In large-scale shipbuilding, welding tasks represent a significant portion of all tasks, requiring automated robot operation. However, current welding robots are not automated for high and narrow spaces because they cannot pull heavy welding cables, causing deviations from the intended path and reducing the welding accuracy. This paper proposes a cable-towing stabilization method considering factors such as the self-weight of multiple vehicles, magnetic adhesion force, and force necessary to hold the cables. The proposed approach integrates welding robots, which perform welding tasks, with towing robots, which alleviate the load imposed by welding cables and wire feeders. Cable-towing on walls requires reducing excessive distances between vehicles, as well as their excessive acceleration, in addition to maintaining the mechanical stability of each vehicle. Therefore, the optimal positions and postures of the towing vehicles are sequentially calculated using an optimization problem. The proposed method was evaluated through simulations and real-world experiments, confirming stable cabletowing on a wall surface of approximately 3 × 1.5 m. The findings of this research enhance the safety and efficiency of managing deformable linear objects with robots, focusing on mechanical safety while expanding the operational range from single-vehicle wall-mounted operations to cooperative multi-vehicle wall-mounted tasks, thereby increasing applicability to various wall-towing scenarios.

Roadmap-based path planning is a well-established method for multi-robot systems, where the free space of the environment is represented as a roadmap graph. The Voronoi diagram is known for its efficiency in creating roadmaps for single-robot systems due to its ability to generate paths with high clearance from obstacles. However, the design of the Voronoi diagram does not allow for redundant paths, as only one path is obtained between each pair of obstacles. In contrast, multi-robot path planning requires multiple path options for efficient solutions. Therefore, we propose redundant Voronoi roadmap graph, which incorporates multiple paths computed from the Voronoi diagram. In this approach, we introduce imaginary obstacles to modify the costmap and obtain a roadmap with more paths. Our proposed roadmap retains the high clearance feature of the Voronoi diagram and is suitable for multi-robot systems due to the availability of alternative route options. We demonstrate that our method can generate roadmaps in various simulated environments with different levels of redundancy. Additionally, we verify the efficiency of our proposed roadmap graph through graph analysis and multi-robot path planning experiments. Comparative analysis shows that the use of the proposed roadmap increases the success rate and solution quality compared to the roadmap obtained directly from the conventional Voronoi diagram.

Localization is crucial for various automation systems to provide awareness of the robot's position and orientation. Additionally, a localization system that offers portability, flexibility, and low computational and economic costs is required by a variety of robotics applications. However, no existing system can offer all the aforementioned features suitable for motion capture tasks involving ground swarm robots. In this study, we propose MoCArU, a novel Motion Capture system based on odometry and ArUco, robustly recognized through image data with low computational cost. We have evaluated the system's performance by comparing it with the ground truth trajectory and adopting different numbers of cameras. The results show that MoCArU can achieve a root mean square error of 0.1345±0.0065 m using ten cameras. Our findings add to previous knowledge by presenting a robust and cost-effective alternative to existing localization methods. Here, we show that MoCArU's use of lightweight camera stands and wireless communication ensures ease of installation, portability, and low computational cost, making it suitable for tracking swarm ground robot systems. We anticipate this system to be used in various applications, such as robot position control, navigation, and obstacle avoidance control. Overall, MoCArU provides a reliable and cost-effective solution for the real-time localization of robots, so its wider applicability in various environments is a significant advantage in robotics. An open-source implementation of MoCArU, as well as its related details, is open for public use at https://www.rm.is.tohoku.ac.jp/MoCArU.

Drop mitigation is an important function of micro aerial vehicles (MAVs) that are used for internal inspections of enclosed and cluttered structures (height: 5–10 m). The mechanism also allows continuous operation of drones, prevents the downtime required for maintenance and repair and also provides a safer environment for workers who are working below the drones. Some solutions include parachutes, auto-rotors, and active auto-rotors. However, these are inapplicable to MAV s with a protector shell because of their size and payload. We herein propose a new rapid response drop mitigation method for MA V s involving active autorotation with bendable wings and shells. Active autorotation enables faster deceleration during dropping motion as compared to parachutes or passive autorotation. Bendable wings can ensure optimal flight performance and enable sufficient deceleration during falling motion. This newly proposed fixture was shown to reduce the impact impulse by 32.2 % and horizontal oscillation by 34.4 %. From our flight tests conducted at heights of 5 m and 10m in both outdoor and indoor environments, the measured impact impulse for both profiles were attained at 0.93 N s. The minimum impact impulse that can cause harm to the human eye, which is the most vulnerable part, is 2 N s. Thus, this mechanism was successfully proven to provide a greater safety buffer based on the drop test conducted. We envision this mechanism to provide greater safety to drone operating environments in relevant fields involving indoor and urban drone flights. Furthermore, it can reduce damage to drones and structures and avoid injuries arising from drone crashes.

