This issue is the third in a series on Real World Robot Challenge in Tsukuba. The Tsukuba Challenge has contributed much in establishing autonomous mobile robot control technology on outdoor walkways where robots must mingle with pedestrians and cyclists – not all of whom may be familiar with such robots. The rain on the day of the final run for the 2015 Tsukuba Challenge taught us valuable lessons in navigating robots in real environments. Since the 2013 Tsukuba Challenge, a new task was introduced in the second stage. This task consists of searching for human targets – a technological challenge for developing robots that are both mobile and useful. Our objective here is to share advanced control technology refined through experiments in the real-world environment of the Tsukuba Challenge. The Tsukuba Challenge is also providing a forum for technological education for university students studying robotics engineering and for technical exchange through open experiments. In this issue, we are pleased to present the control technology that this exchange has brought about.

We would like to express our deep gratitude to the authors contributing to this issue and to the article reviewers who have helped make this all possible.

This paper introduces the robot that was developed for Tsukuba Challenge 2015. One of the team’s design goals was to implement a simple sensor configuration and control algorithm. For example, the robot’s laser range finders (LRF) were of fixed type, and localization was accomplished using only a single LRF and no other sensor. Environment map matching was achieved using an algorithm that processed pyramid images using binary image processing to reduce computational cost. Shape information from the LRF and color information from a camera were combined to detect a signboard placed near a person. During an actual test in rainy weather, the robot ran the entire course and, detected two, out of a possible four, number of target persons.

The Real World Robot Challenge (RWRC), a technical challenge for mobile outdoor robots, has robots automatically navigate a predetermined path over 1 km with the objective of detecting specific persons. RWRC 2015 was conducted in the rain and every robot could not complete the mission. This was because sensors on the robots detected raindrops and the robots then generated unexpected behavior, indicating the need to study the influence of rain on mobile navigation systems – a study clearly not yet sufficient. We begin by describing our robot’s waterproofing function, followed by investigating the influence of rain on the external sensors commonly used in mobile robot navigation and discuss how the robot navigates autonomous in the rain. We conducted navigation experiments in artificial and actual rainy environments and those results showed that the robot navigates stably in the rain.

The Tsukuba Challenge course includes a pedestrian road in which walkers, bicyclists, and mobile robots coexist. As a result, mobile robots encounter potentially dangerous situations when faced with moving bicycles. Navigating the challenge course involves locating target individuals in the search area and paying attention to the safety of bicyclists. Target individuals involve those who typically wear a cap and a refracted vest and are seated on chairs. This study proposes a method to identify pedestrians, bicyclists, and seated individuals by using a 3D LiDAR on Smart Dump 9. The SVM method was employed to identify the target seated individuals. An experiment was conducted on the challenge course to illustrate the advantages of the proposed method.

This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

A kinematics simulator for an autonomous mobile robot has been proposed to simulate complicated motions such as those caused by the interaction between a robot and its environment in terms of geometric relationship. The simulator is expected to assist in the development of a robot control system for autonomous running in the real world. This paper presents the simulator concept, its basic configuration, and the results of preliminary simulation experiments, which have been performed to evaluate a simple motion model, and an environment model based on occupancy grid maps and a laser range finder pseudo sensor model consisting of a typical probabilistic density. The results of the simulation experiments using the aforementioned multiple models are also presented to demonstrate that the simulator can perform in various numerical environments.

For the 2015 Tsukuba Challenge, we realized an implementation of vision-based localization based on ORB-SLAM. Our method combined mapping based on ORB-SLAM and Velodyne LIDAR SLAM, and utilized these maps in a localization process using only a monocular camera. We also apply sensor fusion method of odometer and ORB-SLAM from all maps. The combined method delivered better accuracy than the original ORB-SLAM, which suffered from scale ambiguities and map distance distortion. This paper reports on our experience when using ORB-SLAM for visual localization, and describes the difficulties encountered.

