The purpose this paper is the development a novel polymeric fiber-optic magnetostrictive metal detector, using a fiber–optic Mach-Zehnder interferometer and polymeric magnetostrictive material. Metal detection is based on the strain-induced optical path length change steming from the ferromagnetic material introduced in the magnetic field. Varied optical phase shifts resulted largely from different metal objects. In this paper, the preliminary results on the different metal material detection will be discussed.
This paper presents a new metal detector using a fiberoptic
magnetostriction sensor. The metal sensor uses a fiber-optic Mach-Zehnder
interferometer with a newly developed ferromagnetic polymer as the
magnetostrictive sensing material. This polymeric magnetostrictive fiberoptic
metal sensor is simple to fabricate, small in size, and resistant to RF
interference (which is common in typical electromagnetic type metal detectors).
Metal detection is based on disruption of the magnetic flux density across the
magnetostriction sensor. In this paper, characteristics of the material being
sensed and magnetic properties of the ferromagnetic polymers will be
discussed.
The purpose of this paper is to develop a novel ferromagnetic polymeric metal detector system by using a fiber-optic Mach-Zehnder interferometer with a newly developed ferromagnetic polymer as the magnetostrictive sensing device. This ferromagnetic polymeric metal detector system is simple to fabricate, small in size, and resistant to RF interference (which is common in typical electromagnetic type metal detectors). Metal detection is made possible by disrupting the magnetic flux density present on the magnetostrictive sensor. This paper discusses the magnetic properties of the ferromagnetic polymers. In addition, the preliminary results of successful sensing of different geometrical metal shapes will be discussed.
This paper addresses the challenge of terrainadaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learning (RL). Optimization-based methods, such as model-predictive control, excel in finding optimal reaction forces and achieving agile locomotion, especially in quadruped, but struggle with the nonlinear hybrid dynamics of legged systems and the real-time computation of step location, timing, and reaction forces. Conversely, RL-based methods show promise in navigating dynamic and rough terrains but are limited by their extensive data requirements. We introduce a novel locomotion architecture that integrates a neural network policy, trained through RL in simplified environments, with a state-of-the-art motion controller combining model-predictive control (MPC) and whole-body impulse control (WBIC). The policy efficiently learns high-level locomotion strategies, such as gait selection and step positioning, without the need for full dynamics simulations. This control architecture enables humanoid robots to dynamically navigate discrete terrains, making strategic locomotion decisions (e.g., walking, jumping, and leaping) based on ground height maps. Our results demonstrate that this integrated control architecture achieves dynamic locomotion with significantly fewer training samples than conventional RLbased methods and can be transferred to different humanoid platforms without additional training. The control architecture has been extensively tested in dynamic simulations, accomplishing terrain height-based dynamic locomotion for three different robots.
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