The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real-time, intelligent, and context-aware systems. AI enables perception and reasoning, IoT provides scalable sensing and communication, and robotics delivers embodied actuation. Despite significant progress in pairwise combinations such as AIoT and the Internet of Robotic Things (IoRT), there remains a lack of unified design frameworks that fully integrate all three. This survey synthesizes the state-of-the-art across these domains, emphasizing the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision-making. We propose a modular system architecture that aligns with these trends, analyze persistent gaps in interoperability and feedback control, and classify existing work by integration depth. Our review highlights how hybrid SLM–LLM systems, when coupled with IoT infrastructure and robotic agents, can address challenges in real-time adaptation, scalability, and reliability. This work offers a conceptual and technical roadmap for designing next-generation AI–IoT–Robotic ecosystems that are modular, interpretable, and capable of learning within dynamic environments, paving the way for the emerging paradigm of Connected Robotics and Physical AI.

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.

A large-scale dump truck that automatically transports earth and sand in cooperation with a human-operated backhoe is of interest to the construction industry. A human-operated dump truck generally drives slightly past the desired loading position and then backs up to it for loading the sediment. The turning and loading positions are subjectively decided according to the working posture of the backhoe and the surrounding environment, and the safety margin of cooperative works. Backhoe operators want to perform the same maneuvers for human-operated/automated dump trucks. The movements of the autonomous vehicle should be similar to those of a human-operated one. However, it is difficult to derive a human-like path that does more than minimize costs. This study proposes a path-planning method that generates a path including a turning back, according to the changing backhoe position and surrounding conditions. We modeled the positional relationship during loading between a backhoe and dump truck, determining the loading and turning positions and related parameters from operational data collected in trials with human-operated construction vehicles. The proposed method allowed the autonomous dump truck path to resemble a human-like one. The authors have retrofitted an existing large-scale six-wheeled dump truck for automatic operation. Automatic loading in cooperation with a human-operated backhoe was realized all 17 times using the retrofitted dump. The average stopping accuracy was 0.57 m and 9.7°.

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.

Backhoe loads sediment onto the bed of dump trucks during earthmoving work. The prediction of backhoe loading time is essential for ensuring safe cooperation between the backhoe and dump trucks. However, it is difficult to predict the instant at which the backhoe is ready to load sediment, because of the similarity in motions observed during gathering sediment. Moreover, since operators have different skill levels, the prediction requires a unique model for each operator. In this study, we attempt to predict the instant at which the backhoe is ready to load sediment into the dump truck. For this purpose, the beta-process hidden Markov model (BP-HMM) is employed to build a backhoe motion model for a specific operator. Time series data of backhoe loading motions for crushed rocks and wood chips, which were measured using 6-axis inertial measurement unit (IMU) sensors equipped at the cab, boom, and arm of the backhoe, were used for modeling with the BP-HMM. Several primitive motions of the backhoe, which occur at the completion of preparation before the loading process begins, were discovered as a result of the motion modeling based on the BP-HMM. We developed the prediction of the instant using three primitive motions. At best, the proposed method could predict the instant with a probability of 67% and 100%, at 6.0 s and 0.7 s before the loading motions began, respectively. This phased prediction can be used to reduce the idle time and risk for dump trucks during earthmoving work with the backhoe.

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.

Perception is the main key in enabling robots to react to and interact with their environment. Particularly, for multi-floor operations, the robot must robustly detect and localize stairs to allow for safe climbing. In this paper, we develop a graph-based stairway detection method for point cloud data, that can detect a large variety of stairways. Our approach first segments planar regions and extracts the stair tread- and stair riser-shaped segments. With these segments, a dynamic graph model is initialized that is used to detect stairs including the railing system in the surroundings. We show that our system can accurately detect and localize different stairways from a variety of different positions, including descending stairs. Our system's accuracy is higher than those of most state-of-the-art stairway detection methods even in case of sparse point cloud data.

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.