It is difficult to use histograms of oriented gradients (HOG) or other gradient-based features to detect persons in outdoor environments given that the background or scale undergoes considerable changes. This study involved the segmentation of depth images. Additionally, P-type Fourier descriptors were extracted as shape features from two-dimensional coordinates of a contour in the segmentation domains. With respect to the P-type Fourier descriptors, a person detector was created with the fuzzy c-means method (for general person detection). Furthermore, a fuzzy color histogram was extracted in terms of color features from the RGB values of the domain surface. With respect to the fuzzy color histogram, a detector of a person wearing specific clothes was created with the fuzzy c-means method (specific person detection). The study includes the following characteristics: 1) The general person detection requires less number of images used for learning and is robust against a change in the scale when compared to that in cases in which HOG or other methods are used. 2) The specific person detection gives results close to those obtained by human color vision when compared to the color indices such as RGB or CIEDE. This method was applied for a person search application at the Tsukuba Challenge, and the obtained results confirmed the effectiveness of the proposed method.

When two pedestrians pass one another on busy intersections or pedestrian crossings, they may try to avoid each other by moving in the same direction. Such behavior becomes a factor hindering a smooth walking. When robots will come to be used in our immediate environment in the future, similar situations are likely to occur. The objective of this study is to determine the appropriate avoidance action (direction and timing) that a robot should take in order to minimize the risk of coming face to face with a pedestrian, in situations where the two parties, who share a common travel line, pass one another. First, two experiments were conducted using human subjects to investigate the walking tendencies (gait characteristics) when a person walks past another oncoming person. Here, we examined the relationship between the landed foot and the direction in which it is easier to move and the tendencies of the avoidance direction when a person makes a sudden move to avoid another oncoming person. Based on the results, we proposed the avoidance action that the robot must take when it passes a human pedestrian, and confirmed its effectiveness through a verification experiment.

This paper presents a low-cost, lightweight robot arm with very low stiffness actuated by elastic tendons. To simplify the string tension control, a new winding device was developed. Small pulleys were incorporated into the winding drum to reduce friction between the tendon and the drum. A marionette-style two-link robot arm with compliant joints was prototyped. Because the arm and winding devices were separate from each other, the cost and weight of the robot were reduced. The links are made with lightweight wood connected by simple shaft joints. The robot design can be easily modified by the user because the mechanical parts do not require high machining accuracy. This robot is intended for implementation in tasks that do not require high positioning accuracy using a simple force control under environmental constraints. Because of its low stiffness, simple and sensor-less force control can be easily implemented based on the relationship between forces under static conditions. The proposed simple control method was evaluated experimentally by conducting position, static force, and hybrid position/force control tasks and was shown to perform well. The results also demonstrate that employing additional sensors, such as a camera, improves the accuracy of the controller.

Triangulation is commonly used to restore 3D scenes, but its frame of less than 30 fps due to time-consuming stereo-matching is an obstacle for applications requiring that results be fed back in real time. The structured light field (SLF) our group proposed previously reduced the amount of calculation in 3D restoration, realizing high-speed measurement. Specifically, the SLF estimates depth information by projecting information on distance directly to a target. The SLF synthesized as reported, however, presents difficulty in extracting image features for depth estimation. In this paper, we propose synthesizing the SLF using two projectors with a certain layout. Our proposed SLF’s basic properties are based on an optical model. We evaluated the SLF’s performance using a prototype we developed and applied to the high-speed depth estimation of a target moving randomly at a speed of 1000 Hz. We demonstrate the target’s high-speed tracking based on high-speed depth information feedback.

This paper proposes a trajectory planner for bipedal locomotion that determines a center-of-mass (CoM) trajectory, footsteps, and step durations simultaneously. Trajectory planning based on a linear inverted pendulum model is formulated as a nonlinear constraint satisfaction problem. The proposed iterative constraint solving algorithm is able to solve this problem in a short amount of time so that trajectory replanning at every walking step is possible. Unlike existing planning methods that determine footsteps and a CoM trajectory sequentially under fixed walking period, the proposed planner can produce complex walking patterns that fully utilize the interdependency of these physical quantities. The proposed trajectory planner and a trajectory tracking controller is implemented on a real robot and their performance is evaluated.