There is an increasing demand for robots that can be substituted for humans in various tasks. Mobile robots are being introduced in factories, stores, and public facilities for carrying goods and cleaning. In factories and stores, desks and shelves are arranged such that the work and movement of personnel are reduced. The surrounding furniture is also set to ensure that a single task can be performed in the same place. It is essential to study the intelligence of robots using information from such layouts, wherein human labor and movements are optimized. However, There is no method of map construction or location estimation that uses the characteristics of furniture arrangements that facilitate human work in a work space. Therefore, this study proposes a method for object mapping using layouts in crowded workspaces. Graphically represent the characteristics of furniture placement that make it easy for people to work in a workspace. The links in the graph represent the connections between the objects in the layout property. The nodes are the objects, and the weights of the links represent the strength of the layout properties. This graph is optimized by GraphSLAM to construct a map that considers the arrangement's characteristics. Using the graph structure improves the map's accuracy while allowing for relative changes in placement. The results show a 50.44% improvement in accuracy in a space with 18 desks, followed by two variations of similar desk layouts. The same improvement in accuracy was also observed when the relative positioning of objects changed significantly in each variation, such as a change to the left or right on the same side.

Bridge inspection using drones has become considerably more attractive owing to the ability of drones to gather information safely in lesser time and at a lower cost than traditional inspection methods. However, one of the critical issues in drone-based bridge inspection is ensuring stable communication between the operation base and inspection drones. The inspection drone easily loses sight of the operation base because bridges have intricate structures. Therefore, this study developed a radio map-based flight planning method of autonomous repeater drones for bridge inspection in order to ensure stable communication. Based on the inspection scenario, the repeater drone needs to ensure the communication with a minimum movement even under position deviations caused by winds. First, we generated a three-dimensional radio map of the bridge. Then, under the assumption that the inspection drone adopted a predetermined path, we developed a path planning method for a repeater drone based on the radio map. Some path planning examples demonstrated that the generated path reduces the moving distance and is also robust against the position error, in addition to ensuring stable communication.

In this paper, HueCode, a meta-marker that robustly and simultaneously exposes the relative pose between a marker and a camera along with additional information, is proposed. It occupies the area of a single marker by overlaying multiple types of markers in different colored layers. Using perspective information from the first (most recognizable) type of element marker, the second or higher marker can be recognized with a better success rate. An experiment using a HueCode made from an ArUco marker and a QR code showed that the QR code within the HueCode is recognizable at an elevation angle of up to 15°, compared to 25° for a normal QR code. In addition, the versatility of HueCode is demonstrated using two robotic applications. The first is a lightweight estimation of absolute 6-DoF poses for mobile robots in a GNSS-denied environment, and the other is object annotation in two-dimensional image or three-dimensional space without any prior knowledge. The former realized pose estimation with a position error of less than 0.05 m, and the latter enabled annotation of a mirror and a transparent object, which are difficult for other sensors and machine learning to recognize.

In this study, we developed a real-time simulation method for non-deformable continuous tracks having grousers for rough terrain by explicitly considering the collision and friction between the tracks and the ground. In the proposed simulation method, an arbitrary trajectory of a track is represented with multiple linear and circular segments, each of which is a link connected to a robot body. The proposed method sets velocity constraints between each segment link and the robot body, to simulate the track rotation around the body. To maintain the shape of a track, it also restores the positions of the segment links when required. Experimental comparisons with other existing real-time simulation methods demonstrated that while the proposed method considered the grousers and the friction with the ground, it was comparable to them in terms of the computational speed. Experimental comparison of the simulations based on the proposed method and a physical robot exhibited that the former was comparable to the precise motion of the robot on rough or uneven terrain.

