Masashi Konyo

Drone docking stations promote efficient operations of drones, but they usually support only one vehicle and are accessible primarily through vertical landing. These limitations hinder multidrone operations and result in challenges for fast precise docking, particularly under severe wind conditions. This article assesses the EAGLES Port, which uses a horizontal landing approach to address these challenges, and makes a performance comparison between horizontal and vertical landing through analysis of wind tunnel data with manually controlled drones. Results show that horizontal landing decreases the average landing duration by 35.58% and can achieve 59.67% faster docking compared to vertical landing in optimal conditions. The system also provides near-zero position error at docking and supports multiple drones. These advantages stem from improved flight stability, quicker alignment with landing targets, and a 2.8× higher average velocity compared to vertical landing. These results indicate that vertical landing is better suited for missions with wider landing zones and where delays in landing have mild consequences, whereas horizontal landing excels in scenarios where rapid accurate landings are critical.

This study delves into verifying the applicability of object co-occurrence information generated by a large-scale language model (LLM) to enhance a robot’s spatial ability to understand objects in the real world. Co-occurrence information is crucial in enabling robots to perceive and navigate their surroundings. LLM can generate object co-occurrence information based on the learned representations acquired from the learning process. However, the challenge lies in determining whether the co-occurrence gleaned from linguistic data can effectively translate to real-world object relationships, a concept yet to be thoroughly examined. After providing category information about a specific situation, this paper compares and evaluates the co-occurrence degree (co-occurrence coefficient) output by gpt-4-turbo-2024-04-09 (GPT-4) against the object pair data from the ScanNet v2 dataset. The results revealed that GPT-4 achieved a high recall of 0.78 across various situation categories annotated by ScanNet v2, although its precision was relatively low at an average of 0.29. The root mean square error of the co-occurrence coefficient was 0.31. While GPT-4 tends to output slightly higher co-occurrence coefficients, it effectively captures the overall co-occurrence patterns observed in the ScanNet v2 dataset. GPT-4 produced co-occurrence information for more objects than those available in ScanNet v2 while covering co-occurrences among objects within ScanNet v2. These results demonstrate that integrating co-occurrence data from different sources could enhance the ability to recognize real-world objects and potentially strengthen robot intelligence.

In recent years, there has been a growing demand for realistic haptic experiences in VR. This study employs the Intensity Segment Modulation (ISM) method to convert high-frequency vibrations into low-frequency amplitude-modulated waves on VR devices. We verified the generation of high-immersion vibration stimuli synchronized with audio. Additionally, we compared the perception of ISM-converted vibrations between VR devices and other devices to evaluate the effectiveness of ISM in VR environments. A demonstration on the Meta Quest 3 was also developed, allowing users to experience the tactile sensation of handheld fireworks.

Stereohaptic vibration is an innovative vibrotactile technology that extends the conventional tactile localization to the surrounding space, representing a virtual vibration source in the external environment. Previously, we have developed displays on the forearms and soles. Today, we present a demonstration of a new jacket-type device, which enables localization at any position around the body by arranging multiple vibrators along the torso centered on the midline. In our demonstration, you can experience the footsteps and roars of a inosaur walking around you, and it provides an experience that is as if you are in a fantasy movie.

There is a growing demand for teleoperated robots. This paper presents a novel method for reducing vibration noise generated by robot's own motion, which can disrupt the quality of tactile feedback for teleoperated robots. Our approach focuses on perceived intensity, the amount of how humans experience vibration, to create a noise filter that aligns with human perceptual characteristics. This system effectively subtracts ego-noise while preserving the essential tactile signals, ensuring more accurate and reliable haptic feedback for operators. This method offers a refined solution to the challenge of maintaining high-quality tactile feedback in teleoperated systems.

This paper proposes a novel concept of “stereohaptic vibration,” which employs distributed vibration to localize vibra- tion sources outside the body. Inspired by amplitude panning, a stereophonic sound display technique, we developed a method to localize a virtual vibration source (VVS) by polarizing the perceived intensity of multiple vibration stimuli to a specific orientation. Considering the perceptual characteristics of high- frequency vibration, the perceived intensity of the VVS was allocated to multiple vibrators according to the distance and direction of the target. The velocity discrimination performance was confirmed by utilizing four stimuli around the arm and one vibration stimulus to the palm to localize the movement of a VVS throughout the arm. Discrimination experiments of the trajectory of outgoing objects with a single arm and dual arms revealed that our approach could localize in three dimensions, even outside the body. The proposed technology for localizing external virtual vibration sources is expected to enhance the virtual reality experience.