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Accepted: 2004-07-06, 資料番号: SA1001686000

Exploration missions for small planetary bodies such as asteroids and comets have received significant attentions in recent years. In upcoming missions, it is very important for planetary science to make science-equipped robots hang around the surface of small bodies. In order to provide an exploration robot on the surface of small bodies whose gravity is very small, the authors have researched the mobility under the micro-gravity and have developed a small robot which is specialized in the micro-gravity environment. This paper describes the mobility and the system design of the developed robot and is accompanied with explanation of the strategy how to explore and navigate along the small body surface.

Accepted: 1994-07-18, 資料番号: SA1003273000

The approaches to make an agent generate intelligent actions in the AI field might be roughly categorized into two ways–the classical planning and situated action system. It is well known that each system have its own strength and weakness. However, each system also has its own application field. In particular, most of situated action systems do not directly deal with the logical problem. This paper first briefly mentions the novel action generator to situatedly extract a set of actions, which is likely to help to achieve the goal at the current situation in the relaxed logical space. After performing the action set, the agent should recognize the situation for deciding the next likely action set. However, since the extracted action is an approximation of the action which helps to achieve the goal, the agent could be caught into the deadlock of the problem. This paper proposes the newly developed hybrid architecture to solve the problem, which combines the novel situated action generator with the conventional planner. The empirical result in some planning domains shows that the quality of the resultant path to the goal is mostly acceptable as well as deriving the fast response time, and suggests the correlation between the structure of problems and the organization of each system which generates the action., {"references":["Avrim L. Blum and Merrick L. Furst \"Fast Planning Through Planning\nGraph Analysis\" , Artificial Intelligence, vol. 90, 1997","B. Bonet and H. Geffner, \"Planning as Heuristic Search\" Artificial\nIntelligence, vol. 129, 2001","Jörg Hoffman and Bernard Nebel, \" The FF Planning Systems: Fast Plan\nGeneration Through Heuristic Search\", Journal of Artificial Intelligence\nResearch, vol.14, 2001","Vincent Vidal, \"A Lookahead Strategy for Heuristic Search Planning\",\nProc. AAAI-04, 2004","Alfonso Gerevini, Alessandro Saetti, and Ivan Serina, \"Planning through\nStochastic Local Search and Temporal Action Graphs in LPG\", Journal\nof Artificial Intelligence Research, vol. 20, 2003","Ronald C. Arkin, Behavior-Based Robotics, The MIT Press, 1998","Lucy Suchman, Plans and Situated Actions - The Problem of\nHuman-Machine Communication, Cambridge University Press, 1987","Rodney A. Brooks, \"A robust layered control system for a mobile\nrobot\", IEEE Journal of Robotics and Automation, vol. 2, 1986","Robert A. Wilson and Frank C. Keil, The MIT Encyclopedia of the\nCognitive Sciences, The MIT Press, 1999\n[10] John McCarthy, \"Artificial Intelligence, Logic and Formalizing\nCommon Sense\", Philosophical Logic and Artificial Intelligence, ed. R.\nThomason, Kluwer Academic, 1989\n[11] Jana Koehler and Jörg Hoffman, \"On Reasonable and Forced Goal\nOrderings and their Use in an Agenda-Driven Planning Algorithm\",\nJournal of Artificial Intelligence Research, vol. 12, 2000\n[12] R. James Firby, \"An investigation into reactive planning in complex\ndomains\", Proc. the 6th National Conference on AI, 1987\n[13] Daniel S. Weld, Recent Advances in AI Planning, AI Magazine, 1999\n[14] T. Bylander, \"The computational complexity of propositional STRIPS\nplanning\", Artificial Intelligence, vol. 69, 1994"]}