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Design and Workspace Characterisation of Malleable Robots

Angus B. Clark, Nicolas Rojas

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Abstract

For the majority of tasks performed by traditional serial robot arms, such as bin picking or pick and place, only two or three degrees of freedom (DOF) are required for motion; however, by augmenting the number of degrees of freedom, further dexterity of robot arms for multiple tasks can be achieved. Instead of increasing the number of joints of a robot to improve flexibility and adaptation, which increases control complexity, weight, and cost of the overall system, malleable robots utilise a variable stiffness link between joints allowing the relative positioning of the revolute pairs at each end of the link to vary, thus enabling a low DOF serial robot to adapt across tasks by varying its workspace. In this paper, we present the design and prototyping of a 2-DOF malleable robot, calculate the general equation of its workspace using a parameterisation based on distance geometry-suitable for robot arms of variable topology, and characterise the workspace categories that the end effector of the robot can trace via reconfiguration. Through the design and construction of the malleable robot we explore design considerations, and demonstrate the viability of the overall concept. By using motion tracking on the physical robot, we show examples of the infinite number of workspaces that the introduced 2-DOF malleable robot can achieve.

Topics & Concepts

WorkspaceRobotRevolute jointRobot end effectorArm solutionFlexibility (engineering)Computer scienceDegrees of freedom (physics and chemistry)Robot controlSerial manipulatorControl engineeringVariable (mathematics)RoboticsArtificial intelligenceArticulated robotBang-bang robotSimulationTRACE (psycholinguistics)EngineeringRobot kinematicsRobotic armGrippersComputer visionTracking (education)Mobile robotSocial robotConstraint (computer-aided design)CrawlingStiffnessModular designCartesian coordinate robotTrajectoryRobot calibrationKinematicsSoft Robotics and ApplicationsModular Robots and Swarm IntelligenceRobot Manipulation and Learning