In this fourth of the “Special Issues on Real World Robot Challenge in Tsukuba,” we feature the control technology of autonomous robots. There is no guarantee that it will operate perfectly in a real-world environment even with the method already revealed. Participating robots in Tsukuba Challenge are required to carry out the assigned tasks under the prevailing weather conditions on the day of the events. Robots avoid oncoming pedestrians and obstacles in their path. In order to share the novel technology of the autonomous control method, this special issue presents a summary of the results of robots that participated in past Tsukuba Challenges.

It is only thanks to the ongoing efforts of the organizers of Tsukuba Challenge and the enthusiasm on the part of the participants that we are able to present an issue such as this, and we are truly thankful to them. We also wish to thank the authors who submitted papers and articles for this issue, as well as our reviewers.

A robot working in public spaces must not frighten the surrounding people. However, this could occur if it is not evident that the robot is safe. To avoid frightening people, the robot must be designed with both safety and appearance considerations. However, few relevant design theories have been developed. In this study, a mobile robot was designed using a concept that eliminates dangerous elements from the mobile robot’s appearance. Finally, we developed a mobile robot with a flexible cowl based on safety and design property considerations.

This paper presents an autonomous navigation system. Our system is based on an accurate 3D map, which includes “geometric information” (e.g., curb, wall, street tree) and “semantic information” (e.g., sidewalk, roadway, crosswalk) extracted by environmental recognition. By using the semantic map, we can obtain the suitable area to keep away from undesired places. Furthermore, by comparing the map with real-time 3D geometric information from LIDAR, we obtain the robot position. To show the effectiveness of our system, we conduct a 3D semantic map construction experiment and driving test. The experiment results show that the proposed system enables accurate and highly reproducible localization and stable autonomous mobility.

In order for a robot to provide service in a real world environment, it has to navigate safely and recognize the surroundings. We have participated in Tsukuba Challenge to develop a robot with robust navigation and accurate object recognition capabilities. To achieve navigation, we have introduced the ROS packages, and the robot was able to navigate without major collisions throughout the challenge. For object recognition, we used both a laser scanner and camera to recognize a person in specific clothing, in real time and with high accuracy. In this paper, we evaluate the accuracy of recognition and discuss how it can be improved.

This paper discusses the generated trajectory of an extended lateral guided sensor steering mechanism (SSM) method for a steered autonomous vehicle moving in a real world environment. In a previous study, an extended SSM was applied to the Smart Dump 9 and AR Chair robots for following preset waypoints on a map. These studies showed only the schematic idea of the method; the precise performance of the generated trajectory was not shown. This paper compares the Smart Dump 9 robot with a newly developed AR Skipper robot; these robots participated in the Tsukuba Challenge in 2015 and 2016, respectively. Finally, experimental data from the Tsukuba Challenge 2016 demonstrates the advantages of the extended SSM and developed control system.

Planning is one of the cornerstones of autonomous robot navigation. In this paper we introduce an open source planner called “OpenPlanner” for mobile robot navigation, composed of a global path planner, a behavior state generator and a local planner. OpenPlanner requires a map and a goal position to compute a global path and execute it while avoiding obstacles. It can also trigger behaviors, such as stopping at traffic lights. The global planner generates smooth, global paths to be used as a reference, after considering traffic costs annotated in the map. The local planner generates smooth, obstacle-free local trajectories which are used by a trajectory tracker to achieve low level control. The behavior state generator handles situations such as path tracking, object following, obstacle avoidance, emergency stopping, stopping at stop signs and traffic light negotiation. OpenPlanner is evaluated in simulation and field experimentation using a non-holonomic Ackerman steering-based mobile robot. Results from simulation and field experimentation indicate that OpenPlanner can generate global and local paths dynamically, navigate smoothly through a highly dynamic environments and operate reliably in real time. OpenPlanner has been implemented in the Autoware open source autonomous driving framework’s Robot Operating System (ROS).

This paper describes our approach to perform robust monocular camera metric localization in the dynamic environments of Tsukuba Challenge 2016. We address two issues related to vision-based navigation. First, we improved the coverage by building a custom vocabulary out of the scene and improving upon place recognition routine which is key for global localization. Second, we established possibility of lifelong localization by using previous year’s map. Experimental results show that localization coverage was higher than 90% for six different data sets taken in different years, while localization average errors were under 0.2 m. Finally, the average of coverage for data sets tested with maps taken in different years was of 75%.

In order to realize the work of goods conveyance etc. by robot, localization of robot position is fundamental technology component. Map matching methods is one of the localization technique. In map matching method, usually, to create the map data for localization, we have to operate the robot and measure the environment (teaching run). This operation requires a lot of time and work. In recent years, due to improved Internet services, aerial image data is easily obtained from Google Maps etc. Therefore, we utilize the aerial images as a map data to for mobile robots localization and navigation without teaching run. In this paper, we proposed the robot localization and navigation technique using aerial images. We verified the proposed technique by the localization and autonomous running experiment.

