Attractive Force Estimation of a Magnetic Adsorption Unit for Inspection UAVs

This paper presents an attractive force estimation system and an automatic activation system for an electropermanent magnet (EPM) for an inspection UAV. Adsorption to infrastructures for inspection at a distance is extremely difficult to perform safely because the operator cannot detect the state of adsorption of the drone equipped with a magnetic adsorption device. Therefore, in this paper, we clarify the relationship between the magnetic flux density and attractive force of the EPM through experiments, and develop an estimation algorithm for the attractive force based on the results. An automatic activation system, using the induced voltage in the coil when the EPM approaches the magnetic substance, is developed and mounted on a quadrotor for a flight experiment along with the estimation system for the attractive force. The developed system is verified using flight and adsorption experiments on the quadrotor.

Non-contact bolt axial force measurement based on the deformation of bolt head using quartz crystal resonator and coils

As a health monitoring tool of bolts in infrastructures, we propose a non-contact evaluation method for the axial force of a bolt. Deformation of the bolt head is measured as an electrical capacitance variation detected as a frequency shift of a simple circuit composed of a quartz crystal resonator and coils. The measurement was carried out via magnetic field coupling between the coil installed on the bolt head and another coil connected to the measurement instrument. Since the method requires no active electronic circuit or battery for the bolt, low cost and high durability can be expected. First, the circuit was analyzed and optimized using an equivalent circuit model. Then, the feasibility of the proposed method was experimentally studied using a prototype. It was demonstrated that the method enabled non-contact axial force estimation in which the dependence on the distance between the coils is sufficiently small for detecting bolt looseness.

End-Effector Force and Joint Torque Estimation of a 7-DoF Robotic Manipulator Using Deep Learning

When a mobile robotic manipulator interacts with other robots, people, or the environment in general, the end-effector forces need to be measured to assess if a task has been completed successfully. Traditionally used force or torque estimation methods are usually based on observers, which require knowledge of the robot dynamics. Contrary to this, our approach involves two methods based on deep neural networks: robot end-effector force estimation and joint torque estimation. These methods require no knowledge of robot dynamics and are computationally effective but require a force sensor under the robot base. Several different architectures were considered for the tasks, and the best ones were identified among those tested. First, the data for training the networks were obtained in simulation. The trained networks showed reasonably good performance, especially using the LSTM architecture (with a root mean squared error (RMSE) of 0.1533 N for end-effector force estimation and 0.5115 Nm for joint torque estimation). Afterward, data were collected on a real Franka Emika Panda robot and then used to train the same networks for joint torque estimation. The obtained results are slightly worse than in simulation (0.5115 Nm vs. 0.6189 Nm, according to the RMSE metric) but still reasonably good, showing the validity of the proposed approach.

A Mirror Bilateral Neuro-Rehabilitation Robot System with the sEMG-Based Real-Time Patient Active Participant Assessment

In this paper, a novel mirror visual feedback-based (MVF) bilateral neurorehabilitation system with surface electromyography (sEMG)-based patient active force assessment was proposed for upper limb motor recovery and improvement of limb inter-coordination. A mirror visual feedback-based human–robot interface was designed to facilitate the bilateral isometric force output training task. To achieve patient active participant assessment, an sEMG signals-based elbow joint isometric force estimation method was implemented into the proposed system for real-time affected side force assessment and participation evaluation. To assist the affected side limb efficiently and precisely, a mirror bilateral control framework was presented for bilateral limb coordination. Preliminary experiments were conducted to evaluate the estimation accuracy of force estimation method and force tracking accuracy of system performance. The experimental results show the proposed force estimation method can efficiently calculate the elbow joint force in real-time, and the affected side limb of patients can be assisted to track output force of the non-paretic side limb for better limb coordination by the proposed bilateral rehabilitation system.