Digital human models represent variations of body shapes for the target end-user population. They can simulate human motions, evaluate workloads, and are utilized to assess safety and usability of products and environments in a virtual space with computer-aided design. The digitalized process can reduce actual test panels, time and cost for human-centered design. Recent trends in research show that the target population of digital human models includes people with special needs. Moreover, digital human models are embedded into interactive tools to support design workshop.

Many missions have been launched to explore the Moon, Mars, asteroids, and comets, and many researchers are studying and developing lunar and planetary rovers for unmanned planet exploration, and further cooperative missions targeting human lunar exploration are under discussion. A key technology in these missions and orbital services is space robotics, including Al and automation. Space robotics is expected to support external vehicular activities (EVA) and internal vehicular activities (IVA), which will include constructing, repairing, and maintaining orbiting satellites and space structures.

This special issue presents the updated mission results and advanced research activities of space organizations, institutes, and universities, although it does not include all. We hope that this special issue will be useful to readers as an introduction to advanced space robotics in Japan, and that more robotics and Al researchers and engineers will become interested in space robotics and participate in space missions.

We thank the authors for their fine contributions and the reviewers for their generous contributions of time and effort. In closing, we also thank the Editorial Board of the Journal of Robotics and Mechatronics for helping to make this issue possible.

Locomotion is an important factor affecting astronaut support robots that are used in construction, repair, and inspection. Its requirements include long reach, compactness, and light weight. Tether is a good candidate because it allows for a long reach but is very light. It is also compact when wound up. The authors have previously proposed a reconfigurable tether-based locomotion method. In the concept, the robot attaches/detaches its tethers to/from handrails on the spacecraft and moves by controlling the length and tension of the tethers. From August 2012 to May 2013, JAXA conducted the Robot Experiment on JEM (REX-J) mission, experimentally demonstrating the proposed method on the International Space Station. During the experiment, all the locomotion tasks were successfully completed. This paper describes the results of these locomotion experiments.

In satellites, onboard software is required to perform complicated mission sequences and autonomous scheduling, conduct preliminary data processing, and manage various onboard devices. The dependability of onboard software strongly affects the reliability of a satellite itself. Therefore, the onboard software must be both complex and reliable to perform complicated small satellite missions. We propose an automatic software generator to meet these requirements. This generates onboard software and a database for the ground operating system using satellite development documents, such as command and telemetry definition documents and fault detection, isolation, and recovery (FDIR) definition documents. By using this software generator, the software development load can be reduced and human error can be avoided, even if the definitions are modified in an ad hoc manner during the development process. The generator additionally enables the easy accommodation of user preferences and software depth variation during a mission.

This study proposes a novel genetic programming method using asynchronous reference-based evaluation (called AREGP) to evolve computer programs through single-event upsets (SEUs) in the on-board computer in space missions. AREGP is an extension of Tierra-based asynchronous genetic programming (TAGP), which was proposed in our previous study. It is based on the idea of the biological simulator, Tierra, where digital creatures are evolved through bit inversions in a program. AREGP not only inherits the advantages of TAGP but also overcomes its limitation, i.e., TAGP cannot select good programs for evolution without an appropriate threshold. Specifically, AREGP introduces an archive mechanism to maintain good programs and a reference-based evaluation by using the archive for appropriate threshold selection and removal. To investigate the effectiveness of the proposed AREGP, simulation experiments are performed to evolve the assembly language program in the SEU environment. In these experiments, the PIC instruction set, which is carried on many types of spacecraft, is used as the evolved assembly program. The experimental results revealed that AREGP cannot only maintain the correct program through SEU with high occurrence rate, but is also better at reducing the size of programs in comparison with TAGP. Additionally, AREGP can achieve a shorter execution step and smaller size of programs, which cannot be achieved by TAGP.

In this paper, we propose an operation support system, which shows the spatial relation between a robotic hand and an object, to overcome reduced usability due to communication delay. A communication delay of four seconds occurs during remote operation between the earth and the moon. Previous studies have used a method that feeds back the predicted position of a robotic hand after the communication delay occurs. However, this does not allow an accurate reaching motion necessary for grasping. Therefore, we try to show the spatial relation between a robotic hand and an object using three orthogonal rays, where each is annotated with 1 cm increments. Based on the results of our experiments, the grasping success rate of the proposed method increases by three times. As a result, the proposed method is effective in making accurate reaching motions for grasping in the visual-servoing remote control between the earth and the moon.

In recent years, a plan has been developed to conduct an investigation of an unknown environment by remotely controlling an exploration robot system sent to the Moon. For the robot to successfully perform sophisticated tasks, it must implement multiple degrees of freedom in not only its arms and legs but also its hands and fingers. Moreover, the robot has to be a humanoid type so that it can use tools used by astronauts, with no modification. On the other hand, to manipulate the multiple-degrees-of-freedom robot without learning skills necessary for manipulation and to minimize the psychological burden on operators, employing a method that utilizes everyday movements of operators as input to the robot, rather than a special controller, is ideal. In this paper, the authors propose a compact wearable device that allows for the estimation of the hand pose (hand motion capture) of the subject. The device has a miniature wireless RGB camera that is placed on the back of the user’s hand, rather than on the palm. Attachment of the small camera to the back of the hand may make it possible to minimize the restraint on the subject’s motions during motion capture. In the conventional techniques, the camera is attached on the palm because images of fingertips always need to be captured by the camera before images of any other part of the hand can be captured. In contrast, the image processing algorithm proposed herein is capable of estimating the hand pose with no need for first capturing the fingertips.

Sampling-based searchalgorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT^*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT^* algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT^*. This process improves conventional RRT^* in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

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.

