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Lunar or planetary exploration rovers are expected to have the ability to move across an area as wide as possible in an unknown environment during a limited mission period. Hence, they need an efficient navigation method. Most of the surface of the moon or planets consists of flat ground, sand, and scattered rocks. In a simple flat sandy terrain with some rocks, rough route planning is sufficient for a lunar or planetary rover to avoid obstacles and reach an assigned point. This paper proposes an efficient vision-based planning scheme for exploration rovers on a flat surface with scattered obstacles. In the proposed scheme, dangerous areas are robustly extracted by processing image data, and the degree of danger is defined. A rough routing plan and sensing plan are simultaneously constructed based on the dangerous-area extraction results. The effectiveness of the proposed scheme is discussed based on the results of some simulations and simple experiments.

This paper proposes an efficient scheme to make a routing and sensing plan simultaneously for lunar or planetary exploration rovers. In the proposed scheme, the environment is widely recognized by using a vision system. The proposed algorithm can detect obstacles based on gray-level image. And a rough route is produced by judging whether the area is safe or dangerous. Firstly, the shade average and the variance at each pixel are calculated from the obtained image, and the environment recognition is performed. According to the recognition results, a rough path and a sensing strategy are planned. A rover senses locally based on the proposed behaviour and moves to the destination. The validity of the proposed method is shown by some computer simulations.

Recently, AI, robotics and automation in space, referred to in a general term as ""space robotics"" in this paper, are playing increasingly more important roles for ground support, LEO satellites and planetary probes. In deep space missions, however, space robotics is a ""must"" due to the radio propagation delay and a poor communication link between the spacecraft and Earth. A typical example for robotics for planetary exploration is an autonomous rover that moves around on the surface of planetary bodies and conducts scientific investigations. A new infrastructure called ROBUST is proposed, which stands for ROBotized Unmanned Station. ROBUST is the space station that will be constructed, maintained and expanded by robotic technology completely without human presence in orbit.

As space robots are used in a zero gravity environment, there are some technical problems to be considered, such as a control scheme for the manipulator which considers the reaction movement caused by the manipulator motion, a recognition scheme for 6 DOF (degrees of freedom) motion of the target, and so on. To solve these problems, we have constructed a ground simulation system combining numerical simulation and servo mechanisms. In this system, the dynamics of the space robot and the target are solved based on the momentum conservation law, and the relative motion between them is simulated. Using this simulation system, we can efficiently develop space robots. In this paper, we describe the configuration of this system, the simulation algorithm, and the result of evaluation tests.

*† ‡ This paper proposes an autonomous navigation method to precisely estimate the relative movement onboard of an explorer based on 3D perception in such natural terrain as Moon or Mars. It is a map matching technique that registers the visual information of consecutive robot positions and expresses the robot displacement in a common coordinate system. Local elevation maps of the environment are therefore retrieved from the 3D perception of a stereo camera. The overall process breaks down into three main steps: feature point extraction from map data, matching of features points using triangle configurations and a voting procedure, and direct calculation of the robot displacement in six degrees of freedom using quaternion. The effectiveness of the proposed method is confirmed by computer simulations with synthetic terrain as well as experiments with stereo perception.

This paper describes a position identification method to precisely estimate the relative movement onboard of an autonomous explorer in such natural terrain as Moon or Mars. This paper proposes a new map matching scheme that registers the visual information of consecutive robot positions and expresses the robot displacement in a common coordinate system. Local elevation maps of the environment are therefore retrieved from the 3D perception of a stereo camera. The proposed scheme consists of three main steps: feature point extraction from map data, matching of features points using triangle configurations and a voting procedure, and direct calculation of the robot displacement in six degrees of freedom using quaternions. The effectiveness of the proposed method is confirmed by computer simulations with synthetic terrain and experiments with stereo perception.

In autonomous retrieval of a free-flying object, tumbling motion makes it difficult to grapple the objects with an on-board manipulator. This paper presents a new control scheme to capture a tumbling satellite based on a simple dynamical model. The target motion is estimated based on visual information using Kalman filtering technique. The capture planning based on the proposed model becomes simple because the motion of a grapple fixture on a target satellite nearly stops relative to the end-effector of a manipulator. The validity and the usefulness of the proposed method are shown by computer simulations and experiments using a 3-D hardware simulator with 9 degrees of freedom.