Robot Tracking Research Articles

Research on Neural Network Terminal Sliding Mode Control of Robotic Arms Based on Novel Reaching Law and Improved Salp Swarm Algorithm

Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM), which combines fast nonsingular terminal sliding mode (FNTSM) control, radial basis function (RBF) neural network, and an improved salp swarm algorithm (ALSSA). This method effectively enhances the trajectory tracking accuracy of robotic arms under the influence of uncertain factors. Firstly, the fast nonsingular terminal sliding surface is utilized to enhance the convergence speed of the system and achieve finite-time convergence. Building upon this, a novel multi-power reaching law is proposed to reduce system chattering. Secondly, the RBF neural network is utilized to estimate and compensate for modeling errors and external disturbances. Then, an improved salp swarm algorithm is proposed to optimize the parameters of the controller. Finally, the stability of the control system is demonstrated using the Lyapunov theorem. Simulation and experimental results demonstrate that the proposed ALSSA-RBFTSM algorithm exhibits superior robustness and trajectory tracking performance compared to the global fast terminal sliding mode (GFTSM) algorithm and the RBF neural network fast nonsingular terminal sliding mode (RBF-FNTSM) algorithm.

Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm

In the grinding process of complex-shaped cast workpieces, discrepancies between the workpiece’s contours and their corresponding three-dimensional models frequently lead to deviations in the machining trajectory, resulting in instances of under-grinding or over-grinding. Addressing this challenge, this study introduces an advanced robotic grinding force automatic tracking technique, leveraging a combination of deep neural networks and genetic algorithms. Harnessing the capability of force sensing, our method dynamically recalibrates the grinding path, epitomizing truly flexible grinding. Initially, in line with the prerequisites for force and pose tracking, an impedance control strategy was developed, integrating pose deviations with force dynamics. Subsequently, to enhance steady-state force tracking, we employed a genetic algorithm to compensate for force discrepancies caused by positional errors. This was built upon the foundational concepts of the three-dimensional model, impedance control, and environmental parameter estimation, leading to an optimized grinding trajectory. Following tracking tests, it was observed that the grinding’s normal force was consistently controlled within the bracket of 20 ± 2.5 N. To further substantiate our methodology, a specialized experimental platform was established for grinding complex-shaped castings. Optimized strategies were employed under anticipated forces of 5 N, 10 N, and 15 N for the grinding tests. The results indicated that the contact forces during the grinding process remained stable at 5 ± 1 N, 10 ± 1.5 N, and 15 ± 2 N. When juxtaposed with conventional teaching grinding methods, our approach manifested a reduction in grinding forces by 71.4%, 70%, and 75%, respectively. Post-grinding, the workpieces presented a pronounced enhancement in surface texture, exhibiting a marked increase in surface uniformity. Surface roughness metrics, originally recorded at 17.5 μm, 17.1 μm, and 18.7 μm, saw significant reductions to 1.5 μm, 1.6 μm, and 1.4 μm, respectively, indicating reductions by 76%, 73%, and 78%. Such outcomes not only meet the surface finishing standards for complex-shaped castings but also offer an efficacious strategy for robot-assisted flexible grinding.

Backstepping Controller for Mobile Robot in Presence of Disturbances and Uncertainties

The objective of this work is to devise an effective control system for addressing the trajectory tracking challenge in nonholonomic mobile robots. Two primary control approaches, namely kinematic and dynamic strategies, are explored to achieve this goal. In the kinematic control domain, a backstepping controller (BSC) is introduced as the core element of the control system. The BSC is utilized to guide the mobile robot along the desired trajectory, leveraging the robot’s kinematic model. To address the limitations of the kinematic control approach, a dynamic control strategy is proposed, incorporating the dynamic parameters of the robot. This dynamic control ensures real-time control of the mobile robot. To ensure the stability of the control system, the Lyapunov stability theory is employed, providing a rigorous framework for analyzing and proving stability. Additionally, to optimize the performance of the control system, a genetic algorithm is employed to design an optimal control law. The effectiveness of the developed control approach is demonstrated through simulation results. These results showcase the enhanced performance and efficiency achieved by the proposed control strategies. Overall, this study presents a comprehensive and robust approach for trajectory tracking in nonholonomic mobile robots, combining kinematic and dynamic control strategies while ensuring stability and performance optimization.

