In recent years, the use of multirotor unmanned aerial vehicles (UAVs) has expanded significantly across various fields. Concurrently, aerial manipulation has emerged as a thriving area, leading to the development of diverse aerial manipulators [1]. The most common method is the integration of arms into multirotors [2–4]. Utilizing rotor-concentrated manipulators (RCMs), these drones can perform various manipulation tasks [5–7].
We present here proof of concept for a smart energy-efficient hybrid gait monitoring system. It consists of a hybrid Inertial Measuring Unit (IMU) and Triboelectric Nanogenerator (TENG) sensing module, and it utilizes multiple strategies to reduce the computational load and energy overhead of the device while maintaining accuracy. It meets the lower energy demands through a self-powered mechanism, thus eliminating the need for batteries. Firstly, a TENG harvests the biomechanical energy of human movement and is optimized for high energy output. Then the IMU sensor collects and processes only sparse data. An algorithm is implemented to select the best sampling points in the gait data, and it adapts as it gains knowledge of the user's walking pattern. The IMU sensor is powered intermittently through the TENG and a power management unit while the person is walking. Finally, a TinyML algorithm is employed for data intelligence at the device level.
This paper details the development, implementation and experimental evaluation of an interface-aware, task-agnostic assistance system for shared human-robot teleoperation, specifically applied to a 7-DoF robotic arm. The system addresses a limitation of current shared-control methods by considering the impact of control interfaces on user input precision and the robot agent's incomplete understanding of the human's policy. The approach is evaluated in empirical case studies involving participants with spinal cord injuries. The personalized assistance system improves safety and reduces cognitive load.
In this paper, we investigate the strategies used by a skilled ultrasound physician and explore the functionality of a robotic ultrasound scan acquisition system. We develop a framework to record an expert radiologist's probe motion, probe force, audio narration, and generated ultrasound images. Then, we optimize the acquired scan data to remove redundant sections and use the processed data to perform an autonomous survey scan with the robotic system. Finally, the radiologist performs a full detailed scan of the abdomen through a haptic user interface capable of long-distance remote interaction. We compare images generated at similar points in each scan and identify the same anatomy and pathologies, thus demonstrating equivalent diagnostic performance between the skilled ultrasound physician and robotic system.
This paper presents a distributed, cooperative persistent coverage control for multi-robotic systems with limited communication and limited sensing range. The control framework synthesizes computational geometry-based algorithms with an awareness-based model to incentivize the frequent coverage of every point of interest in a large-scale domain. Theoretical results on the stability and convergence of awareness are given. The performance of the control approach is demonstrated via an experiment with a hexapod in a simulated cooperative environment.
