Although the off-road mobility characteristics of wheeled or tracked vehicles are generally recognized as being inferior to those of man and cursorial animals, the complexity of the joint-coordination control problem has thus far frustrated attempts to achieve improved vehicular terrain adaptability through the application of legged locomotion concepts. Nevertheless, the evident superiority of biological systems in this regard has motivated a number of theoretical studies over the past decade which have now reached a state of maturity sufficient to permit the construction of experimental computer-controlled adaptive walking machines. At least two such vehicles are known to have recently demonstrated legged locomotion over smooth hard-surfaced terrain. This paper is concerned with an extension of the present theory of limb coordination for such machines to the case in which the terrain includes regions not suitable for weight-bearing and which must consequently be avoided by the control computer in deciding when and where to successively place the feet of the vehicle. The paper includes a complete problem formalization, a heuristic algorithm for solution of the problem thus posed, and a preliminary evaluation of the proposed algorithm in terms of a computer simulation study.

Adaptive Locomition of a Multilegged Robot over Rough Terrain

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

Insect-Inspired Robots: Bridging Biological and Artificial Systems.

https://www.mdpi.com/2218-6581/9/2/23/pdf

Static Balancing of Wheeled-legged Hexapod Robots

This article introduces a tracked-leg transformable robot, TALBOT. The mechanical and electrical design, control method, and environment perception based on LiDAR are discussed. The original tracked-leg transformable structure allows the robot to switch between the tracked and legged mode to achieve all-terrain adaptation. In the tracked mode, TALBOT is controlled by the method of differential speed between the two tracked feet. In the legged mode, TALBOT is controlled based on a bionic control strategy of the central pattern generator to realize the generation and conversion of gait. In addition, the robot is equipped with a LiDAR, through sensor preprocessing and optimization of the slam mapping algorithm, so that the robot achieves a better mapping effect. We tested the robot’s motion performance and the slam mapping effect, including going straight and turning in tracked and legged modes and building a map in an indoor environment.

/pdf/talbot-a-track-leg-transformable-robot-3hsfsv1w.pdf

TALBOT: A Track-Leg Transformable Robot

Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm‐like robot segments are often assigned holistically and artificially by mimicking the earthworms’ retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates the authors to extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control. Here, the spatiotemporal dynamics is exploited from the coupled Hopf oscillators to not only unify the two existing gait generators for restoring temporal‐symmetric phase‐coordinated gaits and discrete gaits but also generate novel temporal‐asymmetric phase‐coordinated gaits. Theoretical and experimental tests consistently confirm that the introduction of temporal asymmetry improves the robot's locomotion performance. The CPG‐based controller also enables seamless online switching of locomotion gaits to avoid abrupt changes, sharp stops, and starts, thus improving the robot's adaptability in variable working scenarios.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202206336

A CPG‐Based Versatile Control Framework for Metameric Earthworm‐Like Robotic Locomotion

The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

/pdf/modeling-and-analysis-of-acoustic-emission-generated-by-2yzuj4c6.pdf

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

The failure of insulators may seriously threaten the safe operation of the power system, where the state detection of high-voltage insulators is a must for the normal and safe operation of the power system. Based on the data of insulators in aerial images, this work explored an enhanced particle swarm algorithm to optimize the parameters of the support vector machine. A support vector machine model was therefore established for the identification of the normal and defective states of the insulators. This methodology works with the structure minimization principle of SVM and the characteristics of particle swarm fast optimization. First, the aerial insulator image was segmented as a target by way of the seed region growth based on double-layer cascade morphological improvements, and then, HOG features plus GLCM features were extracted as sample data. Finally, an ameliorated PSO-SVM classifier was designed to realize insulator state identification. Comparisons were made between PSO-SVM and conventional machine learning algorithms, SVM and Random Forest, and an optimization algorithm, Gray Wolf Optimization Support Vector Machine (GWO-SVM), and advanced neural network CNN. The experimental results showed that the performance of the algorithm proposed in this paper touched the top level, where the recognition accuracy rate was 92.11%, the precision rate 90%, the recall rate 94.74%, and the F1-score 92.31%.

/pdf/a-new-approach-to-optimize-svm-for-insulator-state-2oh56j5z.pdf

A New Approach to Optimize SVM for Insulator State Identification Based on Improved PSO Algorithm

The invention discloses a wheel web type integrated travelling mechanism. The wheel web type integrated travelling mechanism comprises a deformation leg of a chain structure and a deformation mechanism used for driving the deformation leg; the deformation leg comprises a plurality of deformation joints hinged sequentially, and the deformation joints are provided with arc sliding grooves formed inthe longitudinal direction of the deformation leg; and the deformation mechanism comprises a sliding chain and a deformation driving mechanism, wherein the sliding chain is formed by hinging a plurality of arc sliding blocks sequentially, and the deformation driving mechanism is used for driving the reciprocating sliding of the sliding chain in the arc sliding grooves of the deformation leg. According to the wheel web type integrated travelling mechanism, the arc sliding blocks and the arc sliding grooves can be concentric completely, the smooth, accurate and quick deformation of a robot is ensured, resistance borne by the robot in the deformation process is reduced, and the phenomenon of unfinished deformation is avoided.

Wheel web type integrated travelling mechanism

This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is applied in the CPG control, which can realize multiple gaits by adjusting a simple parameter. In addition, a smooth gait switching method is also proposed via smooth gait transition functions and gait planning. Tripod gait, quadruped gait, and wave gait are planned for the hexapod robot to achieve quick and stable gait transitions smoothly and continuously. MATLAB and ADAMS simulations and corresponding practical experiments are conducted. The results show that the proposed method can achieve smooth and continuous mutual gait transitions, which proves the effectiveness of the proposed CPG-based hexapod robot control.

CPG-Based Gait Generation of the Curved-Leg Hexapod Robot with Smooth Gait Transition.

In recent years, intelligent fault diagnosis has been widely investigated. Deep learning fault diagnosis methods for rotating machinery are primarily based on the assumption that labeled data are abundant. However, it is difficult to collect sufficient labeled samples for some practical engineering applications. To address this problem, a fault diagnosis method for rotating machinery with semi-supervised graph convolutional network (SSGCN) and 2-D images converted from vibration signals is proposed in this article. First, the 1-D vibration signals are converted into 2-D images, by the non-parameter adjacency symmetrized dot pattern (NPASDP) method in polar coordinate system and the grayscale matrix mapping (GMM) method in the Cartesian coordinate system. The local features of all samples are fully extracted using image processing methods in different coordinate systems to better characterize the relationship between different samples. Second, the normalized cross correlation coefficient (NCC) is used to calculate the similarity of images. Using each sample as a node, the graph structure is constructed by judging the adjacency relationship between all nodes through the similarity threshold. Finally, the SSGCN model is established to realize fault diagnosis. A bearing dataset and a rotor dataset with few data labels are used for validation, and the proposed method can get satisfactory fault diagnosis accuracy. The results show that the method can effectively improve the accuracy of model fault diagnosis with few labeled data.

A Fault Diagnosis Method for Rotating Machinery With Semi-Supervised Graph Convolutional Network and Images Converted From Vibration Signals

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