This paper describes a method of using camera images to detect changes in the display status of pedestrian traffic signals. In much of the research previously done on signal detection, the color or shape of images or machine learning has been used to estimate the signal status. However, it is known that these methods are greatly affected by occlusion and changes in illumination. We propose a method of detecting, using multiple image sequences captured over time, changes in appearance that occur when a signal changes. If this method is used, the position and the status of the traffic light can be accurately detected as long as it appears in the image, even if its relative position or the lighting conditions in the area changes. In this paper, we first describe how pedestrian signals are seen when difference images are used, and we propose an algorithm for detecting when a signal changes. Then, the effectiveness of the proposed method is confirmed through verification tests.

A technique for automating the Image-Information-Added Map, a mapping method for photographing an object at a required resolution, is proposed. The picture shooting vector indicating the angle for taking a picture with sufficient resolution is defined according to the shape of the object surface, and the operator controls a robot remotely to acquire pictures by checking the picture shooting vector in our previous study. For an automated inspection system, image acquisition should be automated. Assuming a 2-D grid map is prepared, first, the shooting vectors are set on the surface of the object in the map, and the picture shooting areas are defined. In order to reduce the number of the points that the mobile robot moves to to take pictures, an overlapping picture shooting area should be selected. As the selection of the points where pictures are taken is a set covering problem, the ant colony optimization method is used to solve it. Edge Exchange Crossover (EXX) is used to select picture taking points that are connected for efficient checking. The proposed method is implemented in a robot and evaluated according to the resolution of the collected images in an experimental environment.

We are developing the robot in order to guide visually impaired persons in large hospitals. This paper describes the structure of the robot and the results of a demonstration examination in Kanagawa Rehabilitation Hospital, Japan. The robot navigates to the destination while steering, depending on the force with which the user pushes on the robot. The success rate for reaching a destination with the robot was higher than with a white cane in the testing at Kanagawa Rehabilitation Hospital. We evaluated the traveling time and the participant questionnaire as an endpoint by Wilcoxon signed-rank test. Though there is no advantage in traveling time between use of the white cane and the robot, according to the scores of the participants questionnaire, it was shown that traveling with the robot was better than traveling with the white cane for the participants.

This paper presents a cooking behavior recognition method for achievement of a cooking navigation system. A cooking navigation system is a system that recognizes the progress of a user in cooking, and accordingly presents an appropriate recipe, thus supporting the activity. In other words, an appropriate recognition of cooking behaviors is required. Among the various cooking behavior recognition methods, such as the use of context with the object being focused on and use of information in the line of sight, we have so far attempted cooking behavior recognition using a method that focuses on the motion of arms. Using the cooking behavior rate obtained from the motion of arms and cooking utensils, this study achieves recognition of the cooking behavior. The average recognition rate was 63% when calculated by the conventional method of focusing on arm motions. It has been improved by approximately 20% by adding the proposed cooking utensil information and optimizing the parameters. An average recognition rate of 84% was achieved with respect to the five types of basic behaviors of “cut,” “peel,” “stir,” “add,” and “beat,” indicating the effectiveness of the proposed method.

This paper proposes cable tension compensation control method for helicopters cabled to a ground station. This method can be applied to human-operated systems and reduces the required control skill. The cable tension vector is measured by a force sensor and an angle sensor mounted to the helicopter. Thrust and helicopter attitude to balance with resultant force of tension and gravity are calculated and automatically added to attitude controller input calculated by using PID control. Simulation and experimental results demonstrate the validity of the proposed method.

This paper presents an evaluation of the maneuverability of impedance-controlled robot motion during a human-robot cooperative positioning task. The objectives of this study are to reveal the results of a quantitative evaluation of the maneuverability of robot motion and to investigate the relationship between the results of the quantitative evaluation and an operator’s higher-order brain activity. Control strategies for the robot that are adequate for human-robot interaction have not yet been explicitly determined because of the difficulty in evaluating the maneuverability of robot motion. First, we analyzed the time normalized position and force/torque trajectories to reveal the characteristics of human motion and performed subjective evaluations for three types of impedance-controlled robot motion, which were controlled using the following strategies: (i) ordinary impedance control, (ii) impedance control with virtual Coulomb friction involved in the robot motion, and (iii) impedance control with a trajectory guidance force. Second, to confirm the analysis results based on the observed trajectories, we investigated differences in the operator’s higher-order brain activity when using the different control strategies by using a functional near-infrared spectroscopy system. The experimental results confirmed the relationship between the analysis results of the control strategies, the motion of the operator, and higher-order brain activity. Consequently, the investigation conducted in this study is effective for evaluating the maneuverability of robot motion during a human-robot cooperative task.

This paper investigates the application of optimal control to a micro ground vehicle (MGV) experimentally. The model predictive control (MPC) technique is used for the overall tracking controller during the maneuver. The reference trajectory for MPC is preliminarily obtained by numerical computation of the optimal control problem, which is prescribed as a minimum-time maneuver. The results provide nominal tracking performance and validate the feasibility of the approach.

This paper reported the tensile strength of the difference of modeling condition on the FDM (Fused Deposition Modeling) 3D printer. The FDM 3D printer is rapidly spread with the end of patent protection in 2009. The FDM models mainly use the prototyping part and art, because that models have low strength. This time we paid attention to that actual models weight is lighter than designing models weight to conduct study on strength. And we investigated the cause of the phenomenon of decrease of polymer extrusion by replacing with the injection molding method. The tensile test proved that the strength of model can be improved by the kind of extruder head. This paper reported influence of the cooling in the supply part of extruder head and temperature of the polymer on the strength of FDM 3D models.