Shinji Doki

Because autonomous mobile robots require pose information for navigation, localization is crucial. Localization estimates a robot’s pose by matching currently observed features with a pre-constructed map. However, each sensor has environments in which the localization accuracy deteriorates owing to the environmental dependency of the sensors, making robust localization with a single sensor difficult. Sensor multiplexing methods have been proposed to address this issue. In addition, condition changes can alter observed environmental features, causing mismatches between current observations and the map because of the condition dependency of maps. This makes robust localization in such environments difficult. Map multiplexing methods have been proposed to address this. Robust localization can be achieved against the environmental dependency of sensors by multiplexing sensors and against the condition dependency of maps by multiplexing maps. However, multiplexing using sensors or maps is insufficient to achieve robust localization against both types of dependency. Hence, in this paper, we propose a system that multiplexes both sensors and maps. Experiments using a dataset confirmed that the proposed system achieved robust localization against both types of dependency.

Wearable assistive robots have been studied widely to reduce the physical burden of tasks such as lifting and caregiving. Most existing systems, however, control only the joint torque. Even though wearable robots transmit actuator forces through direct contact with the body, these systems do not focus on managing the contact force generated at the human–robot interface. Our previous study developed wearable assistive robots capable of measuring and controlling the contact-force distribution. We verified the feasibility of evaluating safety and control performance using a single-joint arm-mounted robot and a multi-joint whole-body robot. However, the influence of force distribution on assist performance was not been sufficiently examined for multi-joint robots under assist conditions. The current study aimed to experimentally verify the effectiveness of using contact-force distribution information in a torso-mounted multi-joint robot (through experiments involving seven participants). The results showed that whereas the actual motor torque tracked the commanded torque under conventional torque-based control, the contact-force distribution experienced by the wearer varied across participants. This finding indicates that for wearable robots with complex joint structures, assessments of wearing states and assist effectiveness require an evaluation of the contact-force distribution in addition to the torque.

With the development of the internet and the availability of sufficient bandwidth, the performance of haptic devices is considered important because device specifications directly determine the comfort of wearing and the tactile sensations that can be transmitted. The Funabot-Grab is a haptic device that evokes a grabbed tactile sensation. Funabot-Grab can exert a tightening force by controlling the air pressure applied to the artificial muscles. There are seven artificial muscles on the fabric, and the tightening force of each artificial muscle is controlled independently. In a previous study, we used a model arm to incorporate Funabot-Grab into a tactile transmission system with the aim of using it as a tactile internet device. We conducted a preliminary experiment with three subjects as a precursor to the full-scale experiment. It was found that it is possible to transmit the static contact force and its position, even to the subjects. However, it became clear that further improvements to the device are needed for it to be worn optimally on every human body, considering the large degree of individual variation, and to evoke a smoother, continuous tactile sensation.

Accurate position estimation of unmanned aerial vehicles (UAVs) is essential for reliable and efficient task execution, such as the automated inspection of structures. However, localization is a significant challenge for UAVs in GNSS-denied spaces, such as under bridges, where satellite signals are obstructed. To address this issue, we employed a cooperative localization system consisting of a work-UAV, which performs tasks in the GNSS-denied space, and support-UAVs, which relatively localize the work-UAV. The critical challenge in this approach is the degradation of the GNSS-localization accuracy of the support-UAV owing to multipath effects, which are difficult to predict. This paper proposes an adaptive formation control method that enables support-UAVs to escape from multipath-affected spaces. Instead of focusing on signal processing or sensor fusion under multipath effects, our proposed system controls support-UAVs to escape from multipath-affected spaces where accurate GNSS-localization is difficult. This physical avoidance strategy enhances the robustness of the cooperative localization systems. Simulation results demonstrate the effectiveness of the proposed method. In two distinct scenarios, the average time spent by the support-UAV in multipath-affected spaces was reduced by 83.7% and 79.9%, respectively. Furthermore, the average task completion time was reduced by 37.6% and 14.3%, respectively.

