220 シ ス テ ム 制 御 情 報 学 会 論 文 誌,Vol. 19, No. 6, pp. 220-232, 2006 論 文 Exploring the Analogy in the Emergent Proper- ties of Locomotion Gaits of Snakebot Adapted to Challenging Terrain and Partial Damage * Ivan Tanev †§ ・Thomas Ray ‡ ・Katsunori Shimohara § Exploring the Analogy in the Emergent Properties of Locomotion Gaits of Snakebot Adapted to Challenging Terrain and Partial Damage * Ivan TANEv †§, Thomas RAY ‡ and Katsunori SHIMoHARA § The objective of this work is to investigate the emergent properties of the gaits of the simulated snake-like robot, Snakebot. The gaits are automatically designed through Genetic Programming (GP) to be robust, general, adaptive, and the fastest possible sidewinding, locomotion. Considering the notion of emergent intelligence as the ability of Snakebot to achieve its goals (of moving fast) without the need to be explicitly taught how to do so, we present empirical results demonstrating the emergence of sidewinding locomotion from relatively simple motion patterns of morphologi- cal segments of Snakebot. We discuss the emergent properties of the evolved robust high velocity sidewinding locomotion gaits of Snakebot when situated in challenging environments. Then we elab- orate on the ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics, and the emergent properties of obtained adaptive gaits . Verifying the practical im- plications of the analogy between the emergent properties of the robust and the adaptive sidewinding gaits, this work could be viewed as a step towards building real Snakebots, which are able to perform robustly in challenging environments. 1. Introduction nisms [4,21]. Robots with these properties open up Wheelless, limbless snake-like robots (Snakebots) several critical applications in exploration, reconnais- feature potential robustness characteristics beyond the sance, medicine and inspection. However, compared capabilities of most wheeled and legged vehicles - abil- to the wheeled and legged vehicles, Snakebots feature ity to traverse terrain that would pose problems for (i) smaller payload, (ii) more difficult thermal control, traditional wheeled or legged robots, and insignificant (iii) more difficult control of locomotiongaits and (iv) inferior speed characteristics. performance degradation when partial damage is in- flicted. Moreover, due to their modular design, Snake- Consideringthe first two drawbacks as beyond the bots may be cheaper to build, maintain and repair. scope of our work, and focusing on the issues of con- Some useful features of Snakebots include smaller size trol and speed, we intend to address the following of the cross-sectional areas, stability, ability to op- challenge: how to develop control sequencesof Snake- erate in difficult terrain, good traction, high redun- bot's actuators, which allow for the fastest possible dancy, and complete sealing of the internal mecha- speed of locomotion achievable with Snakebot mor- phology. * 原 稿 受 付2005年3月14日 For many tasks and robot morphologies,it might † 同 志 社 大 学Department of Information Systems De - be seen as a natural approach to handcraft the lo- comotion control code by applying various theoreti- sign, Doshisha University; 1-3 Miyakodani, Tatara, Ky- cal approaches [3,5,22]. However,handcrafting might otanabe, Kyoto 610-0321, JAPAN not be feasible for developing the control code of a ‡ Department of Zoology, 730 Van Vleet Oval , Room real Snakebot due to its morphological complexity 314, University of Oklahoma Norman, Oklahoma 73019, and the anticipated need of prompt adaptation under USA § ATRネ ッ ト ワ ー ク 情 報 学 研 究 所ATR Network In degraded mechanical abilities and/or unanticipated - environmental conditions. Moreover, while fast lo- formatics Laboratories; 2-2-2 Hikaridai, "Keihanna Sci- comotion gait might emerge from relatively simply ence City", Kyoto 619-0288, JAPAN defined motion patterns of morphologicalsegments of Key Words : emergence, Snakebot, evolution, adaptation . 10 TANEV•ERAY•ESHImoHARA:Exploring the Analogy in the Emergent Properties of Locomotion Gaits of Snakebot•@ 221 Snakebot, the natural implication of the phenomenon bility of success for various runtime intervals is of emergence in complex systems is that neither the applied as a characteristic of computational ef- degree of optimality of the developed code nor the ficiency of GP. The non-determinism of GP is way to incrementally improve the code is evident to viewed as a natural consequence of the stochas- the human designer [13]. Thus, an automated, holis- tic nature of both the way of creating the initial tic approach for evaluation and incremental optimiza- population of potential solutions to the problem tion of the intermediate solution(s) are needed (e.g. and the genetic operations applied