The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic evolutionary processes. However, it is not clear if variation-based search processes can attain the complexity of design necessary for practical engineering of robots. Here, we demonstrate an automatic design system that produces complex robots by exploiting the principles of regularity, modularity, hierarchy, and reuse. These techniques are already established principles of scaling in engineering design and have been observed in nature, but have not been broadly used in artificial evolution. We gain these advantages through the use of a generative representation, which combines a programmatic representation with an algorithmic process that compiles the representation into a detailed construction plan. This approach is shown to have two benefits: it can reuse components in regular and hierarchical ways, providing a systematic way to create more complex modules from simpler ones; and the evolved representations can capture intrinsic properties of the design space, so that variations in the representations move through the design space more effectively than equivalent-sized changes in a nongenerative representation. Using this system, we demonstrate for the first time the evolution and construction of modular, three-dimensional, physically locomoting robots, comprising many more components than previous work on body-brain evolution.

/pdf/generative-representations-for-the-automated-design-of-izn8mlfuyv.pdf

Generative representations for the automated design of modular physical robots

Underwater Robots: Motion and Force Control of Vehicle-Manipulator Systems

Describes the development and verification of a six degree of freedom, non-linear simulation model for the REMUS AUV, the first such model for this platform. In this model, the external forces and moments resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the control inputs of the vehicle propeller and fins are all defined in terms of vehicle coefficients. The paper briefly describes the derivation of these coefficients. The equations determining the coefficients, as well as those describing the vehicle rigid-body dynamics, are left in non-linear form to better simulate the inherently non-linear behavior of the vehicle. Simulation of the vehicle motion is achieved through numeric integration of the equations of motion. The simulator output is then verified against vehicle dynamics data collected in experiments performed at sea. The simulator is shown to accurately model the motion of the vehicle. The paper concludes with recommendations for future model validation experiments.

Development of a Six-Degree of Freedom Simulation Model for the REMUS autonomous Underwater Vehicle

Stable bipedal locomotion has been achieved using coupled evolution of morphology and control of a 5-link biped robot in a physics-based simulation environment. The robot was controlled by a closed loop recurrent neural network controller. The goal was to study the effect of macroscopic, midrange and microscopic changes in mass distribution along the biped skeleton to ascertain whether optimal morphology and control pairs could be discovered. The sensor-motor coupling determined that small changes in morphology manifest themselves as large changes in the performance of the biped, which were exploited by the optimization process. In this way, mechanical design and controller optimization were reduced to a single process, and more mutually optimized designs resulted. This work points to alternative routes for efficient automated and manual biped optimization.

The road less travelled: morphology in the optimization of biped robot locomotion

Robot configuration design is hampered by the lack of established, well-known design rules, and designers cannot easily grasp the space of possible designs and the impact of all design variables on a robot’s performance. Realistically, a human can only design and evaluate several candidate configurations, though there may be thousands of competitive designs that should be investigated. In contrast, an automated approach to configuration synthesis can create tens of thousands of designs and measure the performance of each one without relying on previous experience or design rules. This thesis creates Darwin2K, an extensible, automated system for robot configuration synthesis. This research focuses on the development of synthesis capabilities required for many robot design problems: a flexible and effective synthesis algorithm, useful simulation capabilities, appropriate representation of robots and their properties, and the ability to accomodate application-specific synthesis needs. Darwin2K can synthesize and optimize kinematics, dynamics, structural geometry, actuator selection, and task and control parameters for a wide range of robots. Darwin2K uses an evolutionary algorithm to synthesize robots, and utilizes two new multi-objective selection procedures that are applicable to other evolutionary design domains. The evolutionary algorithm can effectively optimize multiple performance objectives while satisfying multiple performance constraints, and can generate a range of solutions representing different trade-offs between objectives. Darwin2K uses a novel representation for robot configurations called the parameterized module configuration graph, enabling efficient and extensible synthesis of mobile robots, of single, multiple and bifurcating manipulators, and of robots with either modular or monolithic construction. Task-specific simulation is used to provide the synthesis algorithm with performance measurements for each robot. Darwin2K can automatically derive dynamic equations for each robot it simulates, enabling dynamic simulation to be used during synthesis for the first time. Darwin2K also includes a variety of simulation components, including Jacobian and PID controllers, algorithms for estimating link deflection and for detecting collisions; modules for robot links, joints (including actuator models), tools, and bases (fixed and mobile); and metrics such as task coverage, task completion time, end effector error, actuator saturation, and link deflection. A significant component of the system is its extensible object-oriented software architecture, which allows new simulation capabilities and robot modules to be added without impacting the synthesis algorithm. The architecture also encourages re-use of existing toolkit components, allowing task-specific simulators to be quickly constructed. Darwin2K’s synthesis algorithm, simulation capabilities, and extensible architecture combine to allow synthesis of robots for a wide range of tasks. Results are presented for nearly 150 synthesis experiments for six different applications, including synthesis of a free-flying 22-DOF robot with multiple manipulators and a walking machine for zerogravity truss walking. The synthesis system and results represent a significant advance in the state-of-the-art in automated synthesis for robotics.

