Interactive, iterative robot design

Consider how a new robot is designed. Starting from a relative size (e.g., nanometer, centimeter, meter), the roboticist picks a morphological type (manipulator, quadruped, biped), and then uses intuition, experience, biological inspiration, or some combination of the three to select kinematic, dynamic, and geometric parameters. The designer then conducts preliminary checks to see whether the robot can satisfy its intended function: manipulation, locomotion, or both. The designer then iteratively adjusts the physical parameters and conducts checks using sample tasks, presumably until convergence is reached. This paper describes a means to automate part of this approach by combining interactive elements with powerful tools that use multi-rigid body simulation. We describe and demonstrate a virtual testing phase that first determines whether the physically situated robot would serve its intended purpose. If the robot is not capable of performing its target task, the virtual testing phase can determine which of the robot's morphological parameters should be modified in order to do so. The process keeps a human in the loop to help account for hard to quantify design aspects like appearance, quirks of the fabrication procedure (i.e., laser cutting, milling, 3D printing processes), or even expert knowledge. We intend for the described approach to be used as an interactive tool that gives a robot designer feedback on what morphological parameters are likely to limit the performance of a robot and how to modify the design to fix or offset such limitations. We demonstrate that, through simulated prototyping and testing methods, we can improve a robot design and iteratively locate morphological parameters that make efficient use of available hardware.

[1]  Peter Secretan Learning , 1965, Mental Health.

[2]  Markus H. Gross,et al.  Interactive design of 3D-printable robotic creatures , 2015, ACM Trans. Graph..

[3]  B. Brogliato Nonsmooth Impact Mechanics: Models, Dynamics and Control , 1996 .

[4]  Gregory J. Barlow,et al.  Article in Press Robotics and Autonomous Systems ( ) – Robotics and Autonomous Systems Fitness Functions in Evolutionary Robotics: a Survey and Analysis , 2022 .

[5]  Jun Nakanishi,et al.  A unifying framework for robot control with redundant DOFs , 2007, Auton. Robots.

[6]  Jun Nakanishi,et al.  A Unifying Methodology for Robot Control with Redundant DOFs , 2008 .

[7]  Joshua Evan Auerbach,et al.  On the Relationship Between Environmental and Mechanical Complexity in Evolved Robots , 2012, ALIFE.

[8]  Evan Drumwright,et al.  Quadratic programming-based inverse dynamics control for legged robots with sticking and slipping frictional contacts , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Evan Drumwright,et al.  Rapidly computable viscous friction and no-slip rigid contact models , 2015, ArXiv.

[10]  Katie Byl,et al.  More solutions means more problems: Resolving kinematic redundancy in robot locomotion on complex terrain , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Marc Toussaint,et al.  Creating Brain-Like Intelligence: From Principles to Complex Intelligent Systems , 2009 .

[12]  David E. Stewart,et al.  Rigid-Body Dynamics with Friction and Impact , 2000, SIAM Rev..

[13]  Thomas R. Kane,et al.  THEORY AND APPLICATIONS , 1984 .

[14]  K. Mombaur,et al.  Modeling and Optimal Control of Human-Like Running , 2010, IEEE/ASME Transactions on Mechatronics.

[15]  Inman Harvey,et al.  Why Morphology Matters , 2014 .

[16]  Stefan Schaal,et al.  Learning, planning, and control for quadruped locomotion over challenging terrain , 2011, Int. J. Robotics Res..

[17]  Darwin G. Caldwell,et al.  A reactive controller framework for quadrupedal locomotion on challenging terrain , 2013, 2013 IEEE International Conference on Robotics and Automation.

[18]  Dong Jin Hyun,et al.  On the dynamics of a quadruped robot model with impedance control: Self-stabilizing high speed trot-running and period-doubling bifurcations , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Parviz E. Nikravesh,et al.  Computer-aided analysis of mechanical systems , 1988 .

[20]  Roland Siegwart,et al.  Concurrent Optimization of Mechanical Design and Locomotion Control of a Legged Robot , 2014 .

[21]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[22]  Claude Sammut,et al.  Omnidirectional Locomotion for Quadruped Robots , 2001, RoboCup.