Portland State University PDXScholar Dissertations and Theses Dissertations and Theses Spring 7-15-2019 Design of a Canine Inspired Quadruped Robot as a Platform for Synthetic Neural Network Control Cody Warren Scharzenberger Portland State University Follow this and additional works at: https://pdxscholar.edu/open_access_etds Part of the Mechanical Engineering Commons, and the Robotics Commons Let us know how access to this document benefits you. Recommended Citation Scharzenberger, Cody Warren, "Design of a Canine Inspired Quadruped Robot as a Platform for Synthetic Neural Network Control" (2019). Dissertations and Theses.7014 This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar.
Please contact us if we can make this document more accessible: pdxscholar@pdx. Design of a Canine Inspired Quadruped Robot as a Platform for Synthetic Neural Network Control by Cody Warren Scharzenberger A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering Thesis Committee: Alexander Hunt, Chair David Turcic Sung Yi Portland State University 2019 Abstract Legged locomotion is a feat ubiquitous throughout the animal kingdom, but modern robots still fall far short of similar achievements. This paper presents the design of a canine-inspired quadruped robot named DoggyDeux as a platform for synthetic neural network (SNN) research that may be one avenue for robots to attain animal-like agility and adaptability. DoggyDeux features a fully 3D printed frame, 24 braided pneumatic actuators (BPAs) that drive four 3-DOF limbs in antagonistic extensor-flexor pairs, and an electrical system that allows it to respond to commands from a SNN comprised of central pattern generators (CPGs).
Compared to the previous version of this robot, DoggyDeux eliminates out-of-plane bending moments on the legs, increases the range of motion of each joint, and eliminates buckling of the BPAs by utilizing a biologically inspired muscle attachment approach. A simple SNN comprised of a single isolated CPG for each joint is used to control the front left leg on DoggyDeux and joint angle data from this leg is collected to verify that the robot responds correctly to inputs from its SNN. Future design work on DoggyDeux will involve further improving the muscle attachment mechanism, while future SNN research will include expanding the robot’s SNN to achieve coordinated locomotion with all four legs utilizing sensory feedback. i Dedication I would like to dedicate this work to my best friend and partner, Julie Braet, without whose constant support this work would not have been possible.
ii Acknowledgements I would like to acknowledge the support of the entire Agile and Adaptive Robotics Lab at Portland State University, especially Dr. Alex Hunt for his guidance on this thesis, Connor Morrow for providing frequent consultation, and Jonas Mendoza for his help designing a harness for DoggyDeux. I would also like to thank Dr. David Turcic for providing frequent feedback on the electrical and control system design of this robot, as well as Dr.
Sung Yi for serving on my thesis committee. Finally, I would like to acknowledge support by Portland State University, the National Science Foundation under award IIS-1608111, and the National Institutes of Health Common Fund and Office of Scientific Workforce Diversity under awards UL1GM118964, RL5GM118963, and TL4GM118965, administered by the National Institute of General Medical Sci- ences. This work is solely my responsibility and does not necessarily represent the official view of the National Institutes of Health. iii Contents Abstract i Dedication ii Acknowledgements iii List of Tables vii List of Figures viii List of Abbreviations xii Chapter 1: Introduction 1 1.1 Braided Pneumatic Actuators (BPAs) .2 Central Pattern Generators (CPGs) .3 Puppy at Case Western Reserve University .1 Research with Puppy.
14 iv Chapter 3: Methodology 17 3.1 Mechanical Design Methodology .3 Actuation System Design .2 Electrical Design Methodology .3 Control System Design Methodology .1 Local Pressure Controller Design .2 Synthetic Neural Network Controller Design. 50 Chapter 4: Materials & Manufacturing 52 4.1 Mechanical Materials & Manufacturing .1 Structural Materials & Manufacturing .2 Harness Materials & Manufacturing .3 Actuation System Materials & Manufacturing .2 Electrical System Materials & Manufacturing .1 Mechanical Design Results .2 Local Pressure Control Results .3 Synthetic Neural Network Control Results. 64 Chapter 6: Discussion & Future Work 68 6.1 Local Pressure Control .2 Synthetic Neural Network Control. 78 Bibliography 79 vi List of Tables 4.1 Onyx material properties as provided by Markforged.2 3D printer settings used to print most parts on DoggyDeux.3 3D printer settings used to print custom fittings on DoggyDeux.4 Braided pneumatic actuator data for DoggyDeux robot at Portland State University.1 Limb lengths and proportions for DoggyDeux at Portland State Uni- versity compared to typical canine limb proportions [8].2 Range of motion of DoggyDeux joints compared to typical canine range of motion during walking [8].
59 vii List of Figures 1.1 (a) Puppy robot at Case Western Reserve University. (b) DoggyDeux robot at Portland State University.1 (a) Deflated Festo braided pneumatic actuator. (b) Inflated Festo braided pneumatic actuator.2 Four neuron CPG comprised of two interneurons and two half-center neurons with persistent sodium channels and mutual inhibition.3 Severe buckling of the front right shoulder extensor braided pneumatic actuator on Puppy at Case Western Reserve University.1 Mechanical systems block diagram.2 DoggyDeux robot frame at Portland State University.3 DoggyDeux’s front left scapula section view.4 (a) Front right scapula of Puppy robot at CWRU. (b) Front right scapula of DoggyDeux robot at PSU.5 (a) Front right shoulder joint on Puppy robot at CWRU.
(b) Front right shoulder joint on DoggyDeux robot at PSU.6 (a) Front right knee joint on Puppy robot at CWRU. (b) Front right knee joint of DoggyDeux robot at PSU.7 (a) Front right wrist of Puppy robot at CWRU. (b) Front right wrist of DoggyDeux at PSU.8 Exploded view of DoggyDeux’s back left ankle.9 Section view of DoggyDeux’s back left ankle.10 (a) Back right hip on Puppy at CWRU. (b) Back right hip on Doggy- Deux at PSU.11 (a) Back right knee on Puppy at CWRU.
(b) Back right knee on Dog- gyDeux at PSU.12 (a) Back right ankle on Puppy at CWRU. (b) Back right ankle on DoggyDeux at PSU.13 (a) Spine on Puppy at CWRU. (b) Spine on DoggyDeux at PSU.14 (a) Left scapula muscle attachment bracket on DoggyDeux at PSU. (b) Right scapula muscle attachment bracket on DoggyDeux at PSU.15 Top view of DoggyDeux at PSU with harness attachment components boxed in red.16 DoggyDeux harness at PSU.17 (a) Rear view of DoggyDeux’s back knee at PSU.
(b) Front view of DoggyDeux’s back knee at PSU.18 Pressure sensor array on DoggyDeux at Portland State University.19 Pneumatic routing schematic for DoggyDeux at Portland State Uni- versity (not to scale).20 Electrical systems block diagram.21 Information flow between DoggyDeux programs.22 Information flow between DoggyDeux electrical hardware modules.23 Power supply module layout.24 Four 3-stage multiple feedback filter module layout.25 Analog scaling module layout.26 64-Channel multiplexer / demultiplexer module layout.27 Master microcontroller module layout.28 Slave microcontroller module layout.29 Transistor valve breakout module layout.30 Control systems block diagram.31 Bang-bang control flow chart.32 Front left scapula synthetic neural network on DoggyDeux at Portland State University.1 Festo valve manifold used on DoggyDeux at Portland State University.1 To scale labeled schematic of DoggyDeux’s frame with range of motion for each joint.2 (a) BPA pressure step response without flow rate restriction. (b) BPA pressure step response with flow rate restriction.3 (a) BPA pressure sinusoidal response without flow rate restriction. (b) BPA pressure sinusoidal response with flow rate restriction.4 Front left scapula data during operation of DoggyDeux with a simple SNN. (a) Front left scapula CPG membrane voltages.
(b) Front left scapula muscle tensions. (c) Front left scapula BPA pressure. (d) Front left scapula joint angle.5 Front left shoulder data during operation of DoggyDeux with a simple SNN. (a) Front left shoulder CPG membrane voltages.
(b) Front left shoulder muscle tensions. (c) Front left shoulder BPA pressure. (d) Front left shoulder joint angle.6 Front left wrist data during operation of DoggyDeux with a simple SNN. (a) Front left wrist CPG membrane voltages.
(b) Front left wrist muscle tensions. (c) Front left wrist BPA pressure. (d) Front left wrist joint angle. 67 xi List of Abbreviations ADC - Analog-Digital Converter BPA - Braided Pneumatic Actuator CPG - Central Pattern Generator DAC - Digital-Analog Converter DEMUX - Demultiplexer DOF - Degree of Freedom EXT - Extensor FLX - Flexor GUI - Graphical User Interface IMU - Inertial Measurement Unit MUX - Multiplexer PAM - Pneumatic Artificial Muscle PWM - Pulse Width Modulation SNN - Synthetic Neural Network SPI - Serial Peripheral Interface UART - Universal Asynchronous Receiver/Transmitter xii Chapter 1: Introduction Although animals are able to effortlessly achieve complex locomotion in unstructured environments, similar accomplishments still prove elusive for modern robots.
In par- ticular, legged locomotion is a versatile ambulatory technique that is ubiquitous in the animal kingdom from insects and small mammals to humans; yet current control methods are neither robust nor adaptable enough to deliver similar results in artificial systems. One increasingly important approach for addressing the problem of achiev- ing legged locomotion in robots has therefore been to turn to biology for inspiration. The field of biologically inspired robotics casts a wide net, including approaches that draw loosely from biological observations to strict biological realism [14]. However, as the fields of neurobiology and computational neuroscience have matured, more details about the underlying biological neural circuits used by animals for motor con- trol have become available to roboticists [6].
Beyond capturing merely the biological details of structure and form, roboticists are able to study and apply the fundamen- tal mechanisms of biological control. It is for the purpose of better understanding these biological control systems and applying them to robotics that the Biologically Inspired Robotics Lab at Case Western Reserve University (CWRU) developed the canine inspired quadruped robot named Puppy pictured in Fig. While the physical design of Puppy agrees with biological data taken from dogs, more im- portantly, it serves as a platform for testing biologically inspired synthetic neural networks (SNNs) for locomotion control. Toward these same goals, the work pre- 1 sented herein details the design of an updated version of Puppy, named DoggyDeux, as a test bed for SNN and controls research at Portland State University (PSU).1: (a) Puppy robot at Case Western Reserve University.
(b) DoggyDeux robot at Portland State University.1 Motivation The motivation for designing a new version of the Puppy robot is several fold. At the highest level, we intend to use DoggyDeux as a platform for our future SNN research, which will involve such things as expanding our previous SNN to achieve coordinated locomotion among all four limbs, improving the biological plausibility of the constituent neuron models that comprise our SNN, and incorporating more biologically meaningful feedback mechanisms (vision, vestibular sense, etc.) into our SNN. We know from both biology and machine learning that neural networks excel at performing non-linear mappings and can learn from experience, which can improve their ability to function in unstructured environments. Since legged locomotion is a task with just such a requirement, and modern control methods struggle to contend with non-linearities and systems with changing dynamics, it is highly likely that 2 leveraging SNNs for legged locomotion will produce more robust and stable results.
Furthermore, by actually implementing SNNs on a physical robot, we can assess the efficacy of proposed biological models of locomotion in a controlled environment disjoint from the full complexity of an animal body while still maintaining the ability to interact with and retrieve feedback from the environment. Current goals of the project involve redesigning the Puppy robot in order to eliminate the short comings associated with the original robot and to improve the robot’s ability to function as a platform for SNN research.2 Objectives While the ultimate purpose of DoggyDeux is to serve as a tool for SNN research, the focus of this work is primarily on the design, implementation, and testing of this new robot. To this end, we have several goals that we seek to achieve related to the design of DoggyDeux, including: 1.