A novel bionic mantis shrimp robot for tracking underwater moving objects

The four-legged bionic robot for agriculture has been one of the hotspots in the field of robotics. The farmland environment is complex, and the balance requirement of quadruped bionic robot is higher. However, head adjustment is a key factor affecting the balance of quadruped animals. Based on this, this study takes the four-legged animal goat as an example to investigate the effect of goat head movement adjustment on the balance of goat. In this study, the head and neck of goats were simplified to a two-link model. The kinematics model was established by D-H method. MATLAB software was used to solve it. Origin software was used to draw the movement track of the center of mass of the head. The center of mass movement of the head is a quarter ellipse. Then, the goat walking experiments were carried out on the different slopes (0°, 5°, 10°, 15°, 20°, 25°, 30°, 35° and 40°). MATLAB software was used to fit the movement data of goat head. The movement of the center of mass of the goat's head corresponds to Fourier 6. The result is: the determination coefficient is 0.8629, and the mean variance is 0.019. When the slope gradient gradually increases, the volatility gradually increases and is cyclical. The results of this study verify the rationality of the four-legged bionic mechanism under various parameters and reveal the mechanism of goat walking with an appendage at the head. Keywords: bionic four-legged robot, slope, head, center of mass, trajectory, balance DOI: 10.25165/j.ijabe.20191204.4287 Citation: Zhang F, Wang Y F, Teng S, Zheng L M, Wang J J, Chen Z J. Development of agricultural bionic four-legged robot: Effect of head movement adjustment on the stability of goats. Int J Agric & Biol Eng, 2019; 12(4): 10–15.

This paper focuses on bionic robots in the age of intelligence and discusses their research directions and application prospects. Traditional robots often encounter challenges such as poor flexibility and limited sensory and decision-making abilities, which restrict their adaptability to complex and dynamic environments. Bionic robots, inspired by the structures and behaviours of living organisms, offer solutions by enhancing flexibility, perception, and environmental adaptability. Modern bionic principles encompass behavioural patterns, structural bionics, functional bionics, control systems, and new materials. Advanced research fields include bionic sensing systems, such as bionic skin capable of multi-sensory feedback, bionic drive technologies, like flexible drive robots, and the application of new smart materials. These technologies have found applications across a range of sectors, including healthcare, industrial operations, special environment exploration, and national defence, demonstrating significant potential for real-world impact. However, the development of bionic robots still faces challenges, particularly related to high production costs and insufficient adaptability for diverse environments. In the future, research is expected to move toward smarter, more versatile functionalities, with improvements in cost efficiency and system reliability. These advancements are anticipated to drive transformation across multiple industries and contribute to significant breakthroughs in robotic technology. This study provides a theoretical foundation and practical guidance for the ongoing research and future development of bionic robots.

To enhance the motion flexibility and environmental adaptability of underwater robots, this study proposes a novel design, Seeker-M, inspired by the locomotion mechanism of the mantis shrimp. The robot imitates the mantis shrimp's multi-pleopod swimming mode and has multi-modal locomotion ability. The robot features a multifunctional flexible spine capable of active bending (maximum angle of 30°) and dynamic center of gravity adjustment (up to 30% of body length). A pitch control system is developed based on this adjustable structure, employing the linear active disturbance rejection control (LADRC) algorithm. Experimental results demonstrate that the LADRC algorithm maintains robust attitude stability under disturbances from pleopod motion, offering an effective approach for underwater attitude control in complex environments.