In the exploration of a small celestial body, it is important to estimate the position and attitude of the spacecraft, as well as the geometric properties of the target celestial body. In this paper, we propose a method to concurrently estimate these quantities in a highly automatic manner when measurements from an attitude sensor, inertial sensors, and a monocular camera are given. The proposed method is based on the incremental optimization technique, which works with models for sensor fusion, and a tailored initialization scheme developed to compensate for the absence of range sensors. Moreover, we discuss the challenges in developing a fully automatic navigation framework.^*

^* This paper is an extended version of a preliminary conference report [1].

We are developing a robotic system for an asteroid surface exploration. The system consists of multiple small size rovers, that communicate with each other over a wireless network. Since the rovers configure over a wireless mesh sensor network on an asteroid, it is possible to explore a large area on the asteroid effectively. The rovers will be equipped with a hopping mechanism for transportation, which is suitable for exploration in a micro-gravity environment like a small asteroid’s surface. However, it is difficult to control the rover’s attitude during the landing. Therefore, a cube-shaped rover was designed. As every face has two antennas respectively, the rover has a total of twelve antennas. Furthermore, as the body shape and the antenna arrangements are symmetric, irrespective of the face on top, a reliable communication state among the rovers can be established by selecting the proper antennas on the top face. Therefore, it is important to estimate which face of the rover is on top. This paper presents an attitude estimation method based on the received signal strength indicators (RSSIs) obtained when the twelve antennas communicate among each other. Since the RSSI values change depending on an attitude of the rover and the surrounding environment, a significantly large number of RSSIs were collected as a training data set in different kinds of environments similar to an asteroid; consequently, a classifier for estimating the rover attitude was trained from the data set. A few of the experimental results establish the validity and effectiveness of the proposed exploration system and attitude estimation method.

Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA. To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park; and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints; and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.

A planar travel distance sensor (two-dimensional sensor) was developed for a mobile robot in sandy terrain. The sensor system uses an optical flow device integrated into a small module with a simple configuration. The system achieves a high sampling rate on the order of milliseconds as well as precise measurement on a sub-millimeter order. Its performance was evaluated experimentally for measurement accuracy and repeatability, velocity response, robustness at varied heights with respect to terrain, and terrain surface characteristics. The experimental results confirm that the two-dimensional sensor system is accurate, having an error of distance traveled of less than a few percent, and that it possesses a wide dynamic range for the robot’s traveling velocity. This paper also discusses the applicability of the two-dimensional sensor for practical scenarios on the basis of the experimental findings.

Various planetary terrains or asteroids, which are hard to traverse with wheeled platforms, are expected to be explored. Bekker’s model cannot be applied to estimate the motions of rovers without wheels, such as the hopping rover (hopper). In this paper, the resistive force theory (RFT) approach is introduced. This approach is not based on Bekker’s model, and is expected to apply to any platform. However, this RFT approach only applies to static or quasi-static motion, such as in the case of slow motions. To apply the RFT approach to dynamic motions, such as hopping, the effect of velocity as a dynamic variable is also studied. Through the hopping experiments, the effectiveness of RFT with the velocity-term approach is investigated and compared to the RFT approach.

This paper proposes a method that can estimate and classify the magnitude of wheel slippage for a mobile robot in sandy terrains. The proposed method exploits a sensor suite, called an in-wheel sensor, which measures the normal force and contact angle at the wheel-sand interaction boundary. An experimental test using the in-wheel sensor reveals that the maximum normal force and exit angle of the wheel explicitly vary with the magnitude of the wheel slippage. These characteristics are then fed into a machine learning algorithm, which classifies the wheel slippage into three categories: non-stuck wheel, quasi-stuck wheel, and stuck wheel. The usefulness of the proposed method for slip classification is experimentally evaluated using a four-wheel-drive test bed rover.

This paper deals with a percussive rock surface crusher driven with a solenoid to smoothen the sample surface by a 2-axis planar motion. The weathered rock surface should be removed and smoothened before analyzing its structure and composition precisely. The solenoid, which generates a large vibration amplitude and a large impulsive force, was used to vibrate a tool bit with engineered 1-mm pyramids made of tungsten carbide. The tool bit was fixed parallel to the feed direction or with a skew. A rock sample was moved by a stage with movable ranges for the machining of 10 mm and 20 mm in the x- and y-directions, respectively. The sample paths were randomly generated in 1 or 2 directions. In the comparisons of the surface roughness, the 2-axis motion and tool skew not only allowed isotropic and small roughness but also the removal of more amount due to the removed debris. The roughness reached several tens of micrometers without a certain special frequency component. This level allows for component analysis by X-ray fluorescence or laser-induced breakdown spectrometer.

This paper describes a process for adaptive learning of hand movements in human demonstration for manipulation actions by robots using Dynamic Movement Primitives (DMPs) framework. The process includes 1) tracking hand movement from human demonstration, 2) segmenting hand movement, 3) adaptive learning with DMPs framework. We implement a extended DMPs model with a modified formulation for hand movement data observed from human demonstration including hand 3D position, orientation and fingers distance. We evaluate the generated movements by DMPs model which is reproduced without changes or adapted to change of goal of the movement. The adapted movement data is used to control a robot arm by spatial position and orientation of its end-effector with a parallel gripper.

Autonomous mobile robots need to acquire surrounding environmental information based on which they perform their self-localizations. Current autonomous mobile robots often use point cloud data acquired by laser range finders (LRFs) instead of image data. In the virtual robot autonomous traveling tests we have conducted in this study, we have evaluated the robot’s self-localization performance on Normal Distributions Transform (NDT) scan matching. This was achieved using 2D and 3D point cloud data to assess whether they perform better self-localizations in case of using 3D or 2D point cloud data.