Currently, drone research and development has received significant attention worldwide. Particularly, delivery services employ drones as it is a viable method to improve delivery efficiency by using a several unmanned drones. Research has been conducted to realize complete automation of drone control for such services. However, regarding the takeoff and landing port of the drones, conventional methods have focused on the landing operation of a single drone, and the continuous landing of multiple drones has not been realized. To address this issue, we propose a completely novel port system, "EAGLES Port," that allows several drones to continuously land and takeoff in a short time. Experiments verified that the landing time efficiency of the proposed port is ideally 7.5 times higher than that of conventional vertical landing systems. Moreover, the system can tolerate 270 mm of horizontal positional error, ±30° of angular error in the drone’s approach (±40° with the proposed gate mechanism), and up to 1.9 m/s of drone’s approach speed. This technology significantly contributes to the scalability of drone usage. Therefore, it is critical for the development of a future drone port for the landing of automated drone swarms.

For the past few years, unmanned aerial vehicles (UAVs) have been successfully employed in several investigations and exploration tasks such as aerial inspection and manipulations. However, most of these UAVs are limited to open spaces distant from any obstacles because of the high risk of falling as a result of an exposed propeller or not enough protection. On the other hand, a UAV with a passive rotating spherical shell can fly over a complex environment but cannot engage in physical interaction and perform power tethering because of the passive rotation of the spherical shell. In this study, we propose a new mechanism that allows physical interaction and power tethering while the UAV is well-protected and has a good flight stability, which enables exploration in a complex environment such as disaster sites. We address the current problem by dividing the whole shell into two separate hemispherical shells that provide a gap unaffected by passive rotation. In this paper, we mainly discuss the concept, general applications, and design of the proposed system. The capabilities of the proposed system for physical interaction and power tethering in a complex space were initially verified through laboratory-based test flights of our experimental prototype.

In large industrial plants, the inspection of production lines is a heavy and costly task that puts human inspectors at high risk. In order to overcome these challenges, we have developed an autonomous plant inspection system using a mobile tracked vehicle. In this paper, we propose an autonomous navigation method for tracked vehicles based on a contact force model that enables the robot to compensate for collisions with obstacles. The model considers the influence of the contact force on the linear and angular motion of the robot. Using the model, the controllable velocity range is derived during collisions. The experimental results show that the robot is safely controlled by complying with velocity constraints. In addition, our method can generate motions such as leaving wall, L-shaped curve and crosswise locomotion in straight passage while navigation alongside the walls. The method allows the robot to smoothly follow a target path, despite colliding with obstacles.

This paper presents a real-time image restoration method for aerial inspection images that are degraded by the appearance of a passive rotating shell of an unmanned aerial vehicle (UAV). Only images are required as the input. The method mainly consists of feature-based object detection for joints of the UAV shell, outlier rejection based on a sample consensus (SAC) approach and the geometrical model of the shell, reconstruction of the entire region that the shell appears in, and inpainting of the shell. Not only a pure algorithm for general UAVs with passive rotating shells but also an implementation specialized for a UAV that we have been developing are presented. The successfully implemented algorithm was evaluated for actual inspection flights under different conditions. The average rate of successful image restoration was 81.1% at 4.3 fps.

In recent years, several researchers focused their work on the mechanisms to protect the UAV from the dangerous collision with obstacles particularly the application of a spherical shell. However, this mechanism has some drawbacks when used in real-world mission especially in an outdoor environment. In the presence of wind, the spherical shell will experience significant air drag that will affect the flight performance of the UAV. In this paper, we focused our study on improving the flight performance of the UAV by reducing the drag force caused mainly by the spherical shell. We analyzed its structure and components to minimize the unwanted drag force. We evaluated two spherical structure, namely the 2V geodesic and fullerene. We also evaluated the spherical shell's component so-called joint by applying airfoil shape and compared it to a flat-plate design. CFD simulation and wind tunnel experiment were used as an evaluation tool to obtain a quantitative result. Based on our evaluation, changing from flat-plate to airfoil shape decrease the drag force of the joints by 72.31 %. Likewise, changing the structure from 2V geodesic to fullerene reduced the drag force of the connections by 12.42 %. The combination of fullerene structure and airfoil joints reduced the overall drag force by 34.74 %. An actual flight test in the bridge in the presence of wind further verifies the performance of the system by using the spherical shell with fullerene structure and airfoil joint.

