The monitoring of vehicle motion states is a key factor to ensure smooth, safe, and efficient management of traffic in intelligent transportation systems. However, employing multiple sensors for vehicle motion states monitoring not only increases system costs but also complicates the wiring. Here, we propose an integrated magnetic-assisted self-powered vehicle motion sensor (MSVMS) based on a triboelectric nanogenerator for real-time monitoring of vehicle motion states, including acceleration, angular speed, and inclination angle. By introducing a magnetic repulsion adjustment system, the sensor can achieve automatic resetting and effectively monitor the vehicle’s motion state during normal driving. Experimental results indicate that the electromagnetic generator (EMG) unit can achieve a maximum peak power of 4.5 mW at an optimal load resistance of 1 kΩ. Meanwhile, the triboelectric nanogenerator (TENG) unit demonstrated good sensing performance for acceleration, angular speed, and inclination angle, with fitting coefficients of 0.99, 0.979, and 0.978, respectively. Finally, the feasibility of the MSVMS in monitoring acceleration magnitude and direction is verified in a vehicle motion sensing system and actual vehicle test scenarios. This work further validates the potential application prospects of MSVMS in intelligent transportation systems.

Redundant manipulators utilize their redundant solutions to achieve the position and orientation control of the end-effector in a given variety of complex tasks, which is an essential issue in the field of industrial robots. Moreover, for manipulators with unknown models, traditional control methods generate large control errors during the execution of the task or even lead to the failure of the task. To address this problem, this paper proposes a Discrete Data-Driven Position and Orientation Control (D³POC) scheme. The scheme consists of a Discrete Jacobian Matrix Learning (DJML) algorithm, a Discrete Gradient Neural Dynamics (DGND) solver, and a Kalman filter. Then, theoretical analyses are provided to demonstrate the convergence of the D³POC scheme. Subsequently, simulations, comparisons, and experiments based on this scheme are carried out on redundant manipulators. The obtained results indicate the validity, superiority, and practicability of the proposed control scheme.