Kyunam Kim

Rapid prototyping design and control of tensegrity soft robot for locomotion

Co-robots that can effectively move with and operate alongside humans in a variety of conditions could revolutionize the utility of robots for a wide range of applications. Unfortunately, most current robotic systems have difficulty operating in human environments that people easily traverse, much less interact with people. Wheeled robots have difficulty climbing stairs or going over rough terrain. Heavy and powerful legged robots pose safety risks when interacting with humans. Compliant, lightweight tensegrity robots built from interconnected tensile (cables) and compressive (rods) elements are promising structures for co-robotic applications. This paper describes design and control of a rapidly prototyped tensegrity robot for locomotion. The software and hardware of this robot can be extended to build a wide range of tensegrity robotic configurations and control strategies. This rapid prototyping approach will greatly lower the barrier-of-entry in time and cost for research groups studying tensegrity robots suitable for co-robot applications.

Sensor-Based Predictive Modeling for Smart Lighting in Grid-Integrated Buildings

Studies show that if we retrofit all the lighting systems in the buildings of California with dimming ballasts, then it would be possible to obtain a 450 MW of regulation, 2.5 GW of nonspinning reserve, and 380 MW of contingency reserve from participation of lighting loads in the energy market. However, in order to guarantee participation, it will be important to monitor and model lighting demand and supply in buildings. To this end, wireless sensor and actuator networks have proven to bear a great potential for personalized intelligent lighting with reduced energy use at 50%-70%. Closed-loop control of these lighting systems relies upon instantaneous and dense sensing. Such systems can be expensive to install and commission. In this paper, we present a sensor-based intelligent lighting system for future grid-integrated buildings. The system is intended to guarantee participation of lighting loads in the energy market, based on predictive models of indoor light distribution, developed using sparse sensing. We deployed ~92 % fewer sensors compared with state-of-art systems using one photosensor per luminaire. The sensor modules contained small solar panels that were powered by ambient light. Reduction in sensor deployments is achieved using piecewise linear predictive models of indoor light, discretized by clustering for sky conditions and sun positions. Day-ahead daylight is predicted from forecasts of temperature, humidity, and cloud cover. With two weeks of daylight and artificial light training data acquired at the sustainability base at NASA Ames, our model was able to predict the illuminance at seven monitored workstations with 80%-95% accuracy. Moreover, our support vector regression model was able to predict day-ahead daylight at ~92% accuracy.

Robust learning of tensegrity robot control for locomotion through form-finding

Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control strategies for ball-shaped tensegrity robots have successfully enabled punctuated rolling, hill-climbing and obstacle climbing. These gains have been made possible through the use of machine learning and physics simulations that allow controls to be “learned” instead of being engineered in a top-down fashion. While effective in simulation, these emergent methods unfortunately give little insight into how to generalize the learned control strategies and evaluate their robustness. These robustness issues are especially important when applied to physical robots as there exists errors with respect to the simulation, which may prevent the physical robot from actually rolling. This paper describes how the robustness can be addressed in three ways: 1) We present a dynamic relaxation technique that describes the shape of a tensegrity structure given the forces on its cables; 2) We then show how control of a tensegrity robot “ball” for locomotion can be decomposed into finding its shape and then determining the position of the center of mass relative to the supporting polygon for this new shape; 3) Using a multi-step Monte Carlo based learning algorithm, we determine the structural geometry that pushes the center of mass out of the supporting polygon to provide the most robust basic mobility step that can lead to rolling. Combined, these elements will give greater insight into the control process, provide an alternative to the existing physics simulations and offer a greater degree of robustness to bridge the gap between simulation and hardware.

Soft Spherical Tensegrity Robot Design Using Rod-Centered Actuation and Control

This paper presents the design, analysis, and testing of a fully actuated modular spherical tensegrity robot for co-robotic and space exploration applications. Robots built from tensegrity structures (composed of pure tensile and compression elements) have many potential benefits including high robustness through redundancy, many degrees-of-freedom in movement and flexible design. However, to take full advantage of these properties, a significant fraction of the tensile elements should be active, leading to a potential increase in complexity, messy cable, and power routing systems and increased design difficulty. Here, we describe an elegant solution to a fully actuated tensegrity robot: The TT-3 (version 3) tensegrity robot, developed at UC Berkeley, in collaboration with NASA Ames, is a lightweight, low cost, modular, and rapidly prototyped spherical tensegrity robot. This robot is based on a ball-shaped six-bar tensegrity structure and features a unique modular rod-centered distributed actuation and control architecture. This paper presents the novel mechanism design, architecture, and simulations of TT-3, an untethered, fully actuated cable-driven six-bar spherical tensegrity robot. Furthermore, this paper discusses the controls and preliminary testing performed to observe the system’s behavior and performance and is evaluated against previous models of tensegrity robots developed at UC Berkeley and elsewhere. [DOI: 10.1115/1.4036014]

Hopping and rolling locomotion with spherical tensegrity robots

This work presents a 10 kg tensegrity ball probe that can quickly and precisely deliver a 1 kg payload over a 1 km distance on the Moon by combining cable-driven rolling and thruster-based hopping. Previous research has shown that cable-driven rolling is effective for precise positioning, even in rough terrain. However, traveling large distances using thruster-based hopping, which is made feasible by the lightweight and compliant nature of the tensegrity structure, has not been explored. To evaluate the feasibility of a thruster-based tensegrity robot, a centrally-positioned cold gas thruster with nitrogen propellant was selected, and the system was simulated using the NASA Tensegrity Robotics Toolkit (NTRT) for four hopping profiles on hilly terrains. Optimizing energy efficiency and mechanical capabilities of the tensegrity robot, hopping profiles with a long flight distance per hop, followed by the higher accuracy rolling, are recommended. Simulations also show that thrust regulation can improve energy efficiency. Regulation of thrust magnitude can be achieved using a pressure regulator, but regulation of thrust orientation calls for additional control effort. In this paper, it is demonstrated that gimbal systems as well as shape-shifting control of the tensegrity structure have the potential to regulate thrust orientation. Finally, algorithms for localization and path planning that combine hopping and rolling for energy-efficient navigation are presented.

Spin-axis stabilization of a rigid body about an arbitrary direction using Two Reaction Wheels

The purpose of attitude stabilization is to stabilize a body about an equilibrium point, usually requiring at least three independent actuations. In practice, however, a control law for an underactuated system with two actuators becomes crucial when one of the three actuators fails during operation, or when the control objective is to stabilize the spin axis of the body about an arbitrary direction, possibly with a nonzero spinning velocity. In this work, we develop a feedback control law that globally and asymptotically achieves spin-axis stabilization of a rigid body about an arbitrary axis using only two reaction wheels. For this, a modified version of (z;w)-parameterization is presented for the purpose of describing attitude kinematics of a rigid body. We then introduce dynamics of the body with two reaction wheels and use a feedback linearization technique to develop a control law with the goal of achieving spin-axis stabilization of the body. We show that the developed control law is globally and asymptotically stable by using Lyapunovs direct method in conjunction with LaSalles invariance principle. This controller is implemented in simulation, and results are presented that show its stabilizing behavior. While the control law presented here is suitable for general applications, we primarily focus on its application to the thrust direction regulation of tensegrity hoppers.