Passivity-Based Trajectory Tracking and Formation Control of Nonholonomic Wheeled Robots Without Velocity Measurements

This note proposes a passivity-based control method for trajectory tracking and formation control of nonholonomic wheeled robots without velocity measurements. Coordinate transformations are used to incorporate the nonholonomic constraints, which are then avoided by controlling the front end of the robot rather than the center of the wheel axle into the differential equations. Starting from the passivity-based coordination design, the control goals are achieved via an internal controller for velocity tracking and heading control and an external controller for formation in the port-Hamiltonian framework. This approach endows the resulting controller with a physical interpretation. To avoid unavailable velocity measurements or unreliable velocity estimations, we derive the distributed control law with only position measurements by introducing a dynamic extension. In addition, we prove that our approach is suitable not only for acyclic graphs but also for a class of non-acyclic graphs, namely, ring graphs. Simulations are provided to illustrate the effectiveness of the approach.

ConservationBots: Autonomous aerial robot for fast robust wildlife tracking in complex terrains

AbstractRadio tagging and tracking are fundamental to understanding the movements and habitats of wildlife in their natural environments. Today, the most widespread,widely applicabletechnology for gathering data relies on experienced scientists armed with handheld radio‐telemetry equipment to locate low‐power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor‐intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real‐world conditions remains a challenge. We developedConservationBots—small aerial robots for tracking multiple, dynamic, radio‐tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times—significant for energy‐limited aerial systems while avoiding close encounters with potential, counterproductive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: (i) planning to determine both trajectory and measurement actions guided by an information‐theoretic objective, which allows the robot to strategically select near‐instantaneous range‐only measurements to achieve faster localization, and time‐consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; (ii) a bearing detector more robust to noise; and (iii) a tracking algorithm formulation robust to missed and false detections experienced in real‐world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high‐resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.

Robust Laguerre‐based model predictive control for trajectory tracking of a mobile robot using an linear matrix inequality (LMI)‐based approach

AbstractThis paper studies the trajectory tracking of a constrained mobile robot under slippery conditions. The goal is to propose a controller for real‐time operations of time‐varying dynamics with insignificant execution time. Therefore, a Laguerre‐based model predictive control (LMPC) is designed, and robustness is provided with an linear matrix inequality (LMI)‐feedback controller. Moreover, a recursive least square (RLS) algorithm with a forgetting factor is utilized to identify the required parameters of the LMI‐based controller. LMPC and LMI‐based controller stability is achieved with suboptimal theory and Lyapunov function, respectively. This algorithm separates the nominal system and introduces new dynamics containing uncertainties. Furthermore, LMPC is removed from the online calculation, which dramatically reduces computation burden and time; consequently, online computation is dedicated to determining the LMI feedback. Simulations are provided to compare the proposed robust controller to its not robust counterpart. Finally, it is demonstrated that this controller diminishes the execution time considerably, making it incomparable to previous robust MPC strategies.

Improved UAV-to-Ground Multi-Target Tracking Algorithm Based on StrongSORT.

Unmanned aerial vehicles (UAV) are essential for aerial reconnaissance and monitoring. One of the greatest challenges facing UAVs is vision-based multi-target tracking. Multi-target tracking algorithms that depend on visual data are utilized in a variety of fields. In this study, we present a comprehensive framework for real-time tracking of ground robots in forest and grassland environments. This framework utilizes the YOLOv5n detection algorithm and a multi-target tracking algorithm for monitoring ground robot activities in real-time video streams. We optimized both detection and re-identification networks to enhance real-time target detection. The StrongSORT tracking algorithm was selected carefully to alleviate the loss of tracked objects due to factors like camera jitter, intersecting and overlapping targets, and smaller target sizes. The YOLOv5n algorithm was used to train the dataset, and the StrongSORT tracking algorithm incorporated the best-trained model weights. The algorithm's performance has greatly improved, as demonstrated by experimental results. The number of ID switches (IDSW) has decreased by sixfold, IDF1 has increased by 7.93%, and false positives (FP) have decreased by 30.28%. Additionally, the tracking speed has reached 38 frames per second. These findings validate our algorithm's ability to fulfill real-time tracking requisites on UAV platforms, delivering dependable resolutions for dynamic multi-target tracking on land.

Multi-mode filter target tracking method for mobile robot using multi-agent reinforcement learning

Multi-mode filtering target tracking for mobile robot has important research significance for robot path planning, motion control and tracking robot targets. To address the problem that it is difficult for mobile robot to track targets in unknown environments, a multi-mode filtering target tracking method for mobile robot using multi-agent reinforcement learning is proposed. First, based on the extended kalman filter and combined with probability data association, the observation values in different motion modes are calculated and the system state is updated in real time, to achieve concurrent mapping, location and state estimation for each robot. Second, state communication among multi-mobile robot are established, and the multi-agent reinforcement learning model is built. Finally, based on the agent reinforcement learning model, based on the communication and communication among multi-tracking agents and the location of the target agent, the target tracking task is allocated to shorten the total path of the tracking agent, and the multi-target tracking of multi-mobile robot is completed by constantly learning and updating the strategy. The results show that the maximum tracking error of the target is 7 mm, and the calculation time of each step is only 51.1ms. The tracking success rate, observation rate and coverage rate are all high. It has important research value in robot location and navigation, service robot and other fields.