to evolve this based on various models of learning or evolution in population (crossover,mutation, and selection), Nature) [6,12,19].The proposed approach of employ- and ing Genetic Programming (GP) implies that the code, (3) Often the solution, automatically obtained via which governs the locomotionof Snakebot is automat- GP is quite complex and difficult to be com- ically designed by a computer system via simulated prehended by a human designer. Consequently, evolution through selection and survival of the fittest even simple man-made modification to such a in a way similar to the evolution of species in the na- solution is not a straightforward task. ture [8,9]. The use of an automated process to design As an instance of evolutionary algorithms, Genetic the control code opens the possibility of creating a Algorithms (GA) differ from GP mainly in the geno- solution that would be better than one designed by a typic representation (i.e. chromosome) of potential human [10]. solutions. Instead of representing the solution as a In principle, the task of designingthe code of Snake- computer program (usually-a parsing tree) featur- bot could be formalized and the formal mathematical ing arbitrary structure and complexity as in GP, GA models incorporated into direct programmable con- employs a fixed-length linear chromosome. This dif- trol strategies. However,the eventual models would ference implies a favorable computational advantage feature enormous complexity and such models are not of GA over GP for simple problems, because the linear recognizedto have a known, analytically obtained ex- chromosomes are computationally efficiently manip- act optimal solution. The complexity of the model ulated by genetic operations and interpreted by fit- stems from the considerableamount of degrees of free- ness evaluation routines. For complex tasks however dom of the Snakebot, which cannot be treated inde- (such as evolution of locomotion gaits of Snakebot) pendently of each other. The dynamic patterns of the the runtime overhead associated with the manipula- position, orientation, velocity vectors, and moreover, tion of genotype is negligible compared to the much the points and times of contact with the surface (and more significant overhead of the fitness evaluation of consequently-the vectors of resulting traction forces, the evolved (simulated or real) artifact in the (simu- which propel the Snakebot) of each of the morpholog- lated or real) environment. Moreover, an efficient GA ical segments of Snakebot has to be considered within (in terms of computational effort, or number of fitness the context of other segments. Furthermore, often the evaluations) often requires incorporation of extremely dynamic patterns of these parameters cannot be de- computationally heavy probabilistic learning models terministically inferred from the desired velocity char- aimed at maintaining the complex inter-relations be- acteristics of the locomotionof Snakebot. Instead, the tween the variables in the chromosome. In addition, locomotion of the Snakebot is viewed as an emergent the fixed-length chromosome usually implies that the property at higher level of consideration of a com- latter comprises various, carefully encoded problem- plex hierarchical system, comprising many relatively domain-dependent parameters of the solution with an simply defined entities (morphological segments). In a priori known structure and complexity. This might such systems the higher-levelproperties of the system be a concern when no such knowledge is available in and the lower-levelproperties of comprising entities advance, but rather needs to be automatically and au- cannot be induced from each other. GP (and evo- tonomously discovered by the evolving artifact. The lutionary algorithms in general) is considered as an latter is especially true when the artifact has to per- efficient way to tackle such ill-posed problems due to form in unanticipated, uncertain environmental con- the ability of GP to find a near-optimal solution in ditions or under its own (possibly degraded) mechan- a reasonable runtime. This ability often overcompen- ical abilities. sates the drawbacks of GP, which can be summarized Evolving a Snakebot's locomotion (and in general, as follows: behavior of any robot) by applying GP could be per- (1) Relatively long runtime stemming from the sig- formed as a first step in the sequence of simulated nificant computational effort (many potential off-line evolution (phylogeneticlearning) on the soft- solutions need to be evaluated before the suf- ware model, followedby on-line adaptation (ontoge- ficiently good solution is discovered) and poor netic learning) of evolved code on a physical robot computational performance (often the