Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper. Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2° with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators

Locomotion Optimization of Photoresponsive Small-scale Robot: A Deep Reinforcement Learning Approach

Soft robots comprise of elastic and flexible structures, and actuatable soft materials are often used to provide stimuli-responses, remotely controlled with different kinds of external stimuli, which is beneficial for designing small-scale devices. Among different stimuli-responsive materials, liquid crystal networks (LCNs) have gained a significant amount of attention for soft small-scale robots in the past decade being stimulated and actuated by light, which is clean energy, able to transduce energy remotely, easily available and accessible to sophisticated control. One of the persistent challenges in photoresponsive robotics is to produce controllable autonomous locomotion behavior. In this Thesis, different types of photoresponsive soft robots were used to realize light-powered locomotion, and an artificial intelligence-based approach was developed for controlling the movement. A robot tracking system, including an automatic laser steering function, was built for efficient robotic feature detection and steering the laser beam automatically to desired locations. Another robot prototype, a swimmer robot, driven by the automatically steered laser beam, showed directional movements including some degree of uncertainty and randomness in their locomotion behavior. A novel approach is developed to deal with the challenges related to the locomotion of photoresponsive swimmer robots. Machine learning, particularly deep reinforcement learning method, was applied to develop a control policy for autonomous locomotion behavior. This method can learn from its experiences by interacting with the robot and its environment without explicit knowledge of the robot structure, constituent material, and robotic mechanics. Due to the requirement of a large number of experiences to correlate the goodness of behavior control, a simulator was developed, which mimicked the uncertain and random movement behavior of the swimmer robots. This approach effectively adapted the random movement behaviors and developed an optimal control policy to reach different destination points autonomously within a simulated environment. This work has successfully taken a step towards the autonomous locomotion control of soft photoresponsive robots

Towards a Universal Modeling and Control Framework for Soft Robots

Traditional rigid-bodied robots are designed for speed, precision, and repeatability. These traits make them well suited for highly structured industrial environments, but poorly suited for the unstructured environments in which humans typically operate. Soft robots are well suited for unstructured human environments because they them to can safely interact with delicate objects, absorb impacts without damage, and passively adapt their shape to their surroundings. This makes them ideal for applications that require safe robot-human interaction, but also presents modeling and control challenges. Unlike rigid-bodied robots, soft robots exhibit continuous deformation and coupling between structure and actuation and these behaviors are not readily captured by traditional robot modeling and control techniques except under restrictive simplifying assumptions. The contribution of this work is a modeling and control framework tailored specifically to soft robots. It consists of two distinct modeling approaches. The first is a physics-based static modeling approach for systems of fluid-driven actuators. This approach leverages geometric relationships and conservation of energy to derive models that are simple and accurate enough to inform the design of soft robots, but not accurate enough to inform their control. The second is a data-driven dynamical modeling approach for arbitrary (soft) robotic systems. This approach leverages Koopman operator theory to construct models that are accurate and computationally efficient enough to be integrated into closed-loop optimal control schemes. The proposed framework is applied to several real-world soft robotic systems, enabling the successful completion of control tasks such as trajectory following and manipulating objects of unknown mass. Since the framework is not robot specific, it has the potential to become the dominant paradigm for the modeling and control of soft robots and lead to their more widespread adoption.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163062/1/bruderd_1.pd

Soft actuator and agile soft robot

Robots play an important part in many aspects of our society by doing repetitive, dangerous, or precision tasks. Most existing robots are made of rigid components, which lack passive compliance and pose a challenge in adapting to the environment and safe human-robot interaction. Rigid robots may be equipped with sensors and programmed with proprioceptive feedback control to achieve active compliance, but this may fail in the event of unforeseen situations or sensor failure. In contrast, animals have evolved flexible or soft body parts to help them adapt to changing environments. Soft robotics is an emerging field in robotics, drawing inspiration from nature by integrating soft material into the actuator and mechanical design. With the inclusion of soft material, soft actuators and robots can deform actively/passively, making it possible to sense, absorb impact, and adapt to its environment with deformation. However, while soft actuators/robots have superior properties to rigid ones, they are often challenging to manufacture and control precisely. In addition, they may suffer from slow speed and material degradation. Thus, in this thesis, we aim to address the issues in developing high-performance soft actuators and soft robots. The thesis is divided into two parts. In the first part, we focus on improving the manufacturability and performance of a self-contained soft actuator originated in the Creative Machines Lab. The soft actuator is composed of a cured silicone-ethanol mixture embedded with heating coils. When the coils are electrically actuated, ethanol trapped inside undergoes liquid-vapor transitions, and thus the actuator undergoes extreme volume change. While this actuator exhibits high strain and high stress, it is very slow to actuate, has limited life cycles, and requires molds to manufacture. The first part of the thesis will address these issues. Specifically, in chapter 2, we discuss using multi-material 3D printing to automate the manufacturing of silicone-ethanol composite. In chapter 3, we discuss using laser-cut flexible Kirigami patterns to improve the manufacturability of its heating element. Chapter 4 characterizes its actuation profile and addresses improvements to the thermal conductivity by infusing thermally conductive fillers. Soft actuation is an actively researched area; however, many high-performance soft actuators are challenging to manufacture and thus are less accessible to the general robotics community. Conventional actuators such as electric motors are widely available but lack flexibility. Therefore, the second part of the thesis aims at combining rigid motors with soft materials to design and control high-performance hybrid soft robots. Simulation is a good way to evaluate and optimize robot design and control. However, existing simulators that support motor-driven soft robots have limited features. Chapter 5 discusses this issue and presents a physically based real-time soft robot simulator capable of simulating motor-driven soft robots. In addition, chapter 5 presents the design and control of a 3D printed hybrid soft quadruped robot. Chapter 6 presents the design and control of a 3D printed hybrid soft humanoid robot. The two parts of the thesis aim to improve aspects in soft actuators and soft robots. In conclusion, we summarize the lessons learned in developing soft actuators/robots and new possibilities and challenges for advancing soft robotics research