Recently there have been a lot of attention to developing planetary rovers for Lunar or Mars Exploration. In 1997, Mars Pathfinder succeeded in landing Martian surface and the micro-rover “sojourner” could travel on Mars. Toward the turn of the century, several schemes sending an unmanned mobile explorer to the moon or Mars are being planned for scientific exploration. Planetary rovers are required to travel safely over a long distance for many days in unknown terrain and are also required to explore rough and steep areas such as craters, cliffs, etc. to achieve scientific goals. Most of the proposed rovers have some wheels to move on a planetary surface. However, a legged rover can provide higher capability of moving in such a rough terrain than wheeled robots. Therefore the authors have studied a small and light-weight rover which can move across a rough terrain such as a steep slope inside craters. A prototype of the robot with six legs has been developed. This paper presents the system concept, architecture, and configuration of the developed walking rover for planetary exploration. This paper also discusses a stability criterion and a walking algorithm for static walking in a rough terrain.

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At Tohoku University, we have developed a prototype test-bed for the extensive research on planetary rovers, named Proto-Explorer I. This paper presents the mechanism design, kinematics model, and initial experiments of the rover. Dead-reckoning accuracy and wheel slip are evaluated, and a control architecture based on slip analysis is proposed.

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The ability to decide proper actions reactively and robustly in the partially unknown and uncertain environment is very essential to the highly autonomous spacecraft such as planetary rovers. The concept of behavior-based architecture or situated agent, which has received much attention in the field of autonomous agents recent years, is very attractive and promising for this purpose. One of the significant issues in this methodology is how to prepare the large set of reactive behavior rules for the agent. To deal with this problem, we propose an inductive behavior learning method, with which the agents can acquire their proper reactive behavior rules from their experiences autonomously. In this method, learned behavior rules are represented by a set of “behavior subgoals” - overlapping regions in the “attribute space” (sensor space) and the decision making is performed reactively by matching the sensor inputs and the behavior history with the acquired behavior subgoals. As a consequence, the difficulty in the construction of reactive behavior modules in autonomous agents is relieved, and the robustness against the inevitable uncertainty in the real world is increased. We also present some simulation results of the goal-pursuing / obstacle avoidance task performed by a planetary rover to show the effectiveness of the proposed method.

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Remote inspection is a crucial technology for service vehicles that support space infrastructures. Since dexterous translation of viewpoint is vital for such surface inspection, we have proposed utilizing a redundant manipulator for such inspection. Service vehicles need to be in space for a long period of time, so manipulators should autonomously adapt to partial failures. In this paper, we propose a decentralized autonomous control algorithm for a hyper-redundant manipulator that can automatically adapt to partial failures.

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In this article, we propose a new method to realize a fast unmanned sample-collecting with relatively large and solid samples in a sample return mission. Strategies for shape-recognition of a sample-collection are described. Fundamental computer simulations for shape-recognition and experiments for sample-collecting using its shape-recognition with a gripper are carried out to verify the proposed methods.

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