/pdf/automated-synthesis-and-optimization-of-robot-configurations-2707os3mc6.pdf

Automated synthesis and optimization of robot configurations: an evolutionary approach

This paper proposes a method for task based design of modular robotic systems using genetic algorithms (GA). We introduce a 3D 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 GA to determine (allowing redundant manipulators). The upper-level GA is dedicated to the topology evolution and uses the lower-level GA 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 GA optimizes several performance criteria under constraints. To illustrate the method capacities, an example is presented for a redundant manipulator in a cluttered workspace.

Genetic design of 3D modular manipulators

Proposes an adaptative multi-chromosome evolutionary algorithm (AMEA) to perform task based design of modular robotic systems. The kinematic design is optimized by the AMEA which uses both binary and real encoding (for kinematic and configuration parameters). In the problem considered for illustration, the robotic system consists in a mobile base and a manipulator arm which may be built with serially assembled link and joint modules. The manipulator has to reach a predefined set of goal frames in a 3D cluttered environment. Its design is evaluated with geometric and kinematic performance measures. Optimization results for a 3D task are given and compared with a simple genetic algorithm. They clearly show the superiority of the multi-chromosome representation and adaptive operators in term of computing time and criteria optimization performance.

Evolutionary algorithms in kinematic design of robotic systems

This 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.

Evolutionary design of modular robotic arms

This paper presents the concept and design of a new reconfigurable magnetic-coupling thruster (RMCT). Mechanical models, as well as digital and real-world experiments, are proposed to understand the fundamental aspects of such technology. The principle of RMCT is to reorient the propeller, and therefore the thrust vector, to the desired direction without adding or moving any motors. The focus of this study is to propose a new design to solve the problems raised in a previous work on RMCT and to understand how the transmitted coupling torque behaves with regard to the new kinematics of the propeller shaft. After introducing the models underlying this thruster design, we build a numerical simulation and validate it through experimental results. The performance of this design is then discussed based on open-loop trials and its potential integration and control. The conclusion proposes guidelines for developing an underwater prototype that will be designed and tested on real autonomous underwater vehicles.

A Validated Feasibility Prototype for AUV Reconfigurable Magnetic Coupling Thruster

Reconfigurable Magnetic Coupling Thruster (RMCT) is an enabling technology for achieving vectorial thrust on AUVs. This allows us to dispose of dynamic, compliant, and secure propulsion to enhance their agility for demanding locomotion tasks. RMCT can generate and control a thrust vector by redirecting its propeller through the compliance of the magnetic coupling and therefore achieve thrust vectoring for AUVs. In this paper, a new design of RMCT is presented. This new prototype uses a flat geometry magnetic coupling for a better AUV full integrability. The new design provides some additional advantages with regard to its predecessor (spherical RMCT); namely, construction simplicity and better torque transmission. The first characteristic is derived from its flat shapes, which makes it easier to manufacture (cubic magnets) and to isolate (planar air gap between rotors). The second characteristic is due to the fact that now both parts of the magnetic coupling can store magnets (increasing the maximal transmitted torque). This paper proposes a thruster dynamics model, a magnetic coupling model, and shows the use of a dynamic parameter identification method based on algorithms used for robotic manipulators. The validity of the dynamic model and identified parameters is then analyzed by means of experimental tests.

Olivier Chocron

Papers

Genetic design of 3D modular manipulators

Evolutionary algorithms in kinematic design of robotic systems

Evolutionary design of modular robotic arms

A Validated Feasibility Prototype for AUV Reconfigurable Magnetic Coupling Thruster

A Flat Design and a Validated Model for an AUV Reconfigurable Magnetic Coupling Thruster