This paper describes logical winnowing methods from multiple identification candidates using corresponding appearance identification results with chronological pedestrian tracking results. It is difficult to identify individual using appearance identification, because appearance identification has some properties. This research proposes two methods that logically winnow out the identification candidates as methods that effectively fuse different directional identification results without the directional information. Experiments were made to verify the validity of the proposed methods. A mobile robot equipped with a laser scanner and a camera was used in the experiments. A pedestrian tracking method uses the laser scanner. The appearance identification uses the camera. The experimental results verified the validity of the logical winnowing method taking the logical product of identification candidates determined by each round of identification. In this paper, the appearance identification properties, the proposed methods and the experiments are described.

This paper provides valuable information about 3-D map construction using a tracked vehicle at Disaster City. Disaster City is a training facility for the Federal Emergency Management Agency (FEMA) in the United States. FEMA staff used our rescue robot Kenaf to conduct simulated emergency testing. Kenaf is a tele-operated tracked vehicle that has high mobility suitable for use in outside terrains and inside buildings. We equipped Kenaf with a 3-D laser scanner that can measure dense and wide angle 3-D shapes. We collected about 50 3-D mapping data sets during the tests. A part of these data sets can be accessed at our website. In this paper, we explain the Kenaf's tele-operation system, the 3-D mapping system, and the comments about the usability of the 3-D mapping from FEMA staff. In addition, we discuss about the limit of our mapping method on the basis of the trials at Disaster City. This information will be helpful for researchers in the robotics to improve their method of 3-D map construction and their tracked vehicles.

Gyro-based odometry is an easy-to-use localization method for tracked vehicles because it uses only internal sensors. However, on account of track-terrain slippage and transformation caused by changes in sub-track angles, gyro-based odometry for tracked vehicles with sub-tracks experiences difficulties in estimating the exact location of the vehicles. In order to solve this problem, we propose an estimation method with 6 degrees of freedom (DOF) for determining the position and pose of the tracked vehicles using terrain information. (In this study, position refers to the robot's position and pose.) In the proposed method, position are estimated using a particle filter. The subsequent position of each particle are predicted using a motion model that separately considers each contact point of the vehicle with the ground. In addition, each particle is analyzed using terrain and gravity information. Experimental results demonstrate the effectiveness of this method.

In the search and rescue situation at the disaster area, the searching task should be conducted by robots in order to avoid the second disaster. We study crawler robots that can traverse rough terrains and are used in such search operations. This paper describes the designing of an online simulation environment using which we can develop control algorithms for crawler robots, especially for the robot named “Kenaf.” We use USARSim that can simulate three-dimensional space. By employing realtime simulation, we connect a real robot controller and the simulator directly and realize online simulation using the real robot controller. The tests show that the created simulation system can be used for the development of control algorithms.

IRS Soryu is rescue robot which used for victim search in disaster area. We aim at development of display method for wide view angle and high definition images for controlling the rescue robots and searching for victims. In our study, we developed small size multi camera system and displayed a wide view angle and high definition image. However, it is too hard for an operator to recognize the robot and environment intuitively. We need to develop a display method of these multi camera images for intuitive recognition. In this paper, we develop the on-line simulation system for the verification of multi camera based wide field of view display. This system has some requirements that; a. Simulation of IRS Soryu dynamics, b. Simulation of multi camera system, c. Flexibility of camera setting. We developed such simulator based on USARSim. Using the simulator, we examined some experiments for the verification of of the display methods.

We have developed a positioning system that uses a phased array sensor to measure the distance and direction of multiple landmarks relative to the position of a mobile robot so that the robot can self-localize. The positioning system can control the strength and the width of the beam from the phased array sensor and observe the multiple landmarks at the same time. By using the distance and direction of plural landmarks, our positioning system is able to use various self-localizing methods. A main part of our work has been incorporating our various techniques for making a robot positioning error smaller into a system in which the most suitable self-localizing method is selected on the basis of sensitivity, which is defined as the ratio between localizing and sensor errors. We propose using a set of indices to evaluate the accuracy of self-localizing methods. The indices are derived from the sensitivity. We used these indices to compare the accuracy of three methods for the self-localizing of a mobile robot using landmarks, and we demonstrated a rational way to minimize a localizing error.