Unmanned aerial vehicles (UAVs) such as multi-copters are expected to be used for inspection of aged infrastructure or for searching damaged buildings in the event of a disaster. However, in a confined space in such environments, UAVs suffer a high risk of falling as a result of contact with an obstacle. To ensure an aerial inspection in the confined space, we have proposed a UAV with a passive rotating spherical shell (PRSS UAV); The UAV and the spherical shell are connected by a 3DOF gimbal mechanism to allow them to rotate in all directions independently, so that the UAV can maintain its flight stability during a collision with an obstacle because only the shell is disturbed and rotated. To apply the PRSS UAV into real-world missions, we have to carefully choose many design parameters such as weight, structure, diameter, strength of the spherical shell, axis configuration of the gimbal, and model of the UAV. In this paper, we propose a design strategy for applying the concept of the PRSS mechanism, focusing on disaster response and infrastructure inspection. We also demonstrate the validity of this approach by the successful result of quantitative experiments and practical field tests.

We propose a method by which a micro air vehicle (MAV) can hover without propulsion power. In this paper, we describe the design of a magnetic adhesion mechanism and a winch mechanism for an MAV that is used to perform search operations inside buildings. An MAV equipped with these mechanisms can adhere to an iron ceiling and search for a long time from an appropriate position. We designed a magnetic adhesion mechanism with an adhesion force of over 20 N. The magnetic adhesion mechanism comprises a magnet, dual coil springs, and a switching mechanism. Using models of MAV and the adhesion mechanism, we determined the parameters of the mechanism. The magnetic adhesion mechanism can be detached easily by a force less than the magnetic adhesion force. The winch mechanism comprises a structure to retrieve the adhesion mechanism, a strong, lightweight tether and a servo-motor that produces enough torque to lift the MAV by the tether. We confirmed that the MAV can hover without propulsion power by using these mechanisms.

Tracked vehicles are frequently used as search-and-rescue robots for exploring disaster areas. To enhance their traversability on rough terrain, some are equipped with “active flippers.” However, manual control of such flippers also increases the operator's workload, particularly for teleoperation with limited camera views. To eliminate this tradeoff, we developed a shared autonomy system using an autonomous controller for flippers that is based on continuous three-dimensional terrain scanning. In our system, real-time terrain slices near the robot are obtained using three laser range sensors, and these are integrated to generate three-dimensional terrain information. In this paper, we introduce the autonomous controller for the flippers and validate the reliability of the shared autonomy system through experimental results on actual rough terrain.

For tracked vehicles moving over rough terrain, it is important to avoid rollovers and rapid motion. To realize smooth locomotion on rough terrain, some tracked vehicles are equipped with ¿active flippers.¿ Such flippers increase the traversability and stability of tracked vehicles. However, their control increases the operator workload, especially in the case of teleoperation. To eliminate this problem, we have developed an autonomous controller for generating terrain-reflective motions of flippers. Terrain information is obtained using laser range sensors that are located at both sides of our tracked vehicle testbed. Using this system, operators only have to specify a direction to the robot, following which the robot traverses rough terrain using autonomous flipper motions. In this paper, we introduce a strategy and an algorithm for the controller for active flippers and validate the reliability of the system through experimental results on rough terrain.

Tracked vehicles have the advantage of stable locomotion on uneven terrain, and, as a result, such mechanisms are used for locomotion on outdoor robots, including those used for search and rescue. However, such mechanisms always slip when a tracked vehicle follows a curve, and the slippage generates large accumulated positioning errors in the vehicle compared with conventional wheeled mobile robots. To improve the accuracy of the odometry and enable a path-following control, the estimation of the track slippage is essential. In this paper, we propose an improved method of odometry for tracked vehicles to follow a straight line or a curve. In this method, the vehicle estimates the slip ratios using two encoders (attached to the actuators) and a gyro-sensor. Based on the improved odometry, the path-following control of tracked vehicles is significantly improved. The validity of the method was confirmed with experiments involving our tracked vehicle on several types of surfaces.