Recently, a number of researches related to underwater vehicle has been conducted worldwide with the huge demand in different applications. In this paper, we propose visual servoing for underwater vehicle using dual-eyes cameras. A new method of pose estimation scheme that is based on 3D model-based recognition is proposed for real-time pose tracking to be applied in Autonomous Underwater Vehicle (AUV). In this method, we use 3D marker as a passive target that is simple but enough rich of information. 1-step Genetic Algorithm (GA) is utilized in searching process of pose in term of optimization, because of its effectiveness, simplicity and promising performance of recursive evaluation, for real-time pose tracking performance. The proposed system is implemented as software implementation and Remotely Operated Vehicle (ROV) is used as a test-bed. In simulated experiment, the ROV recognizes the target, estimates the relative pose of vehicle with respect to the target and controls the vehicle to be regulated in desired pose. PID control concept is adapted for proper regulation function. Finally, the robustness of the proposed system is verified in the case when there is physical disturbance and in the case when the target object is partially occluded. Experiments are conducted in indoor pool. Experimental results show recognition accuracy and regulating performance with errors kept in centimeter level.

In this study, a pneumatically-driven forceps manipulator was developed for a master-slave-type surgical robot. The proposed manipulator had two flexible joints, one for the bending joint at the tip and the other for transmitting a bending force from the actuators to the wires of the forceps. The manipulator had two degree-of-freedoms (DOFs) of bending driven by only two pneumatic cylinders and a gripper driven by a cylinder. Given the interoperability in real surgery, a mechanism was proposed such that the clean forceps part could be easily attached to and detached from the filthy drive unit. An experiment of the master-slave-system was conducted with the proposed manipulator to verify the tracking performance of the cylinders’ position and the bending angle of the forceps manipulator.

This work focuses on a robust nonlinear control design of a robot arm with micro-hand (RAMH) by using operator-based robust right coprime factorization (RRCF) approach. In the proposed control system, we can control the endpoint position of robot arm and obtain the desired force of micro-hand to perform a task, and a miniature pneumatic curling soft (MPCS) actuator which can generate bidirectional curling motions in different positive and negative pressures is used to develop the fingers of micro-hand. In detail, to control successively the precise position of robot arm and the desired force of three fingers according to the external environment or task involved, this paper proposes a double-loop feedback control architecture using operator-based RRCF approach. First, the inner-loop feedback control scheme is designed to control the angular position of the robot arm, the operator controllers and the tracking controller are designed, and the robust stability and tracking conditions are derived. Second, the complex stable inner-loop and micro-hand with three fingers are viewed as two right factorizations separately, a robust control scheme using operator-based RRCF approach is presented to control the fingers forces, and the robust tracking conditions are also discussed. Finally, the effectiveness of the proposed control system is verified by experimental and simulation results.

The service robots now common in daily life are expected to provide highly useful services with high usability and high user experience (UX) to users and human beings around the service robots. Human-centered design (HCD) became used in a variety of industries, and HCD will be required in the service robot industry for improving usability and UX. However, no practical process of HCD has, to our knowledge, been proposed for service robots. So, we have been studying a development process for the service robots to improve a quality in use – a process we call model-based development with user models (MBD/UM). We certified the possibility of MBD/UM as a practical process of HCD by applying MBD/UM to approach function research on a telepresence robot.

Complication ratio of the pericardiocentesis was reported 4.7%, due to the procedure using needle. To decrease complication of the pericardiocentesis, purposes of this study was to develop a new pericardiocentesis assist device without needle. The proposed device was able to aspirate fluid inside the pericardial space by grasping and cutting the pericardium. To cut the pericardium, the device needed to grasp the pericardium surely. In this paper, we designed pericardium grasping mechanism. The result of the structural analysis using the finite element method showed proposed grasping mechanism could keep grasping force of 120 N and pericardium elastic force of 2.7 N without fracture. Results of in vitro porcine pericardium grasping experiment using a proposed device model showed that the pericardium grasping force was elucidated to need more than 7.5 N, while the pericardium elastic force was 2.44 N. The proposed pericardium grasping mechanism could grasp a porcine pericardium securely without fracture.

People suffering from tetraplegia are unable to perform many activities of daily living, such as dressing and toileting. The purpose of this study is to develop a dummy humanoid robot to assess assistive products and technologies for patients of tetraplegia. This paper describes the mechanism, motion planning, and sensing system of the dummy robot. The proposed dummy robot has upper-arm mechanisms that simulate the human collarbones based on a functional anatomy. To realize a variety of body shapes, the proposed robot has deformation mechanisms that use linear actuators and rotating servo-motors. The sensing system of the dummy robot can measure clothing pressure before and after exterior deformation is measured, and can hence detect changes in it.