Towards object tracking for quadruped robots

With the development of quadruped robot technology, object tracking for quadruped robots has become an important research topic where violent camera shaking caused by the robot’s movement makes this task very challenging. In this letter, a quadruped robot object tracking dataset (QROD-111), including 111 video sequences, is first established, which was collected through our quadruped robot platform. A tracking algorithm based on Siamese network is then proposed where an alignment module is introduced to alleviate tracking difficulties caused by the quadruped robot movement. Moreover, a scale adaptation subnetwork is designed to alleviate the impact of the object scale variation during the whole tracking process. Experimental results demonstrate that our algorithm can achieve advanced performance for quadruped robot object tracking.

Robotic arm trajectory tracking method based on improved proximal policy optimization

To study the method of trajectory tracking for robotic arms, the traditional tracking method has low accuracy and cannot realize the complex tracking tasks. Compared with traditional methods, deep reinforcement learning is an effective scheme with the advantages of robustness and solving complex problems. This study aims to improve the tracking efficiency of robotic arms based on deep reinforcement learning. Thereby, we propose an approach to improve the proximal policy optimization (Improved-PPO) in this paper, which can be applied to multiple degrees of freedom robotic arms for trajectory tracking. In this study, proximal policy optimization (PPO) and model predictive control (MPC) are integrated to provide an effective algorithm for robotic arm applications. MPC is employed for trajectory prediction to design the controller. Further, the Improved-PPO algorithm is employed for trajectory tracking. The Improved-PPO algorithm is further compared with the asynchronous advantage actor-critic (A3C) and PPO algorithms. The simulation results show that the convergence speed of the Improved-PPO algorithm is increased by 84.3% and 15.4% compared with the A3C and PPO algorithms. This method provides a new research concept for robotic arm trajectory tracking.

Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors

The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.

Cascaded ESO based multi-task priority tracking and null-space compliance control for redundant robots

This paper investigates multi-task priority trajectory tracking and null-space compliance control of redundant robots without joint velocity and joint torque sensors. A novel multi-task priority controller is proposed to improve the dynamic performance and robustness of all level tasks. First, a cascaded extended state observer (ESO) is put forward to estimate joint velocity, lumped disturbances and the interference from high-priority tasks to low-priority tasks simultaneously. Then, a tracking controller for higher priority tasks and a null space compliance controller for the lowest priority task are designed. The stability of the closed loop system under the proposed cascaded ESO and task trajectory tracking controller is analyzed, as well as the passivity of the lowest priority task. The parameter tunning procedure of the cascaded ESO is given. Comparative simulation and experiment results verify the effectiveness of the proposed method.

Development of Rhombus Hanging Basket Walking Track Robot for Cantilever Casting Construction in Bridges

For enhancing the efficiency of cantilever casting construction in a bridge, a novel rhombus traveling track for a hanging basket was developed and optimized. The business-oriented software called MIDAS was applied to analyze the mechanical properties of the hanging basket with a full load and a control load. The strength and distortion of the walking mechanism are within the specified range when the maximum beam unit internal force is 149.69 MPa, which is less than the allowable stress of steel of 175 Mpa, and the anti-overturning safety factor of the hanging basket is 2.5, which meets the requirements. Through the comparative analysis of the key components and the finite element calculation, it was found that there is about a 30% redundancy in the structure performance. Therefore, further optimization of each structure was carried out, and the front elevation of the main truss of 20° was achieved. It obtained the best performance in all aspects.

Reinforcement Learning-Based Approach to Robot Path Tracking in Nonlinear Dynamic Environments

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system’s environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

Extended state observer based dynamic iterative learning for trajectory tracking control of a six-degrees-of-freedom manipulator

With the development of industrial automation comes an ever, broadening number of application scenarios for manipulators along with increasing demands for their precise control. However, manipulator trajectory tracking control schemes often exhibit problems such as those related to high levels of coupling, complex calculations, and in various difficulties in application for industrial environments. For the problems of low accuracy in control and poor robustness of multiple-jointed robotic trajectory tracking, iterative learning control (ILC) with model compensation (MC) based on extended state observer (ESO) has been proposed for the trajectory tracking control of six-degrees-of-freedom (six-DOF) manipulators. The scheme has excellent features to overcome uncertainties in repetitive tasks, including unknown bounded perturbations that are external to the model or dynamic perturbations that are internal to the model. The proposed control strategy combines ESO, iterative learning, and MC, for precise control of trajectory tracking. Here, ESO is used to estimate disturbances, iterative learning allows fast and accurate control in repeated tasks, and the model-compensated control algorithm alleviates the necessary for many inverse operations. The convergence of our proposed control scheme is proved through Lyapunov function and time-varying approximation theory. Simulation and experimental results verify the validity of the proposed scheme.

