While significant recent progress has been made in development of mobile robots for planetary surface exploration, there remain major challenges. These include increased autonomy of operation, traverse of challenging terrain, and fault-tolerance under long, unattended periods of use. We have begun work which addresses some of these issues, with an initial focus on problems of “high risk access, ” that is, autonomous roving over highly variable, rough terrain. This is a dual problem of sensing those conditions which require rover adaptation, and controlling the rover actions so as to implement this adaptation in a well understood way (relative to metrics of rover stability, traction, power utilization, etc.). Our work progresses along several related technical lines: 1) development a fused state estimator which robustly integrates internal rover state and externally sensed environmental information to provide accurate “configuration ” information; 2) kinematic and dynamical stability analysis of such configurations so as to determine “predicts ” for a needed change of control regime (e.g., traction control, active c.g. positioning, rover shoulder stance/pose); 3) definition and implementation of a behavior-based control architecture and action-selection strategy which autonomously sequences multi-level rover controls and reconfiguration. We report on these developments, both software simulations and hardware experimentation. Experiments include reconfigurable control of JPL's Sample Return Rover geometry and motion during its autonomous traverse over simulated Mars terrain.

Abstract—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. Index Terms—Design automation, evolutionary robotics, generative representations. I.

Autonomy is a key enabling factor in the advancement of robotics for remote exploration. As the level of resources devoted to this effort continues to increase, it has become equally important to provide simulation tools and environments to scientists in which to test the autonomy algorithms. While industrial robotics benefits from a variety of high quality simulation tools, researchers developing autonomy software are still dependent primarily on block-world simulations. The Mission Simulation Facility (MSF) project addresses this shortcoming by providing a simulation toolkit that will enable developers of autonomous control systems to test their systems ’ performance against integrated, standardized simulations of NASA mission scenarios. The MSF provides a distributed architecture that connects the autonomous system to a set of simulated components replacing the robot hardware and its environment. 1

© 1998 Carnegie Mellon University

Optimization (EMO) algorithm based on differential evolution is proposed for evolving locomotion controllers in an artificially embodied legged creature. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost dramatically. Moreover, the performance of our proposed Pareto EMO algorithm was found to be comparable against a current state-of-the-art Pareto EMO algorithm, the NSGA-II algorithm, for evolving legged locomotion controllers. 1

We present Darwin2K, a widely-applicable, extensible software tool for synthesizing and optimizing robot configurations. The system uses an evolutionary optimization algorithm that is independent of task, metrics, and type of robot, enabling the system to address a wide range of synthesis problems. Darwin2K can synthesize fixed-base and mobile robots (including free-flying robots, mobile manipulators, modular robots, and multiple or bifurcated manipulators), and includes a toolkit of simulation and analysis algorithms which are useful for many synthesis tasks. Some of these capabilities, such as dynamic simulation, are novel in automated synthesis of robots. An extensible software architecture enables new synthesis tasks to be addressed while maximizing use of existing system capabilities; this

This paper presents a knowledge-intensive support paradigm for platform-based product family design and development. The fundamental issues underlying the product family design and development, including product platform and product family modeling, product family generation and evolution, and product family evaluation for customization, are discussed. A module-based integrated design scheme is proposed with knowledge support for product family architecture modeling, product platform establishment, product family generation, and product variant assessment. A systematic methodology and the relevant technologies are investigated and developed for knowledge supported product family design process. The developed information and knowledge-modeling framework and prototype system can be used for platform product design knowledge capture, representation and management and offer on-line support for designers in the design process. The issues and requirements related to developing a knowledge-intensive support system for modular platform based product family design are also addressed.

Mass customization and global economic collaboration drive the product development and management beyond internal enterprise to cover the whole product value chain. This paper presents a platform-based strategy and approach for collaborative product development and customization. The implementation of this strategy takes 1) the product platform as the core, 2) the view/search engine and rule-based control as the data access and navigation mechanism, and 3) the internetenabled web-based integration and collaboration bus as an enabler to allow participants involved in the product lifecycle to access into both internal and external enterprise resources, applications, and services. In the paper, a generic collaborative platform design and development process model is presented for product family design and mass customization. Based on this model, a module-based integrated & distributed collaborative framework for product family design and mass customization is developed with knowledge intensive support for customer or task requirements ' modeling, product architecture modeling, product platform establishment, product family generation, and product variant assessment for customization. The issues related to the high-level information & knowledge modeling and the development of knowledgeintensive collaborative support framework are addressed. Finally, a case study for collaborative design of families of modular robotic systems is given.

This thesis entitled “Design of a Physics Abstraction Layer for

any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from