A rescue crawler robot having flipper arm has high ability to get over rough terrain, but it is hard to control its flippers in remote control. The authors aim at development of a semi-autonomous control system for the crawler robot. In the system, moving direction is specified by an operator at remote place. Joypad (Sony PS2 controller) is used as input devise. For increasing its stability and robustness about change of the environment, its flippers are controlled according to sensor informations which can be obtained in real-time. Concretely, the robot recognizes the environment (upward step or downward step) using its physical model, its postural information, flippers' contact and distance between the body and ground, and controls these flippers. In this movie, we show you performance of our proposed control method using "Aladdin".

A rescue crawler robot with flipper arms has high ability to get over rough terrain, but it is hard to control its flipper arms in remote control. The authors aim at development of a semi-autonomous control system for the solution. In this paper, the authors propose a sensor reflexive method that controls these flippers autonomously for getting over unknown steps. Our proposed method is effective in unknown and changeable environment. The authors applied the proposed method to Aladdin, and examined validity of these control rules in unknown environment.

Our research objective is simultaneous localization and mapping (SLAM) in rubble environment. The map construction requires estimation of robot trajectory in 3D space. However, it is hard to estimate it by using odometry or gyro in rubble. In this paper, the authors proposed real-time SLAM based on 3D scan match. 3D camera is used for measurement of 3D shape and its texture in real-time. 3D map and robot trajectory are estimated by combining these 3D scan data. ICP algorithm is used for the matching method. The authors modified ICP algorithm as fast and robust one for real-time 3D map construction

Research objective of the authors is 3D map building and localization of search robot for rescue use. In this paper, the authors propose a novel method of dense 3D map building and present its trial result. For building a map, it is necessary to estimate robot motion. However, on rubble, it is difficult to estimate robot motion by using odometry or gyro. Therefore, in this framework, rough 3D map and discrete robot motions are derived using SLAM based on 3D scan matching. ICP algorithm is used for the matching method. Then, the dense 3D map is reconstructed from the rough 3D map and texture images.

The final objective of the authors is to realize mobile robot navigation in walkway of outdoor environment. The path along the walkway which is measured correctly together with landmarks must be given in the framework in this paper. The robot identifies its position by means of odometry, DGPS and laser range finder (LRF) during the motion along the path. This work contributes to the map building of landmarks along the walkway. To make the map, RTK-GPS, odometry and LRF are used for the sensory devices and Kalman smoothing technique is also incorporated to interpolate trajectory of the scanning LRF equipment between the two point that RTK-GPS measurement took place. The scanning by LRF is performed to acquire the 3-D shape of objects along the walkway. The trajectory presumed properly and well organized shapes of the objects are reconstructed.

The authors aim at map based outdoor navigation of a mobile robot. In navigation, robot position is fundamentally obtained by odometry. However, the position is misaligned as the robot moves because odometry has cumulative error. DGPS measurement data may cancel its position error. The framework of EKF is used for the modification and the fusion between odometry and DGPS measurement data. The DGPS measurement data, however, could have large error because of multipath near buildings. In this paper, the authors propose a method which eliminates erroneous DGPS measurement data when odometry robot position is fused, and confirm the validity of this approach.

This paper describes a strategy of a mobile robot walkway following using color image. There are different colors in a walkway and lawn area alongside the walkway. The walkway area is extracted by an image processing. Color difference among walkway and waysides is considered in the image processing. Robustness against daylight change for segmentation of walkway area is also considered. The robot moves to the direction toward vanishing point of the walkway which is obtained from the image processing. Repeating these processes, the walkway following can be accomplished.

This paper describes a strategy of a mobile robot walkway following using color image. There are different colors in a walkway and lawn area along side the walkway. The walkway area is extracted by an image processing using the difference of color. The robot moves to the direction of vanishing point which is obtained from the image processing. Repeating these processes, the walkway following will be accomplished.