A nonsingular fast terminal sliding mode predictive controller for underwater soft crawling robots with parameter uncertainty

Unknown disturbances and material properties affect the trajectory tracking of underwater soft clawing robots. A nonsingular fast terminal sliding mode predictive controller is proposed to eliminate these effects. To characterize hysteresis and creep in dielectric elastomer materials, a dynamic model incorporating an improved dead-zone operator algorithm is constructed. Meanwhile, a nonsingular fast terminal sliding mode predictive controller that combines the Lie derivative function and sliding mode controller surface is developed to improve the accuracy and robustness of trajectory tracking. Additionally, the dynamic response and convergence speed of the proposed control system are improved by using an adaptive nonlinear disturbance observer to estimate the disturbance and uncertainty terms. Finally, the superiority and effectiveness of the proposed control method are verified by numerical simulations and experiments.

Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

AbstractTo promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, nonlinear model predictive control (MPC) is applied to realize trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time‐varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, the optimal solution of the linear MPCs is approximated by using the lattice PWA models. That is, for different sampling states, the optimal control inputs are obtained offline, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of the method in comparison with the state‐of‐the‐art methods, and the results show that the method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error.

Podążanie za zadaną trajektorią grupy robotów kołowych z użyciem wirtualnych połączeń sprężysto-tłumiących

The article presents the solution to the problem of trajectory tracking of a self-organized group of wheeled robots in the environment without obstacles. The group of robots is tracking a trajectory realized as following a reference point by the geometric center of the group, as well as simultaneously, reaching and maintaining a given distance between neighboring robots. The proposed method is based on virtual forces from virtual spring-damper connections between robots, which allows for the trajectory tracking of the previously self-organized group while maintaining its desired shape. The presented method of control is described in detail with the description of i-th robot dynamics and was tested numerically and experimentally. The paper presents the results of numerical tests and experimental research and ends with discussion and conclusions. The paper’s results could be expanded for applications related to robotic group trajectory tracking.

Coordination control of the automated fiber placement system using photogrammetry-based leader–follower approach

The use of multiple robots in an automated fiber placement system (AFPS) makes it possible to manufacture composite structures with complex shapes. The robots in such system usually adopt independent trajectory planning and control methods. This makes the AFPS lack coordination, and rely heavily on the high-precision calibration of the system when completing complex fiber placement tasks. In this article, a novel photogrammetry-based leader–follower (PBLF) approach is proposed for a particular AFPS which consists of a serial manipulator and a parallel robot to improve its accuracy and coordination. The approach is composed of an offline trajectory planning module, a leader module, and a follower module. The trajectory planning algorithm in the offline trajectory planning module solves the problem of robot workspace constraints and mandrel occlusion in the fiber placement tasks for complex structure. Besides, a novel photogrammetry-based pose tracking method is proposed to make the follower robot track its desired relative trajectory with respect to the moving reference frame and keep the coordination of the two robots. The theoretical stability of the proposed approach is proved. The results of experiments indicate that the proposed approach can reduce the average mean absolute errors of the relative pose in position and orientation direction by 62.15% and 46.42%, respectively. While the corresponding average root mean square errors are reduced by 61.20% and 45.26%, respectively. Therefore, compared with Non-Coordination approach, the PBLF approach can effectively improve the coordination of the two robots.

A Disturbance Observer-based Novel Fuzzy SMC Control Scheme for Path Tracking of AUV with Mismatched Disturbances

A new sliding mode control scheme is proposed for the path tracking problem of autonomous underwater vehicles (AUVs) with uncertainty and mismatch interference. Considering the complexity of parameter rectification, a fuzzy logic controller-based system rectification scheme is designed in this paper. In the control system, a disturbance observer (DOB) and a radial basis function neural network (RBFNN) are used to estimate the mismatch disturbance and uncertainty, respectively. In addition, this paper analyzes the conditions for the Liapunov stability of the designed controller and the results show the closed-loop stability of the control system. The control scheme can be applied to the path tracking of underwater robots with rapidly changing trajectories in different driving environments. Finally, a number of comparative experiments are carried out to demonstrate the good performance of the proposed controller.