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Detecting transportation pipeline leakage points within chemical plants is difficult due to complex pathways, multi-dimensional survey points, and highly dynamic scenarios. However, hexapod robots' maneuverability and adaptability make it an ideal candidate for conducting surveys across different planes. The path-planning problem of hexapod robots in multi-dimensional environments is a significant challenge, especially when identifying suitable transition points and planning shorter paths to reach survey points while traversing multi-level environments. This study proposes a Particle Swarm Optimization (PSO)-guided Double Deep Q-Network (DDQN) approach, namely, the PSO-guided DDQN (PG-DDQN) algorithm, for solving this problem. The proposed algorithm incorporates the PSO algorithm to supplant the traditional random selection strategy, and the data obtained from this guided approach are subsequently employed to train the DDQN neural network. The multi-dimensional random environment is abstracted into localized maps comprising current and next level planes. Comparative experiments were performed with PG-DDQN, standard DQN, and standard DDQN to evaluate the algorithm's performance by using multiple randomly generated localized maps. After testing each iteration, each algorithm obtained the total reward values and completion times. The results demonstrate that PG-DDQN exhibited faster convergence under an equivalent iteration count. Compared with standard DQN and standard DDQN, reductions in path-planning time of at least 33.94% and 42.60%, respectively, were observed, significantly improving the robot's mobility. Finally, the PG-DDQN algorithm was integrated with sensors onto a hexapod robot, and validation was performed through Gazebo simulations and Experiment. The results show that controlling hexapod robots by applying PG-DDQN provides valuable insights for path planning to reach transportation pipeline leakage points within chemical plants. [ABSTRACT FROM AUTHOR]

A systematic method is employed for the design and analysis of a small size eddy current (EC) displacement sensor. Simulations are first performed to determine the optimal winding structure and dimensions of the sensor. A linear-fitting approach is then developed for converting the AC displacement signal of the sensor to a DC signal. Finally, a compensation method is proposed for mitigating the temperature drift of the EC sensor under different working temperatures. The experimental results show that the proposed sensor has a sensitivity of approximately 3 μm, a working temperature range of 25–55 °C, and a linearity of ±1.025%. [ABSTRACT FROM AUTHOR]

Free-range chicken farming allows egg-laying hens to move freely through their environment and perform their natural behavior, including laying her eggs. However, it takes time to gather these eggs manually, giving rise to high labor costs. This study proposes a smart mobile robot for poultry farms that can recognize eggs of two different colors on free-range farms. The robot can also pick up and sort eggs without damaging them. An egg feature extraction method with automatic thresholding is employed to detect both white and brown eggs, and a behavior-based navigation method is applied to allow the robot to reach the eggs while avoiding obstacles. The robot can move towards the position of each egg via visual tracking. Once the egg is within the collection area of the robot, it is gathered, sorted and stored in the tank inside the robot. Experiments are carried out in an outdoor field of size 5 m × 5 m under different climatic conditions, and the results showed that the average egg recognition rate is between 94.7% and 97.6%. The proposed mobile poultry robot is low in production cost and simple in operation. It can provide chicken farmers with automatic egg gathering on free-range farms. [ABSTRACT FROM AUTHOR]

The use of engineered cementitious composite (ECC) has attracted extensive attention in recent years because of the highly enhanced ductility owing to its unique strain-hardening behavior. In this paper, an electromechanical impedance-based technique is used to monitor the structural damage of RC beams strengthened with an ECC layer at the tensile zone. To achieve this purpose, three specimens are tested under bending loads to evaluate the proposed damage detection methodology. Five externally bonded PZT transducers are uniformly distributed at the surface of the ECC layer of the beams to measure the output conductance signatures in a healthy state and in different damage scenarios induced by different load levels. Test results showed that discrepancies exist between the signals measured in the intact state and each damage state, which can be used to evaluate the structural integrity changes. To assess the damage of ECC-concrete composite beams quantitatively, the statistical scalar index-root mean square deviation (RMSD) is used as the index, which can be calculated from the variations of conductance measurements of PZT sensors. The damage index values of the uniformly distributed PZT sensors provided cogent evidence of damage and revealed the evolution of structural damage. The crack patterns of beams at different damage levels are compared with the damage index values, and it shows the damage location can be derived from the measured conductance signatures of an array of PZT transducers. [ABSTRACT FROM AUTHOR]

This paper presents a resonant pressure microsensor with the measurement range of 1 MPa suitable for the soaring demands of industrial gas pressure calibration equipment. The proposed microsensor consists of an SOI layer as a sensing element and a glass cap for vacuum packaging. The sensing elements include a pressure-sensitive diaphragm and two resonators embedded in the diaphragm by anchor structures. The resonators are excited by a convenient Lorentz force and detected by electromagnetic induction, which can maintain high signal outputs. In operation, the pressure under measurement bends the pressure-sensitive diaphragm of the microsensor, producing frequency shifts of the two underlining resonators. The microsensor structures were designed and optimized using finite element analyses and a 4" SOI wafer was employed in fabrications, which requires only one photolithographic step. Experimental results indicate that the Q-factors of the resonators are higher than 25,000 with a differential temperature sensitivity of 0.22 Hz/°C, pressure sensitivities of 6.6 Hz/kPa, and −6.5 Hz/kPa, which match the simulation results of differential temperature sensitivity of 0.2 Hz/°C and pressure sensitivities of ±6.5 Hz/kPa. In addition, characterizations based on a closed-loop manner indicate that the presented sensor demonstrates low fitting errors within 0.01% FS, high accuracy of 0.01% FS in the pressure range of 20 kPa to 1 MPa and temperature range of −55 to 85 °C, and the long-term stability within 0.01% FS in a 156-day period under the room temperature. [ABSTRACT FROM AUTHOR]

This paper presents a resonant pressure microsensor relying on electrostatic excitation and piezoresistive detection where two double-ended tuning forks were used as resonators, enabling differential outputs. Pressure under measurement caused the deformation of the pressure sensitive membrane, leading to stress buildup of the resonator under electrostatic excitation with a corresponding shift of the resonant frequency detected piezoresistively. The proposed microsensor was fabricated by simplified SOI-MEMS technologies and characterized by both open-loop and closed-loop circuits, producing a quality factor higher than 10,000, a sensitivity of 79.44 Hz/kPa and an accuracy rate of over 0.01% F.S. In comparison to the previously reported resonant piezoresistive sensors, the proposed device used single-crystal silicon as piezoresistors, which was featured with low DC biased voltages, simple sensing structures and fabrication steps. In addition, the two double-ended tuning forks were used as resonators, producing high quality factors and differential outputs, which further improved the sensor performances. [ABSTRACT FROM AUTHOR]