Robot Path Tracking Research Articles

Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain Improved Genetic Algorithm

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

Robot Path Planning and Tracking with the Flower Pollination SearchOptimization Algorithm

Robot path planning and tracking involve two critical aspects of autonomous navigation systems: determining an optimal route for the robot to follow and ensuring that it accurately adheres to this path in real-time. Path planning focuses on generating a feasible and efficient trajectory from a starting point to a destination while considering obstacles, dynamic environments, and constraints. This paper investigates the effectiveness of the Flower Pollination Search Optimization (FPSO) algorithm for robot path planning and compares its performance with traditional algorithms such as A* Algorithm, Dijkstra, and Rapidlyexploring Random Tree (RRT). The FPSO algorithm was evaluated across three distinct scenarios, demonstrating superior performance in terms of path length, computation time, and path smoothness. In Scenario 1, FPSO achieved an optimized path length of 10.5 meters with a computation time of 3.2 seconds, a path smoothness score of 8.9, and a path efficiency of 95%. In Scenario 2, FPSO resulted in a path length of 12.3 meters and a computation time of 3.5 seconds, with a smoothness score of 8.7 and an efficiency of 93%. Scenario 3 showed FPSO's best performance, with a path length of 9.8 meters, computation time of 3.0 seconds, a smoothness score of 9.0, and a path efficiency of 96%. Comparatively, the A* Algorithm, Dijkstra, and RRT exhibited longer path lengths and higher computation times, with lower smoothness scores and efficiencies.

MOBILE ROBOT TRACKING SYSTEM BASED ON MACHINE VISION AND LASER RADAR

The proposed solution addresses the issue of insufficient real-time performance and accuracy in mobile robot path tracking by introducing a system that combines machine vision and laser radar. In this study, the Broadcom BCM2711 microcontroller chip is connected to the RS232 communication interface for transmitting information to the ARM embedded processor. Users can access position distance, direction, and other robot-related data through the man-machine interface's LCD display in a Windows operating system environment. By initiating an adaptive position tracking algorithm program identified by the robot within the position tracking unit, mobile position tracking of the robot is achieved. Experimental results demonstrate significant improvements in both real-time performance and accuracy of this mobile robot tracking system.

Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints.

The essence of biomimetics in human-computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR's kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms.

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.

Fast terminal sliding mode control of agricultural robots with permanent magnet synchronous motor servo systems based on an extended state observer for path tracking

In response to the challenges in mobile robot path tracking using model predictive control, where the predictive model weakens the controller’s ability to respond to sudden changes in the reference path curvature and heading, this paper proposes a composite control strategy suitable for agricultural robots. The strategy combines the maximum torque per ampere control and an Extended State Observer (ESO). The paper initially establishes a mathematical model for a Permanent Magnet Synchronous Motor (PMSM) considering aggregated disturbances. It designs a position tracking controller based on a non-singular terminal sliding mode and convergence law. This controller, employing a non-cascaded structure, replaces traditional position and velocity loop controllers and is proven to be stable with finite-time convergence through Lyapunov’s theorem. To enhance the system’s disturbance rejection capabilities further, the paper introduces an ESO to estimate system disturbances and applies it for feedforward compensation. The paper concludes by providing stability proof for the overall PMSM servo system in agricultural robots. Finally, the paper conducts simulations and experimental verifications based on the designed controller, demonstrating that the controller exhibits excellent path tracking performance, fast convergence, and robustness against external disturbances.

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.

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.

Regulated pure pursuit for robot path tracking

Regulated pure pursuit for robot path tracking

Parking Robot Path-Tracking System Based on Discrete PID Algorithm

To improve the space utilization rates of intelligent stereo garages, their key execution equipment—parking robots—must be highly flexible; therefore, the chassis of parking robots use steering wheel systems. However, this design increases the complexity of robot motion control and path-tracking systems. Because of the large sizes of parking robots, large position and angle deviations can lead to derailment events, posing significant risk to users. Thus, in this study, based on the characteristics required for parking robots, a diagonally arranged parking robot with double steering wheels was developed and its kinematic model was established using the velocity–geometry method. For the positioning of the robot in a garage, a fusion positioning algorithm based on quick response (QR) codes and track calculation was used, considering the requirements of real-time response and accuracy. A discrete proportional-integral-derivative (PID) path-tracking algorithm for the synchronous compensation of the position and angle deviations was also devised. Using the Visual C++ platform, a path-tracking system that uses an industrial computer and motion control card was developed. Experiments on the path-tracking capability of the parking robot showed that, using the discrete PID path-tracking algorithm, it can track a planned path well. On Z- and U-type paths, its maximum tracking deviations were reduced by 84.8% and 64.0%, compared with the unused path-tracking system, reaching 10.4 mm and 10.9 mm, respectively. Thus, the tracking accuracy was significantly improved, proving that the developed robot well satisfies the path-tracking requirements of parking robots.

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Robustness Analysis of a Fixed-Time Convergent GNN for Online Solving Sylvester Equation and Its Application to Robot Path Tracking

Robustness Analysis of a Fixed-Time Convergent GNN for Online Solving Sylvester Equation and Its Application to Robot Path Tracking

Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning

Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

Improved A* algorithm and model predictive control- based path planning and tracking framework for hexapod robots

PurposePath planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.Design/methodology/approachFirst, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.FindingsThe simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.Originality/valueThe work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

Cutting Forces Impact on the Spindle Path during Robotic Milling

Offline programming is a critical step in the implementation of various robotic tasks such as pick-and-place, welding, cutting, and milling. This paper describes a simulation study that analyses the accuracy of the robot's path tracking, during tasks that require the robot tool to interact with the environment, while considering the current operating conditions. To accurately determine the actual position of the Tool Center Point (TCP) and the associated orientation of the end effector, the study will first establish a robot model that takes into account the elasto-static behavior during the milling process that generates significant contact forces on the end effector. Then, an offline simulation tool is developed within the SolidWorks® CAD environment. The analysis of simulation results from multiple scenarios revealed that the tool/material contact forces were the main source of the robot's deviation from its nominal trajectories. Moreover, the range of positioning errors varies according to the architecture of the robot and the workpiece emplacement. Depending on the working conditions, the tool deflection ranges from 0.1 mm to 0.75 mm in the or cutting directions and increases as one moves away from the reference frame, while the Cartesian orientation deviation is negligible (less than 1°).

OpenStreetMap-Based Autonomous Navigation for the Four Wheel-Legged Robot Via 3D-Lidar and CCD Camera

OpenStreetMap (OSM) is widely used in outdoor navigation research recently, which is publicly available and can provide a wide range of road information for outdoor robot navigation. In this article, aiming at the problem that the map error of OSM will cause the global path to be inconsistent with the real environment, we propose an OSM-based robot navigation method that combines road network information and local perception information. As a global map, OSM provides road network information to obtain the global path by the Dijkstra algorithm. Multisensor (including 3D-LiDAR and Charge-coupled Device (CCD) camera) information fusion offers local information to detect local road information and obstacles for local path planning. We filter local road information and then extract useful road features to optimize the local path. Finally, this local path is used for robot path tracking to complete navigation tasks. The experimental results show that the average error between the trajectory of the robot and the road center is 0.18 m. This reveals that our method has high navigation accuracy and strong robustness in the real complex environment.

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.