Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Designing an inexpensive, low-noise, safe for individual, mobile robot with an efficient vision system represents a challenge. This paper proposes a soft mobile robot inspired by the silkworm body structure and moving behavior. Two identical pneumatic artificial muscles (PAM) have been used to design the body of the robot by sewing the PAMs longitudinally. The proposed robot moves forward, left, and right in steps depending on the relative contraction ratio of the actuators. The connection between the two artificial muscles gives the steering performance at different air pressures of each PAM. A camera (eye) integrated into the proposed soft robot helps it to control its motion and direction. The silkworm soft robot detects a specific object and tracks it continuously. The proposed vision system is used to help with automatic tracking based on deep learning platforms with real-time live IR camera. The object detection platform, named, YOLOv3 is used effectively to solve the challenge of detecting high-speed tiny objects like Tennis balls. The model is trained with a dataset consisting of images of   Tennis balls. The work is simulated with Google Colab and then tested in real-time on an embedded device mated with a fast GPU called Jetson Nano development kit. The presented object follower robot is cheap, fast-tracking, and friendly to the environment. The system reaches a 99% accuracy rate during training and testing. Validation results are obtained and recorded to prove the effectiveness of this novel silkworm soft robot. The research contribution is designing and implementing a soft mobile robot with an effective vision system

The Design, Construction, and Experimental Characterization of Spatial Parallel Architectures of Elastofluidic Systems

Creating organic, life like motion has historically been extremely difficult and costly for general applications. Traditional structures and robots use rigid components with discrete joints to produce desired motions but are limited in freedom by the range of motion each additional component allows. In a traditionally rigid robot complex motion is obtained through the addition of joints and links. These additions add complexity to a rigid robot but improve its ability to create motion. Soft robotics aims to overcome the limitations of traditional robotics by creating integrated actuation and structure which more closely imitates organic movement. Often the most effective examples to learn from are natural phenomenon or organisms such as underwater and land based invertebrates. In pursuit of the goal of effective soft robotics researchers have explored the idea of a pneumatic elastofluidic actuator, one which expands and deforms in response to applied pressure. While these systems have demonstrated some limited success, they are often used either as a single entity or in series with one another to produce novel motions. In this thesis parallel structures made of these actuators are shown to have the potential to be extremely powerful and useful for soft robotic applications. These spatial arrangements of connected and dependent actuators exhibit behaviors impossible for a single actuator. This research concerns the effective design and construction of these complex parallel structures in an attempt to define a method of characterization which produces useful and desirable spatial architectures and motions

The Design, Manufacture, and Testing of a Novel Adhesion System for a Climbing Vehicle

We present the design and fabrication of a prototype wall-climbing vehicle employing a unique combined locomotion and adhesion system in which the adhesive vacuum is transmitted through moving, perforated treads. Implementing the adhesion/drive system involved a broad range of design challenges, including: developing reliable sealing of sliding and static interfaces, understanding the frictional interactions between the drive treads and various vehicle components and surfaces on which they ride, as well as designing for lightness, manufacturability, and adjustability. The clean sheet design presented in this thesis was taken from concept to functioning prototype in less than 6 months, requiring a considered mix of off-the-shelf components, custom fabrication, and outsourced production. Proof of concept testing is reviewed, including static pressure and force results as well as dynamic vertical surface maneuverability trials

Functionally-Graded Soft Robotic Actuators

The goal of this project is to design, analyze, and fabricate a pneumatically powered, functionally-graded soft robotic actuator made of a polymer embedded with nanoparticles, and later attach three of them to a hand-sized gripper assembly for object distribution. This was accomplished through finite element analysis, polymer-nanoparticle mix tensile testing, and construction of a mechanical arm controlled by a wearable gesture controller. Results show that the functionally-graded actuator produces 1.6 times the lateral force output and twice the displacement than the 15wt% control actuator

Designing LMPA-Based Smart Materials for Soft Robotics Applications

This doctoral research, Designing LMPA (Low Melting Point Alloy) Based Smart Materials for Soft Robotics Applications, includes the following topics: (1) Introduction; (2) Robust Bicontinuous Metal-Elastomer Foam Composites with Highly Tunable Mechanical Stiffness; (3) Actively Morphing Drone Wing Design Enabled by Smart Materials for Green Unmanned Aerial Vehicles; (4) Dynamically Tunable Friction via Subsurface Stiffness Modulation; (5) LMPA Wool Sponge Based Smart Materials with Tunable Electrical Conductivity and Tunable Mechanical Stiffness for Soft Robotics; and (6) Contributions and Future Work.Soft robots are developed to interact safely with environments. Smart composites with tunable properties have found use in many soft robotics applications including robotic manipulators, locomotors, and haptics. The purpose of this work is to develop new smart materials with tunable properties (most importantly, mechanical stiffness) upon external stimuli, and integrate these novel smart materials in relevant soft robots. Stiffness tunable composites developed in previous studies have many drawbacks. For example, there is not enough stiffness change, or they are not robust enough. Here, we explore soft robotic mechanisms integrating stiffness tunable materials and innovate smart materials as needed to develop better versions of such soft robotic mechanisms. First, we develop a bicontinuous metal-elastomer foam composites with highly tunable mechanical stiffness. Second, we design and fabricate an actively morphing drone wing enabled by this smart composite, which is used as smart joints in the drone wing. Third, we explore composite pad-like structures with dynamically tunable friction achieved via subsurface stiffness modulation (SSM). We demonstrate that when these composite structures are properly integrated into soft crawling robots, the differences in friction of the two ends of these robots through SSM can be used to generate translational locomotion for untethered crawling robots. Also, we further develop a new class of smart composite based on LMPA wool sponge with tunable electrical conductivity and tunable stiffness for soft robotics applications. The implications of these studies on novel smart materials design are also discussed

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

Design and Control of Compliant Tensegrity Robots Through Simulation and Hardware Validation

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center has developed and validated two different software environments for the analysis, simulation, and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ("tensile-integrity") structures have unique physical properties which make them ideal for interaction with uncertain environments. Yet these characteristics, such as variable structural compliance, and global multi-path load distribution through the tension network, make design and control of bio-inspired tensegrity robots extremely challenging. This work presents the progress in using these two tools in tackling the design and control challenges. The results of this analysis includes multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures. The current hardware prototype of a six-bar tensegrity, code-named ReCTeR, is presented in the context of this validation