運動誘導のための振動錯覚に基づく触覚フィードバック

要約のみTohoku University平田泰久課

Preferential Multi-Target Search in Indoor Environments using Semantic SLAM

In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearchComment: 6 pages, 8 figure

Cooperation of assistive robots to improve productivity in the nursing care field

This paper introduces an overview of the “Adaptable AI-enabled Robots to Create a Vibrant Society” project, which is part of the “Moonshot R &D Program” led by the Cabinet Office of Japan. We also introduce CARE, Cooperation of Ai-Robot Enablers, which are being researched and developed to improve productivity in the nursing care field. The importance of building an educational system for the successful use of advanced technologies will also be presented, and then we propose a nursing care motion guidance system using AR glasses that allows non-expert caregivers to learn appropriate nursing care

A performance evaluation of overground gait training with a mobile body weight support system using wearable sensors

Overground gait training under body weight support (BWS) for patients who suffer from neurological injuries has been proven practical in recovering from walking ability. Conventionally, skilled therapists or additional robots are required to assist the patient’s body weight and pelvis movement, making the rehabilitation process physically and economically burdensome. We investigate if a BWS walker using only two actuators can support the user’s body weight and simultaneously protect/assist the transverse pelvis rotation, improving natural gait with minimal motion compensation. In this paper, a BWS strategy called transverse pelvis rotation support (TPRS) is proposed to enable the BWS system to generate cable tension in the forward direction, as a purpose to support transverse pelvis rotation in addition to our previously proposed static or variable BWS. Wearable sensory devices, including instrumented shoes and harness, were developed to provide real-time ground reaction force and pelvis rotation signals simultaneously. Ten non-disabled participants were unloaded with 0% ~ 15% BWS under four different controls. Vertical ground reaction force, transverse pelvis kinematics, and user experience were compared using proposed controls. One-Way repeated measures ANOVA analysis assessed if control strategies generally affect the performance. All proposed controls enable the walker to support part of the user’s body weight. SBWS-TPRS and VBWS-TPRS control enable users to achieve a significantly improved pelvic motion and prolonged single support phase than pure static BWS or variable BWS, although users perceive a higher workload under them. The proposed BWS controls show the potential to become a complementary method in gait rehabilitation

Nursing care teaching system based on mixed reality for effective caregiver-patient interaction

In the nursing care tasks such as assistance for transferring and walking, it is necessary to provide appropriate nursing care movements depending on factors such as the patient's pose and the degree of disability. However, for novice caregivers to practice and learn appropriate nursing care, they must practice for a long time under the guidance of skilled caregivers. To solve this problem, we propose a novel framework for a system that teaches appropriate nursing care actions according to the current situation. The realization of such a teaching system requires technology to recognize the current situation and effectively teach the interaction between the caregiver and the patient. In this article, we propose a system that integrates depth camera-based pose estimation of the patient and Mixed Reality (MR) technology to present the target motion of the patient to a caregiver. To accurately present the patient's target pose to the novice caregivers, our system displays an avatar showing the patient's ideal animation overlaid on the actual patient. Experimental results show that our system can accurately instruct the caregiver about the patient's target pose in each movement procedure

Concept and prototype development of adaptive touch walking support robot for maximizing human physical potential

We propose a new walking support robot concept, “Nimbus Guardian,” designed to enhance the mobility of both healthy and frail elderly individuals who can walk independently. The proposed robot differs from traditional walker-type or cane-type aids by offering adaptive, minimal touch support based on the user's walking dynamics. Our goal is to realize versatile touch to the user as a preliminary study for developing the adaptive touch walking support robot. To achieve this, we have established a categorization system for walking support touch, outlining the specific types of assistance required for our robot. Based on these categorization, we have developed a prototype that improves the versatility of touch support (touch point, force, and initiator), adapting to the user's body. Our prototype is equipped to offer multiple touch support parts, adjusting to the user's physique. For versatile touch capabilities, we designed a motion control algorithm that includes a controller which directs the robot's wheel movements according to the chosen support points, and a state machine that provides multiple arm placements and movements. We have experimentally implemented this motion control algorithm in our prototype. Through experiments, we verified the touch versatility and discussed the prototype's utility and potential for further development