Energy Management

Human activity wearable obstacle detection for the visually impaired (VI) is developed for routine monitoring and observation of surrounding events. Environmental observation, home surveillance, and assistive supports are now built on wearable devices using Inertia-based sensors, such as accelerometers, linear acceleration, and gyroscopes. Previous assisted living system (ALS) still faces challenges in energy management and resource allocation when performing daily activities, particularly with ambulation. Legacy systems cannot fully improve self-esteem, hence, WearROBOT.


This dataset is used to assess the energy consumption of the collaborative robot UR3e. The dataset consists of six experiments. Different operational conditions are modified in each experiment to determine their influence. The datasets have the following information: recording time, trajectory ID, joints' positions, joints' velocities, motor currents, motor torques, motor voltages, motor temperatures, current, and voltage of the robot. This dataset is used as a study case of a new methodology for energy assessment.