Jumping Like an Insect: From Biomimetic Inspiration to a Jumping Minirobot Design

Abstract

AbstractLocomotion is a key issue for autonomous robots, moreover if we consider gait efficiency in exploration and monitoring application. Despite the implicit mechanical and kinematic complication, legged locomotion is often preferred to the simpler wheeled version in unstructured environment, e.g., difficult terrains. Focusing on microrobot, lessons from nature often provide us a good insight of profitable solutions and suggest bioinspired design for small legged robots.According to the biological observation experiment, it was found that a specific leg configuration maps the nonlinear muscle-like force into a constant force at feet–ground interface so as to minimize the risk of both leg ruptures and tarsus slippage, which represents an optimum design of jumping insects. That gives us the bionic inspiration to optimize the saltatorial legs by reproducing the dynamic characteristics of insect jumping. Based on this idea, jumping robot prototype GRILLO is designed and tested with different ways.In this chapter, we present the bioinspired design of such a jumping mini robot including the dynamically optimized saltatorial leg which is designed to imitate the characteristics of a real jumping insect, kinematically and dynamically, and proposed to mitigate the peak contact force at tarsus–ground interface during jumping acceleration; the overall design of the jumping robot prototype; and as a part of the biomimetic research, the measuring and comparing of the jumping characteristics between the robot and animal so as to show the dynamic similarity and optimization results between them. The finally energy integrated jumping robot prototype is able to move by continuous jumping, of which a single one reaches 100 mm high and 200 mm long, about twice and four times of its body length respectively.KeywordsAutonomous RobotDrive GearRobot BodyLocomotion ModeRisk AvoidanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.