Autonomous mobile robots are expected to demonstrate a high degree of adaptability, enabling effective operation across diverse environments. As sensor performance is largely influenced by environmental conditions, relying solely on a single sensor for localization makes robust localization challenging. To address this issue, various studies have enhanced the localization robustness using multiple sensors with different characteristics that complement each other’s weaknesses. However, conventional studies require the design of separate fusion systems for each type and numerous sensor observations. As future developments facilitate increased cooperation with environment-fixed sensors and external agents, the types and number of sensor observations accessible to robots are expected to dynamically change depending on location and time. Therefore, a pose fusion system that adapts to such changes is required. This paper proposes a fusion system that can adapt to changes in the type and number of sensor observations. This system dynamically fuses pose information obtained from onboard sensors, environment-fixed sensors, and external agents by extending the selective fusion method, one of the existing pose fusion methods for onboard sensors. Simulation experiments confirm that our system can adapt to changes in the type and number of sensor observations and robustly localize by dynamically fusing pose information from onboard sensors, environment-fixed sensors, and external agents.

We propose a novel pan-tilt-zoom (PTZ) camera configuration planning method to improve data collection for structure visual inspections. This method plans a set of configurations for PTZ camera-equipped robots to collect visual data of target surfaces at the required spatial resolution. Unlike previous methods that focus solely on optimizing camera positions to cover different areas of the target, our method fully leverages all the degrees of freedom of PTZ cameras. Our method allows the robot to completely cover targets with far less movement by adjusting the camera direction and zoom level. Experiments on bridge inspections using a PTZ ground inspection platform have validated that our method ensured both the completeness and spatial resolution of the collected data. Furthermore, drone-based inspection simulations have demonstrated that our method avoids unnecessary robot movement and reduces inspection time by up to 38% compared to the previous algorithm, significantly enhancing inspection efficiency.

We propose a viewpoint generation algorithm for the visual inspection of civil engineering structures using robots equipped with a gimbal-mounted camera. This algorithm enables robots to completely inspect target surfaces at the required spatial resolution while reducing movement by effectively utilizing the gimbal. Previous studies generally assume that the camera is always oriented orthogonally to the target surface, and they focus on the inspection position optimization problem, resulting in the underutilization of gimbals and heavy movement of robots. In this study, firstly we have formulated the conditions under which the collected data’s quality does not deteriorate in terms of damage detection, and we have relaxed the orthogonal measuring restrictions. Based on the conditions, we propose a viewpoint generation algorithm that can carry out inspection work by changing measurement orientations instead of moving the robot. In simulation experiments, our method reduces inspection time by 47.7% compared to the benchmark method for the same tasks of inspecting a pier surface with the required spatial resolution. In addition, the viewpoint generation time was reduced by 96.8% compared to the benchmark method. We also conducted experiments in real environments using pan-tilt heads and confirmed that our algorithm can collect data qualified for the damage detection of visual inspections.

Haptic feedback systems play a critical role in enriching the user experience in human-robot interaction. However, existing devices designed for evoking haptic sensations often face limitations owing to their low degree of freedom of deformation. In this study, we introduce the Funabot-Sleeve, a haptic device based on McKibben artificial muscles, and investigate its potential to evoke a range of haptic sensations using both steady-state and transient air pressure patterns. Our investigation examines the influence of these patterns on evoking distinct haptic sensations and identifies four specific sensations that can be evoked: Embraced, Pinched, a combination of Embraced and Pressed, and Twisted sensations. Across all participants, the evoked sensations showed positive correlations, with most correlations exceeding a value of 0.4, indicating a high degree of agreement in the sensations felt by the subjects. Our research lays the groundwork for the design of fabric actuators, capable of replicating specific stimuli and skin surface effects, thereby enabling a more sophisticated and personalized haptic feedback experience.

This paper investigates the feasibility and effectiveness of applying AR (Augmented Reality) technology to assist UAV (Unmanned Aerial Vehicle) pilots in collecting data for structure inspections. The challenge of using UAVs to collect inspection data is that pilots could not precisely determine where to collect qualified data. Using AR technology to provide visual assistance to pilots is expected to tackle this challenge. However, previous research has focused on enhancing pilots’ perception of UAVs’ current situations and goal waypoints. How to indicate ideal data collection poses (positions and orientations) with AR and the practical improvement that AR can bring remain unknown. Therefore, we proposed a system that visualizes ideal poses as AR models, allowing pilots to easily collect qualified data by aligning UAVs with these models and taking images. Experiments in virtual and real confirmed that the system significantly improves the proportion of the valid inspection area (achieving up to 99.3%).