Tremor, which is one of the most common movement disorders, is a repetitive movement that is caused by periodic muscle contraction and relaxation. To suppress tremors of upper-limb tremor patients, such as essential tremor (ET) patients, many kinds of devices have been developed. On the other hand, when mechanical vibration stimulation is applied to a human muscle, sustained muscle contraction, which is referred to as the tonic vibration reflex (TVR), is induced in the stimulated muscle. In this study, a novel tremor suppression method that utilizes the periodic TVR to induce muscle contraction/relaxation to generate the counterphase motion of the ET is proposed and applied to the forearm pronation-supination ET. In the proposed method, periodic vibration stimulation is applied to generate the periodic TVR in the pronator teres muscle and/or supinator muscle. First, the results confirmed that the TVR can be induced by applying mechanical vibration stimulation to the pronator teres muscle and supinator muscle since the forearm pronation-supination tremor is one of the key features of the ET. Furthermore, the findings also confirmed that the TVR intensity that is induced in these muscles can be adjusted by changing the vibration stimulation frequency. Second, the results show that the counterphase motion of the ET (i.e., periodic pronation-supination motion) can be generated by applying the proposed method. The effectiveness of the proposed method for tremor suppression is evaluated by comparing the generated motion with the ET motion.

Multi-agent coverage control plays a crucial role in the modeling and coordination of complex systems, especially for teams of robots that, through frequent interactions with each other and their environment, can accomplish complex tasks in a distributed and parallel manner. However, most existing studies on coverage control for multi-agent systems are limited to two-dimensional environments, with few addressing the height factor critical to three-dimensional spaces. This study proposes a novel approach that adapts centroidal Voronoi tessellation (CVT) with a time-varying density function and Control Barrier Functions (CBFs) for dynamic coverage in 3D environments. By reconfiguring these methodologies, this approach enhances distribution and coordination efficiency within 3D spaces while ensuring safe, collision-free navigation. The simulation results validate the effectiveness of the proposed approach, demonstrating its potential for the efficient deployment of multi-robot systems, such as unmanned autonomous vehicles and unmanned autonomous underwater vehicles, in diverse operational contexts.

This paper addresses safe coverage control for multi-agent systems on a spherical surface with obstacles. While centroidal Voronoi tessellation has been widely used for distributed coverage generation, its extension to spherical environments with safety constraints remains relatively underexplored. To address this problem, we propose a spherical coverage control framework that integrates spherical centroidal Voronoi tessellation with control barrier functions. The proposed method assigns coverage objectives through spherical Voronoi partitioning and corresponding centroid computation, while safety is enforced through barrier-based constraints that prevent inter-agent collisions and obstacle collisions during motion. In addition, a surface-retention mechanism is incorporated to mitigate agent drift away from the desired spherical surface. The method is examined in a Python-based simulation environment with multiple obstacle layouts. The results demonstrate that the proposed framework enables safe distributed coverage over a spherical surface and that the surface-retention mechanism improves conformity of the agent trajectories to the target spherical manifold. The proposed approach provides a practical basis for safe multi-agent deployment in spherical environments.

Unmanned aerial vehicles (UAVs) are increasingly used in applications such as inspection and search and rescue, yet safe and intuitive teleoperation remains challenging in cluttered and GPS-denied environments. This paper presents a modular haptic teleoperation framework that integrates Unity, ROS2, optical motion capture, a physical UAV platform, and a stylus-based haptic device for bidirectional human–robot interaction. Operator inputs are mapped to UAV velocity commands, while obstacle proximity is rendered as continuous haptic feedback to enhance spatial awareness. A Control Barrier Function (CBF)-based command filtering layer is incorporated to modify unsafe velocity commands in real time. The system is evaluated through both Unity-based simulation and real-world experiments using a DJI Tello UAV and OptiTrack motion capture. In the virtual experiments, four conditions were compared: baseline, CBF only, haptic only, and CBF + haptic. The combined CBF + haptic condition reduced the average task completion time from 62.9 s to 42.8 s and resulted in no observed collisions under the evaluated virtual scenarios. The real-world experiments further confirmed stable force–distance behavior, bounded latency, and feasible haptic-assisted UAV navigation in a constrained indoor environment. These results indicate that combining haptic feedback with CBF-based safety control can improve teleoperation efficiency, safety, and usability under the tested conditions while providing a practical step toward simulation-to-real haptic UAV teleoperation.