This paper proposes a method for task based design of modular robotic systems using Genetic Algorithms (GA). We introduce a 30 kinematic description for modular serial manipulators and a two-level GA to optimize their topology from task specifications. Revolute and prismatic joints are considered and the number of DOF is let to the G A to determine (allowing redundant manipulators). The upper-level G A is dedicated to the topology evolution and uses the lower-level G A to search for inverse kinematics problem solutions. The topology is evolved for adaptation to a global task constituted by several required end effector configurations (subtasks). The implemented G A optimizes severalperformance criteria under constraints. To illustrate the method capacities, an example is presented for a redundant manipulator an Q cluttered workspace.

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SUMMARYThis paper proposes a method for task based design of modular serial robotic arms using evolutionary algorithms (EA). We introduce a 3D kinematics and a global optimization for both topology and configuration from task specifications. The search features revolute as well as prismatic joints and any number of DOF to build up a solution without using any design knowledge. A study of the evolution dynamics gives some keys to set evolution parameters that enable artificial evolution. An adapted algorithm dealing with the topology/configuration search tradeoff is proposed, descibed, and discussed. Illustrations of the algorithms results are given and conclusions are drawn from their analysis. Perspectives of this work are given, extending its reach to control and complex system design.

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