Design and control of an origami-enabled soft crawling autonomous robot (OSCAR)

Soft mobile robots offer unique benefits as they are highly adaptable to the terrain of travel and safe for interaction with humans. However, the lack of autonomy currently limits their practical applications. Autonomous navigation has been well studied for conventional rigid-bodied robots; however, it is underrepresented in the soft mobile robot research community. Its implementation in soft robots comes with multiple challenges. However, the major challenge is the significant motion uncertainties due to the robot compliance, ground interactions, and limited available sensing. These uncertainties prevent high-level control implementation, such as autonomous navigation, to be performed successfully. Therefore, soft robots require robust design methods, as well as path following and path planning algorithms, to mitigate these uncertainties and enable autonomy. This dissertation develops and implements autonomous navigation for a novel origami-enabled soft crawling autonomous robot called OSCAR. In order to implement autonomous navigation, it first mitigates the OSCAR’s motion uncertainties by a multi-step iterative design process. Analysis has shown that OSCAR’s motion uncertainties are the result of: (i) the ground-feet interaction, (ii) effectiveness of low-level closed-loop control and, (iii) variability in the manufacturing assembly process. The iterative control-oriented design allows a robust and reliable OSCAR performance and enables high-level path following control implementation. To design and implement path following control, this research presents an idealized kinematic model and introduces an empirically based correction to make the model predictions match the experimental data. The dissertation investigates two separate path-following controllers: a model-based pure pursuit and a feedback controller. The controllers are investigated in both simulation and experiment and the need for feedback is clearly demonstrated. Finally, this research presents the path planning in order to complete OSCAR’s autonomous navigation. The simulation and experimental results show that OSCAR can accurately navigate in a 2D environment while avoiding static obstacles. Lastly, the coupled locomotion of multiple OSCARs demonstrates an extension of functionality and expands the potential design and operation space for this promising type of soft robot

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Master of Science

thesisThis thesis describes the design, modeling, and gait control of a new bounding/rolling quadruped robot called the roll-U-ped. The robot has four uniquely-designed compliant legs for bounding gait locomotion, and the legs can reconfigure for passive and powered rolling. One of the main advantages of such a design is versatility as the robot can efficiently and quickly traverse over flat and downhill terrain via rolling and then transition to running for traveling over more complex terrain with a bounding gait. The contributions of this work are: (1) a detailed description of the robot design, (2) modeling and simulation of bounding motion, (3) investigation of bounding gait effectiveness using sinusoidal control inputs and inputs obtained from machine learning, and (4) prototype development and performance evaluation. Specifically, the prototype robot utilizes 3D-printed compliant legs for dynamic running and rolling, and the dual-purpose leg design minimizes the number of joints. Two functional prototypes are developed with on-board embedded electronics and a single-board computer running the Robot Operating System for motion control and evaluation. Simulations of the bounding gait locomotion are shown and compared to the performance of the prototype designs. Additionally, the robot's running motion is investigated for two types of inputs: a sinusoidal trajectory and a learned gait using the Q-learning technique, where results demonstrate effective running and rolling behavior. For example, using sinusoidal inputs, the robot can run with a bounding gait over a flat and stiff sandpaper-like surface at speeds of up to 0.21 m/s. On the other hand, over a flat and tacky-cushioned surface, the speed is measured at 0.14 m/s. Simulation results for Q-learning show gait speeds of 0.22 m/s for the tacky-cushioned surface, where experiments on the physical system yielded a gait speed of 0.15 m/s. For powered rolling, the robot was able to reach a speed of 0.53 m/s over a flat-smooth surface. The results demonstrate proof-of-concept of the design and feasibility of using machine learning to determine inputs for effective running locomotion. Finally, possible future improvements to the design, modeling, and motion control of the robot are discussed

Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges

Continuum soft robots are mechanical systems entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). This work aims to introduce the control theorist perspective to this novel development in robotics. We aim to remove the barriers to entry into this field by presenting existing results and future challenges using a unified language and within a coherent framework. Indeed, the main difficulty in entering this field is the wide variability of terminology and scientific backgrounds, making it quite hard to acquire a comprehensive view on the topic. Another limiting factor is that it is not obvious where to draw a clear line between the limitations imposed by the technology not being mature yet and the challenges intrinsic to this class of robots. In this work, we argue that the intrinsic effects are the continuum or multi-body dynamics, the presence of a non-negligible elastic potential field, and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure