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

Time and fuel balance is an important topic in the dynamic tracking problem of unmanned systems. In this paper, we propose a quasi time-fuel optimal control strategy (QTFOC) to solve the dynamic tracking problem of a double integrator system, which is capable of multi-target switching tracking tasks, such as the multi-target strike of weapons and the rapid multi-target grabbing of robots on industrial assembly lines. Compared with the previous optimal control algorithms, the proposed controller retains the time-fuel optimal characteristic when switching among multiple dynamic targets, and overcomes the high-frequency oscillation problem by incorporating local linear control region and two nonlinear buffer areas. That is, when switching among multiple dynamic targets, the proposed control strategy enables the corresponding system to achieve desired transient performance and satisfactory steady-state performance simultaneously. In addition, the asymmetry of friction load is further explored, which affects the dynamic performance of the actual system. Extensive experiments based on the visual tracking turntable demonstrate the superiority and feasibility of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of multi-target switching tracking, such as the multi-target strike of weapons with vision sensors and the rapid multi-target grabbing of robots on industrial assembly lines. In recent years, various algorithms have been developed for trajectory planning and tracking. However, it is still challenging for unmanned servo system to capture dynamic targets quickly due to the constrains of motion performance, energy, and computational power. In order to reduce the computational burden and obtain ideal response under various physical constraints, a quasi time-fuel optimal control strategy (QTFOC) with analytical solutions is proposed in this paper. First, the static target is extended to the dynamic target by further investigating the traditional time-fuel optimal control theory for double integrator system. Second, to deal with the oscillation problem caused by system disturbances, we incorporate buffer areas and local linear control region into the control algorithm, which makes the system more robust. Third, the system performance is further improved by analyzing the frictional load asymmetry. This algorithm requires small computational resources and can be implemented directly on microcontrollers such as STM32. The experimental results demonstrate the superiority of our proposed method in handling the multi-target switching tracking problem, which can also adjust the response speed weight, making the unmanned system perform better under different operating conditions. Furthermore, the proposed QTFOC can be extended to other unmanned systems, such as trajectory planning and motion control for unmanned vehicles and bionic robots.

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

With the deepening of human exploration of the Earth, more and more survey tasks are being carried out in unstructured environments. Biomimetic robots that can replace humans in completing related tasks under harsh conditions have gradually become a hot topic in robot development. Therefore, developing a biomimetic robot with a wide range of activities, strong mobility, excellent obstacle crossing ability, and rapid avoidance response has important theoretical research significance and broad application prospects. This paper focuses on frogs as the research object,research is conducted from the perspective of institutional design, aiming to design a frog like jumping robot with strong obstacle crossing ability and good environmental adaptability. In terms of overall structural design, a M-shaped structure based on linear bearings was proposed to ensure the stability of the robot during takeoff and landing; In terms of leg structure design, C-shaped flexible springs are used to integrate the calf, ankle joint, and foot into one, which can play a buffering role when the robot lands; In terms of transmission structure design, Propose to control the compression cycle of the jumping leg through a cam to achieve continuous jumping of the robot. The relevant research results indicate that the structural principle of the designed frog like jumping robot is correct, and the jumping effect is relatively good.For the sake of ideals, this paper provides a certain theoretical basis and basis for further research on bionic jumping robots.

The tracking of underwater targets represents a basic unit in underwater surveillance for offering effective search and rescue operations. However, the tracking of the target is a major constraint in finding the underwater target. This paper devises a model for underwater target tracking considering radar signals. Radar signals are subjected to the image reconstruction process to make them suitable for further processing. The gridding is the next step, which is utilized for generating the grids to focus on each region in a precise manner. The features like Ridgelet transform and Local Gabor XOR Pattern (LGXP) are produced. Data augmentation is the next process, which helps to reduce the size of data by performing resizing. At last, the tracking of targets is performed from each grid with a Deep neuro fuzzy network (DNFN). Here, DNFN training is performed with Exponential-Competitive Swarm Optimization (E-CSO), which is derived by integrating Exponential Weighted Moving Average (EWMA) and Competitive Swarm Optimization (CSO). The proposed E-CSO-based DNFN offered high efficiency with a maximal detection rate of 98.8%, minimal Mean square error of 0.012, and Mean Absolute Percentage Error (MAPE) of 17.883.

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

Design and depth control of bionic mantis shrimp underwater robot with miniaturized low-cost sinking and floating system

Corrigenda

Using traditional sonar arrays may be difficult and impractical in some mission-critical scenarios, because they should be mounted on or towed by a ship or a submersible. Alternatively, underwater acoustic sensor networks (UW-ASNs) offer a promising solution approach. In this paper, a target-tracking algorithm for UW-ASNs, i.e., 3-D underwater target tracking (3DUT), is presented. The objective of 3DUT is to collaboratively accomplish accurate tracking of underwater targets with minimum energy expenditure. Based on the time of arrival of the echoes from the target after transmitting acoustic pulses from the sensors, the ranges of the nodes to the target are determined, and trilateration is used to obtain the location of the target. The location and the calculated velocity of the target are then exploited to achieve tracking. To realize energy-effective target tracking, 3DUT incorporates a new target-movement-based duty-cycle mechanism. To avoid rapid depletion of the energy resources of boundary nodes due to continuous surveillance, 3DUT employs an adaptive procedure to find, designate, and activate new boundary nodes. Performance evaluation shows that 3DUT is a promising alternative to the traditional sonar-based target-tracking approaches, particularly for on-demand surveillance applications.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UW...

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.