organizing 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. Finally, we present several applications which can be achieved within this framework via robot prototypes and simulations, such as a self-balancing table. In our experiments, we demonstrate the algorithm is highly responsive and robust in the face of real-world actuation and sensing noise.

Coordinating collective locomotion in an amorphous modular robot

Modular robots can potentially assemble into a wide range of configurations to locomote in different environments. However, designing locomotion strategies for each configuration is often tedious and has generally relied on a priori known connection geometry. Here we present a framework for 2D modular robots made of square modules assembled with arbitrary geometry, which achieve collective and directed locomotion with no centralized controller. Individual modules communicate locally and provably achieve consensus in coordinating movement in a common travel direction. In experiments with simulations and hardware prototypes, we show that robots achieve effective locomotion, irrespective of the number of modules and their connectivity which can be highly asymmetric.

Engineering self-adaptive modular robotics: A bio-inspired approach

In nature, animal groups achieve robustness and scalability with each individual executes a simple and adaptive strategy. Inspired by this phenomenon, we propose a decentralized control framework for modular robots to achieve coordinated and self-adaptive tasks with each modules performs simple distributed sensing and actuation [1]. In this demonstration, we show that such a framework allows several different modular robotic systems to achieve self-adaptation tasks scalably and robustly, examples tasks include module-formed table and bridge that adapt to constantly-perturbed environment, a 3D relief display that renders sophisticated objects, and a tetrahedral robot that performs adaptive locomotion.