Modeling, simulation, and control of soft robots

2019 Fall.Includes bibliographical references.Soft robots are a new type of robot with deformable bodies and muscle-like actuations, which are fundamentally different from traditional robots with rigid links and motor-based actuators. Owing to their elasticity, soft robots outperform rigid ones in safety, maneuverability, and adaptability. With their advantages, many soft robots have been developed for manipulation and locomotion in recent years. However, the current state of soft robotics has significant design and development work, but lags behind in modeling and control due to the complex dynamic behavior of the soft bodies. This complexity prevents a unified dynamics model that captures the dynamic behavior, computationally-efficient algorithms to simulate the dynamics in real-time, and closed-loop control algorithms to accomplish desired dynamic responses. In this thesis, we address the three problems of modeling, simulation, and control of soft robots. For the modeling, we establish a general modeling framework for the dynamics by integrating Cosserat theory with Hamilton's principle. Such a framework can accommodate different actuation methods (e.g., pneumatic, cable-driven, artificial muscles, etc.). To simulate the proposed models, we develop efficient numerical algorithms and implement them in C++ to simulate the dynamics of soft robots in real-time. These algorithms consider qualities of the dynamics that are typically neglected (e.g., numerical damping, group structure). Using the developed numerical algorithms, we investigate the control of soft robots with the goal of achieving real-time and closed-loop control policies. Several control approaches are tested (e.g., model predictive control, reinforcement learning) for a few key tasks: reaching various points in a soft manipulator's workspace and tracking a given trajectory. The results show that model predictive control is possible but is computationally demanding, while reinforcement learning techniques are more computationally effective but require a substantial number of training samples. The modeling, simulation, and control framework developed in this thesis will lay a solid foundation to unleash the potential of soft robots for various applications, such as manipulation and locomotion

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Advances in Bio-Inspired Robots

This book covers three major topics, specifically Biomimetic Robot Design, Mechanical System Design from Bio-Inspiration, and Bio-Inspired Analysis on A Mechanical System. The Biomimetic Robot Design part introduces research on flexible jumping robots, snake robots, and small flying robots, while the Mechanical System Design from Bio-Inspiration part introduces Bioinspired Divide-and-Conquer Design Methodology, Modular Cable-Driven Human-Like Robotic Arm andWall-Climbing Robot. Finally, in the Bio-Inspired Analysis on A Mechanical System part, research contents on the control strategy of Surgical Assistant Robot, modeling of Underwater Thruster, and optimization of Humanoid Robot are introduced

Deep Reinforcement Learning for the Velocity Control of a Magnetic, Tethered Differential-Drive Robot

The ROBOPLANET Altiscan crawler is a magnetic-wheeled, differential-drive robot being explored as an option to aid, if not completely replace, humans in the inspection and maintenance of marine vessels. Velocity control of the crawler is a crucial part in establishing trust and reliability amongst its operators. However, thanks to the crawler's elongated, magnetic wheels and umbilical tether, it operates in a complex environment rich with nonlinear dynamics which makes control challenging. Model-based approaches for the control of a robot that aim to mathematically formalize the physics of the system require an in-depth knowledge of the domain. Reinforcement learning (RL) is a trial-and-error-based approach that can solve control problems in nonlinear systems. To accommodate for high-dimensionality and continuous state spaces, deep neural networks (DNNs) can be used as nonlinear function approximators to extend RL, creating a method known as deep reinforcement learning (DRL). DRL coupled with a simulated environment provides a way for a model to learn physics-naive control. The research conducted in this thesis explored the efficacy of a DRL algorithm, proximal policy optimization (PPO), to learn the velocity control of the Altiscan crawler by modeling its operating environment in a novel, GPU-accelerated simulation software called Isaac Gym. The approaches evaluated the error between measured base velocities of the crawler as a result of the actions provided by the DRL model and target velocities in six different environments. Two variants of PPO, standard and recurrent, were compared against the inverse velocity kinematics model of a differential-drive robot. The results show that velocity control in simulation is possible using PPO, but evaluation on the real crawler is needed to come to a meaningful conclusion.M.S

Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain. The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed. By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology