A Biologically Inspired Robot for Assistance in Urban Search and Rescue

Total Page:16

File Type:pdf, Size:1020Kb

A Biologically Inspired Robot for Assistance in Urban Search and Rescue A Biologically Inspired Robot for Assistance in Urban Search and Rescue by ALEXANDER HUNT Submitted in partial fulfillment of the requirements For the degree of Master of Science in Mechanical Engineering Advisor: Dr. Roger Quinn Department of Mechanical and Aerospace Engineering CASE WESTERN RESERVE UNIVERSITY May 2010 Table of Contents A Biologically Inspired Robot for Assistance in Urban Search and Rescue ...................................... 0 Table of Contents ............................................................................................................................. 1 List of Tables .................................................................................................................................... 3 List of Figures ................................................................................................................................... 4 Acknowledgements.......................................................................................................................... 6 Abstract ............................................................................................................................................ 7 Chapter 1: Introduction ................................................................................................................... 8 1.1 Search and Rescue ................................................................................................................. 8 1.2 Robots in Search and Rescue ................................................................................................. 9 1.3 Identified Specifications ....................................................................................................... 10 1.4 Robotic Locomotion ............................................................................................................. 13 Chapter 2: Background .................................................................................................................. 15 2.1 Reduced Actuation Robots .................................................................................................. 15 2.2 Search and Rescue Robots ................................................................................................... 20 2.3 Situational Awareness Mast ................................................................................................ 23 Chapter 3: Design and Manufacturing of USAR WhegsTM ............................................................. 25 3.1 Working Model Simulations ................................................................................................. 26 3.2 Chassis .................................................................................................................................. 30 3.3 Locomotion .......................................................................................................................... 34 3.4 Gearing ................................................................................................................................. 40 3.5 Torsion Device ...................................................................................................................... 40 Internal Linear Spring Design 1 .............................................................................................. 42 Internal Linear Spring Design 2 .............................................................................................. 43 External Linear Spring Design ................................................................................................ 44 Torsion Spring Design ............................................................................................................ 45 Comparison ............................................................................................................................ 46 Pursued Designs ..................................................................................................................... 47 3.6 Wheel-Legs ........................................................................................................................... 50 3.7 Electronics ............................................................................................................................ 61 Chapter 4: Tests ............................................................................................................................. 66 1 4.1 Wheel-Leg Test .................................................................................................................... 66 4.2 Lab Runs ............................................................................................................................... 68 Chapter 5: Results .......................................................................................................................... 70 5.1 Specifications ....................................................................................................................... 70 5.2 Wheel-Leg Test Results ........................................................................................................ 73 5.3 Lab Runs Results .................................................................................................................. 75 Tracks ..................................................................................................................................... 75 Wheel-legs ............................................................................................................................. 76 Chapter 6: Conclusions and Future Work ...................................................................................... 79 6.1 Conclusions .......................................................................................................................... 79 6.2 Future Work ......................................................................................................................... 79 Appendix A. Motor calculations ................................................................................................. 81 Torque Calculations ............................................................................................................... 81 Speed Calculations ................................................................................................................. 82 Motor Chosen ........................................................................................................................ 82 Appendix B. Shaft Calculations ................................................................................................... 83 Weight Calculations ............................................................................................................... 83 Torque Calculations ............................................................................................................... 84 Works Cited .................................................................................................................................... 87 2 List of Tables Table 2.1 Power to mass ratio, and speed comparison of several legged robots (Allen, 2004), (Saranli, Buehler, & Koditschek, 2001). ...................................................................... 18 Table 5.1 - Comparison of WhegsTM I, WhegsTM II, Lunar WhegsTM, and USAR WhegsTM across several areas of interest ............................................................................................. 72 Table 5.2 - Weight of various sections of USAR WhegsTM .................................................... 72 Table 5.3 - Vertical displacement of center for all 3 specimens in position 1 ....................... 73 Table 5.4 - Horizontal displacement of center for all 3 specimens in position 1 ................... 73 Table 5.5 - Vertical displacement of all 3 specimens in position 2 ........................................ 74 Table A.1 - Data of Maxon RE 40, 150W motor and 26:1 planetary gear set ........................ 82 3 List of Figures Figure 1.1 - Comparison of ground compaction between a wheel and a leg (Martin-Alvarez, De Peuter, Hillebrand, Putz, Matthyssen, & de Weerd, 1996). ...................................................... 14 Figure 2.1 - Prolero .................................................................................................................... 16 Figure 2.2 – Rhex (Saranli, Buehler, & Koditschek, 2001) .......................................................... 17 Figure 2.3 - WhegsTM II ............................................................................................................. 19 Figure 2.4 - Dagsi WhegsTM ...................................................................................................... 20 Figure 2.5- Inuken VGTV shown in three positions ................................................................... 21 Figure 2.6 - iRobot Packbot with arm and camera extensions .................................................. 22 Figure 2.7 - OmniTread OT-8 climbing stairs ............................................................................. 23 Figure 2.8 - SAM8 on Packbot .................................................................................................... 24 Figure 3.1 - Robot in final configuration next to graduate student ........................................... 25 Figure 3.2 - Comparison of a wheel and a wheel-leg climbing an obstacle (Allen, 2004). ........ 26 Figure 3.3 - Fastest climbing configurations for (a) 1.0r, (b) 1.1r, (c) 1.2r and
Recommended publications
  • Remote and Autonomous Controlled Robotic Car Based on Arduino with Real Time Obstacle Detection and Avoidance
    Universal Journal of Engineering Science 7(1): 1-7, 2019 http://www.hrpub.org DOI: 10.13189/ujes.2019.070101 Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance Esra Yılmaz, Sibel T. Özyer* Department of Computer Engineering, Çankaya University, Turkey Copyright©2019 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract In robotic car, real time obstacle detection electronic devices. The environment can be any physical and obstacle avoidance are significant issues. In this study, environment such as military areas, airports, factories, design and implementation of a robotic car have been hospitals, shopping malls, and electronic devices can be presented with regards to hardware, software and smartphones, robots, tablets, smart clocks. These devices communication environments with real time obstacle have a wide range of applications to control, protect, image detection and obstacle avoidance. Arduino platform, and identification in the industrial process. Today, there are android application and Bluetooth technology have been hundreds of types of sensors produced by the development used to implementation of the system. In this paper, robotic of technology such as heat, pressure, obstacle recognizer, car design and application with using sensor programming human detecting. Sensors were used for lighting purposes on a platform has been presented. This robotic device has in the past, but now they are used to make life easier. been developed with the interaction of Android-based Thanks to technology in the field of electronics, incredibly device.
    [Show full text]
  • Kinematic Modeling of a Rhex-Type Robot Using a Neural Network
    Kinematic Modeling of a RHex-type Robot Using a Neural Network Mario Harpera, James Paceb, Nikhil Guptab, Camilo Ordonezb, and Emmanuel G. Collins, Jr.b aDepartment of Scientific Computing, Florida State University, Tallahassee, FL, USA bDepartment of Mechanical Engineering, Florida A&M - Florida State University, College of Engineering, Tallahassee, FL, USA ABSTRACT Motion planning for legged machines such as RHex-type robots is far less developed than motion planning for wheeled vehicles. One of the main reasons for this is the lack of kinematic and dynamic models for such platforms. Physics based models are difficult to develop for legged robots due to the difficulty of modeling the robot-terrain interaction and their overall complexity. This paper presents a data driven approach in developing a kinematic model for the X-RHex Lite (XRL) platform. The methodology utilizes a feed-forward neural network to relate gait parameters to vehicle velocities. Keywords: Legged Robots, Kinematic Modeling, Neural Network 1. INTRODUCTION The motion of a legged vehicle is governed by the gait it uses to move. Stable gaits can provide significantly different speeds and types of motion. The goal of this paper is to use a neural network to relate the parameters that define the gait an X-RHex Lite (XRL) robot uses to move to angular, forward and lateral velocities. This is a critical step in developing a motion planner for a legged robot. The approach taken in this paper is very similar to approaches taken to learn forward predictive models for skid-steered robots. For example, this approach was used to learn a forward predictive model for the Crusher UGV (Unmanned Ground Vehicle).1 RHex-type robot may be viewed as a special case of skid-steered vehicle as their rotating C-legs are always pointed in the same direction.
    [Show full text]
  • 1 Design of a Low-Cost, Highly Mobile Urban Search and Rescue Robot
    Design of a Low-Cost, Highly Mobile Urban Search and Rescue Robot Bradley E. Bishop * Frederick L. Crabbe Bryan M. Hudock United States Naval Academy 105 Maryland Ave (Stop 14a) Annapolis, MD 21402 [email protected] Keywords: Rescue Robotics, Mobile Robotics, Locomotion, Physical Simulation, Genetic Algorithms Abstract— In this paper, we discuss the design of a novel robotic platform for Urban Search and Rescue (USAR). The system developed possesses unique mobility capabilities based on a new adjustable compliance mechanism and overall locomotive morphology. The main facets of this work involve the morphological concepts, initial design and construction of a prototype vehicle, and a physical simulation to be used for developing controllers for semi-autonomous (supervisory) operation. I. INTRODUCTION Recovering survivors from a collapsed building has proven to be one of the more daunting challenges that face rescue workers in today’s world. Survivors trapped in a rubble pile generally have 48 hours before they will succumb to dehydration and the elements [1]. Unfortunately, the environment of urban search and rescue (USAR) does not lend itself to speedy reconnaissance or retrieval. The terrain is extremely unstable and the spaces for exploration are often irregular in nature and very confined. Though these challenges often make human rescue efforts deep within the rubble pile prohibitive, a robot designed for urban search and rescue would be very well suited to the problem. Robots have already proven their worth in urban search and rescue, most notably in the aftermath of the September 11 th , 2001 disaster. The combined efforts of Professor Robin Murphy, 1 a computer scientist at the University of South Florida, and Lt.
    [Show full text]
  • AUV Adaptive Sampling Methods: a Review
    applied sciences Review AUV Adaptive Sampling Methods: A Review Jimin Hwang 1 , Neil Bose 2 and Shuangshuang Fan 3,* 1 Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia; [email protected] 2 Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada; [email protected] 3 School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China * Correspondence: [email protected] Received: 16 July 2019; Accepted: 29 July 2019; Published: 2 August 2019 Abstract: Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed. Keywords: autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion 1. Introduction Autonomous underwater vehicles (AUVs) are unmanned marine robots. Owing to their mobility and increased ability to accommodate sensors, they have been used for a broad range of oceanographic missions, such as surveying underwater plumes and other phenomena, collecting bathymetric data and tracking oceanographic dynamic features.
    [Show full text]
  • Towards Pronking with a Hexapod Robot
    Towards pronking with a hexapod robot Dave McMordie Department of Electrical Engineering McGill University, Montreal, Canada July, 2002 A thesis submitted to the Faculty of Graduate 5tudies and Research in partial fulfillment of the requirements of the degree Master of Engineering © Dave McMordie, 2002 National Library Bibliothèque nationale 1+1 of Canada du Canada Acquisitions and Acquisisitons et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue Wellington Ottawa ON K1A DN4 Ottawa ON K1A DN4 Canada Canada Your file Votre référence ISBN: 0-612-85890-1 Our file Notre référence ISBN: 0-612-85890-1 The author has granted a non­ L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/film, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts from it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou aturement reproduits sans son permission. autorisation. Canada Abstract RHex is a robotic hexapod with springy legs and just six actuated degrees of freedom. This work presents the development of a pronking gait for RHex, extending its efficiency and speed by exploiting its springy legs for hopping.
    [Show full text]
  • Development of an Open Humanoid Robot Platform for Research and Autonomous Soccer Playing
    Development of an Open Humanoid Robot Platform for Research and Autonomous Soccer Playing Karl J. Muecke and Dennis W. Hong RoMeLa: Robotics and Mechanisms Lab Virginia Tech Blacksburg, VA 24061 [email protected], [email protected] Abstract This paper describes the development of a fully autonomous humanoid robot for locomotion research and as the first US entry in to RoboCup. DARwIn (Dynamic Anthropomorphic Robot with Intelligence) is a humanoid robot capable of bipedal walking and performing human like motions. As the years have progressed, DARwIn has evolved from a concept to a sophisticated robot platform. DARwIn 0 was a feasibility study that investigated the possibility of making a humanoid robot walk. Its successor, DARwIn I, was a design study that investigated how to create a humanoid robot with human proportions, range of motion, and kinematic structure. DARwIn IIa built on the name ªhumanoidº by adding autonomy. DARwIn IIb improved on its predecessor by adding more powerful actuators and modular computing components. Finally, DARwIn III is designed to take the best of all the designs and incorporate the robot's most advanced motion control yet. Introduction Dynamic Anthropomorphic Robot with Intelligence (DARwIn), a humanoid robot, is a sophisticated hardware platform used for studying bipedal gaits that has evolved over time. Five versions of DARwIn have been developed, each an improvement on its predecessor. The first version, DARwIn 0 (Figure 1a), was used as a design study to determine the feasibility of creating a humanoid robot abd for actuator evaluation. The second version, DARwIn I (Figure 1b), used improved gaits and software.
    [Show full text]
  • Design and Control of a Large Modular Robot Hexapod
    Design and Control of a Large Modular Robot Hexapod Matt Martone CMU-RI-TR-19-79 November 22, 2019 The Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA Thesis Committee: Howie Choset, chair Matt Travers Aaron Johnson Julian Whitman Submitted in partial fulfillment of the requirements for the degree of Master of Science in Robotics. Copyright © 2019 Matt Martone. All rights reserved. To all my mentors: past and future iv Abstract Legged robotic systems have made great strides in recent years, but unlike wheeled robots, limbed locomotion does not scale well. Long legs demand huge torques, driving up actuator size and onboard battery mass. This relationship results in massive structures that lack the safety, portabil- ity, and controllability of their smaller limbed counterparts. Innovative transmission design paired with unconventional controller paradigms are the keys to breaking this trend. The Titan 6 project endeavors to build a set of self-sufficient modular joints unified by a novel control architecture to create a spiderlike robot with two-meter legs that is robust, field- repairable, and an order of magnitude lighter than similarly sized systems. This thesis explores how we transformed desired behaviors into a set of workable design constraints, discusses our prototypes in the context of the project and the field, describes how our controller leverages compliance to improve stability, and delves into the electromechanical designs for these modular actuators that enable Titan 6 to be both light and strong. v vi Acknowledgments This work was made possible by a huge group of people who taught and supported me throughout my graduate studies and my time at Carnegie Mellon as a whole.
    [Show full text]
  • Learning Preference Models for Autonomous Mobile Robots in Complex Domains
    Learning Preference Models for Autonomous Mobile Robots in Complex Domains David Silver CMU-RI-TR-10-41 December 2010 Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, 15213 Thesis Committee: Tony Stentz, chair Drew Bagnell David Wettergreen Larry Matthies Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Robotics Copyright c 2010. All rights reserved. Abstract Achieving robust and reliable autonomous operation even in complex unstruc- tured environments is a central goal of field robotics. As the environments and scenarios to which robots are applied have continued to grow in complexity, so has the challenge of properly defining preferences and tradeoffs between various actions and the terrains they result in traversing. These definitions and parame- ters encode the desired behavior of the robot; therefore their correctness is of the utmost importance. Current manual approaches to creating and adjusting these preference models and cost functions have proven to be incredibly tedious and time-consuming, while typically not producing optimal results except in the sim- plest of circumstances. This thesis presents the development and application of machine learning tech- niques that automate the construction and tuning of preference models within com- plex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback.
    [Show full text]
  • Uncorrected Proof
    JrnlID 10846_ ArtID 9025_Proof# 1 - 14/10/2005 Journal of Intelligent and Robotic Systems (2005) # Springer 2005 DOI: 10.1007/s10846-005-9025-1 Temporal Range Registration for Unmanned 1 j Ground and Aerial Vehicles 2 R. MADHAVAN*, T. HONG and E. MESSINA 3 Intelligent Systems Division, National Institute of Standards and Technology, Gaithersburg, MD 4 20899-8230, USA; e-mail: [email protected], {tsai.hong, elena.messina}@nist.gov 5 (Received: 24 March 2004; in final form: 2 September 2005) 6 Abstract. An iterative temporal registration algorithm is presented in this article for registering 8 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured 9 environments. We are primarily motivated by the development of 3D registration algorithms to 10 overcome both the unavailability and unreliability of Global Positioning System (GPS) within 11 required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable 12 modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is 13 shown to be robust to outliers and false matches during the registration of successive range images 14 obtained from a scanning LADAR rangefinder on the UGV. Towards registering LADAR images 15 from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain 16 being traversed, we then propose a hybrid registration approach. In this approach to air to ground 17 registration to estimate and update the position of the UGV, we register range data from two 18 LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. 19 Registration of range data guarantees an estimate of the vehicle’s position even when only one of 20 the vehicles has GPS information.
    [Show full text]
  • Multipurpose Tactical Robot
    Engineering International, Volume 2, No 1 (2014) Asian Business Consortium | EI Page 20 Engineering International, Volume 2, No 1 (2014) Multipurpose Tactical Robot Md. Taher-Uz-Zaman1, Md. Sazzad Ahmed2, Shabbir Hossain3, Shakhawat Hossain4, & G. R. Ahmed Jamal5 1,2,3,4Department of Electrical & Electronic Engineering, University of Asia Pacific, Bangladesh 5Assistant Professor, Dept. of Electrical & Electronic Engineering, University of Asia Pacific, Bangladesh ABSTRACT This paper presents a general framework for planning a multipurpose robot which can be used in multiple fields (both civil and military). The framework shows the assembly of multiple sensors, mechanical arm, live video streaming and high range remote control and so on in a single robot. The planning problem is one of five fundamental challenges to the development of a real robotic system able to serve both purposes related to military and civil like live surveillance(both auto and manual), rescuing under natural disaster aftermath, firefighting, object picking, hazard like ignition, volatile gas detection, exploring underground mine or even terrestrial exploration. Each of the four other areas – hardware design, programming, controlling and artificial intelligence are also discussed. Key words: Robotics, Artificial intelligence, Arduino, Survelience, Mechanical arm, Multi sensoring INTRODUCTION There are so many robots developed based on line follower, obstacle avoider, robotic arm, wireless controlled robot and so on. Most of them are built on the basis of a specific single function. For example a mechanical arm only can pick an object inside its reach. But what will happen if the object is out of its reach? Besides implementing a simple AI (e.g. line following, obstacle or edge detection) is more or less easy but that does not serve any human need.
    [Show full text]
  • Dynamically Diverse Legged Locomotion for Rough Terrain
    Dynamically diverse legged locomotion for rough terrain The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Byl, Katie, and Russ Tedrake. “Dynamically Diverse Legged Locomotion for Rough Terrain.” Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe International Conference Center, Kobe, Japan, May 12-17, 2009 1607-1608. © Copyright 2009 IEEE. As Published http://dx.doi.org/10.1109/ROBOT.2009.5152757 Publisher Institute of Electrical and Electronics Engineers Version Final published version Citable link http://hdl.handle.net/1721.1/64752 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. 2009 IEEE International Conference on Robotics and Automation Kobe International Conference Center Kobe, Japan, May 12-17, 2009 Dynamically Diverse Legged Locomotion for Rough Terrain Katie Byl and Russ Tedrake Abstract— In this video, we demonstrate the effectiveness of which aim toward precise foot placement often traverse a kinodynamic planning strategy that allows a high-impedance terrain by using relatively slow, deliberate gaits [3], [4], [10], quadruped to operate across a variety of rough terrain. At one [8], [6]. In this video we demonstrate a control approach extreme, the robot can achieve precise foothold selection on intermittent terrain. More surprisingly, the same inherently- which can achieve both precise foot placement, as illustrated stiff robot can also execute highly dynamic and underactuated in Figure 1, and highly dynamic and fast motions, such as motions with high repeatability.
    [Show full text]
  • Design of a Multi-Directional Variable Stiffness Leg for Dynamic Running
    University of Pennsylvania ScholarlyCommons Departmental Papers (ESE) Department of Electrical & Systems Engineering 2007 DESIGN OF A MULTI-DIRECTIONAL VARIABLE STIFFNESS LEG FOR DYNAMIC RUNNING Kevin C. Galloway University of Pennsylvania, [email protected] Jonathan E. Clark Florida State University, [email protected] Daniel E. Koditschek University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/ese_papers Part of the Engineering Commons Recommended Citation Kevin C. Galloway, Jonathan E. Clark, and Daniel E. Koditschek, "DESIGN OF A MULTI-DIRECTIONAL VARIABLE STIFFNESS LEG FOR DYNAMIC RUNNING", . January 2007. @inproceedings{ Galloway.07, Author = {Galloway, K.C. and Clark, J.E. and Koditschek, D.E.}, Title = {Design of a Multi-Directional Variable Stiffness Leg for Dynamic Runnings}, BookTitle = {ASME Int. Mech. Eng. Congress and Exposition}, Year = {2007}} This paper is posted at ScholarlyCommons. https://repository.upenn.edu/ese_papers/508 For more information, please contact [email protected]. DESIGN OF A MULTI-DIRECTIONAL VARIABLE STIFFNESS LEG FOR DYNAMIC RUNNING Abstract Recent developments in dynamic legged locomotion have focused on encoding a substantial component of leg intelligence into passive compliant mechanisms. One of the limitations of this approach is reduced adaptability: the final leg mechanism usually performs optimally for a small range of conditions (i.e. a certain robot weight, terrain, speed, gait, and so forth). For many situations in which a small locomotion system experiences a change in any of these conditions, it is desirable to have a variable stiffness leg to tune the natural frequency of the system for effective gait control. In this paper, we present an overview of variable stiffness leg spring designs, and introduce a new approach specifically for autonomous dynamic legged locomotion.
    [Show full text]