Learning 3D shape proprioception for continuum soft robots with multiple magnetic sensors.

Sensing the shape of continuum soft robots without obstructing their movements and modifying their natural softness requires innovative solutions. This letter proposes to use magnetic sensors fully integrated into the robot to achieve proprioception. Magnetic sensors are compact, sensitive, and easy to integrate into a soft robot.

Closed-Loop Control Through Self-Sensing of a Joule-Heated Twisted and Coiled Polymer Muscle.

The twisted and coiled polymer muscle (TCPM) has two major benefits: low weight and low cost. Therefore, this new type of actuator is increasingly used in robotic applications where these benefits are relevant. Closed-loop control of these muscles, however, requires additional sensors that add weight and cost, negating the muscles' intrinsic benefits.

A Big Data Analysis Approach for Rail Failure Risk Assessment.

Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras.

Approximate analytical solutions to the double-stance dynamics of the lossy spring-loaded inverted pendulum.

This paper introduces approximate time-domain solutions to the otherwise non-integrable double-stance dynamics of the 'bipedal' spring-loaded inverted pendulum (B-SLIP) in the presence of non-negligible damping. We first introduce an auxiliary system whose behavior under certain conditions is approximately equivalent to the B-SLIP in double-stance. Then, we derive approximate solutions to the dynamics of the new system following two different methods: (i) updated-momentum approach that can deal with both the lossy and lossless B-SLIP models, and (ii) perturbation-based approach following which we only derive a solution to the lossless case.

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks.

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults are diagnosed from their spatial and temporal dependences.