Navigation Control System Research Articles

Research on Satellite Navigation Control of Six‐Crawler Machinery Based on Fuzzy PID Algorithm

ABSTRACTThe six‐crawler driving mechanism plays a crucial role in the operation of large machines such as bucket‐wheel excavators, dumping machines, and mobile crushing stations, as it serves functions like bearing, movement and steering. The driving characteristics of this mechanism directly influence the safety and efficiency of these machinery systems. To enhance the design methodology for multi‐crawler machinery, improve path controllability, and achieve adaptive driving, a satellite navigation control system for six‐crawler machinery was developed based on the principles of real‐time kinematic (RTK) satellite positioning. This system utilizes the distance deviation and heading angle deviation between the actual path and the predetermined path of the six‐crawler machinery as inputs to a fuzzy proportion integration differentiation (fuzzy PID) controller. This controller regulates the deviation angle of the steering crawler and the driving speeds of each track, thereby ensuring precise path tracking control. To evaluate the path tracking control performance under both straight and curved driving conditions, a virtual prototype model of the six‐crawler mechanical system was established, and co‐simulation analysis was conducted. In addition, an experimental platform for path tracking control of six‐crawler machinery was established to validate the efficacy of the satellite navigation system. The actual tracking data obtained from various driving conditions and initial deviations demonstrated that the RTK satellite navigation path tracking control system exhibited excellent control performance.

ADAS and automation-arelevant contribution to maintaining mobility of older drivers?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have ahigh level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be apriority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in acritical situation.

Development of a combined harvester navigation control system based on visual simultaneous localization and mapping-inertial guidance fusion

Recently, the existing unmanned systems of combine harvesters mostly adopts satellite navigation scheme, lacking real-time observation of harvesting adjustment. To improve the operational efficiency of combine harvester assisted navigation operation, this paper designs a combine harvester navigation control system based on vision simultaneous localization and mapping (SLAM)-inertial guidance fusion. The system acquires field image information and extracts the crop boundary line as the navigation datum by binocular camera. First, the system acquires field image information through binocular camera and extracts the crop boundary line as the navigation datum. Second, fusing camera and inertial guidance information to obtain the real-time relative position of a combine harvester. Third, constrained optimization of image and inertial guidance information is achieved through a sliding window optimization method based on tightly coupled nonlinear optimization. Finally, obtain the position of the combine harvester relative to the navigation datum line, and output a signal to the steering mechanism to realize the combine harvester in the field intelligent positioning navigation control. The system consists of binocular camera, inertial measurement unit, motorized steering wheel, monitor display, angle sensor and microcontroller. During field testing, the system underwent repetitive harvesting trials over a distance of 25 m.. The testing machine performs field operations at a speed of 0.9-1.5 m/s, with an average lateral deviation range of 2.21-8.62 cm, a standard deviation range of 0.13-4.21 cm and an average cutting rate range of 92.2%-96.0%, achieving the expected harvesting effect.

A novel method for measuring roll angle

The precise measurement of the spin speed of a high-speed autonomous unmanned aerial vehicle (HSA-UAV) is a key element in mastering flight stability, and the measurement of roll angle is the key to determining the accuracy of navigation control systems. We put forward a novel method for measuring roll angle. This method starts from the time–frequency domain analysis of the output signal of the geomagnetic sensor, extracts the time–frequency ridge of the time–frequency matrix (TFM) to obtain the spin speed of the HSA-UAV, and reconstructs the output signal of the geomagnetic sensor. It can calculate the roll angle when the calibration parameters of the geomagnetic sensor are unknown and have good engineering practical value. In addition, we also propose an improved nonlinear short-time Fourier transform with high-frequency resolution and a forward penalty dynamic path ridge-extraction method with frequency jump suppression to extract instantaneous frequency in the TFM.

Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Design and Experiment of an Agricultural Field Management Robot and Its Navigation Control System

The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations.

Feasibility study on Multiphysics H2-O2 combustion model for space debris removal system – NIRCSAT-X

Space debris is a growing problem for low earth orbit (LEO) and geosynchronous orbit (GEO). The risk of space debris currently affects human activities in Space and is controlled by the collision avoidance alert. However, the risk is growing, which increases future space mission costs to avoid or shield against space debris impact.The project has evolved over four years, culminating in Meng/BEng graduation projects. At the heart of our innovation is utilising the naturally high temperatures in the exosphere and stratosphere, which can soar to 1200 °C. This environment is ideal for initiating a chemical reaction within a pressurised chamber containing a mix of H2-O2 gases, generating heat sufficient to ablate common space debris materials such as titanium, aluminium, and composites. We have crafted an initial satellite design and performed Multiphysics simulations using COMSOL to validate our concept. The project now seeks investment to enhance four critical areas: the satellite's mechanical design to ensure safe operation within a debris field, the development of a dynamic control system for debris collection and satellite navigation, the management of H2 and O2 tank refilling, and the creation of a mechanism for the safe release of ablated materials back into Space.

Navigation control of an autonomous Ackerman robot in unknown environments by using a lidar-sensing-based fuzzy controller

In this paper, a real-time navigation control system based on lidar sensing is proposed for use in unknown environments. The proposed system comprises a behavioral controller for controlling an autonomous Ackerman robot for obstacle avoidance in the absence of global map information when moving toward a goal. The adopted obstacle avoidance method is selected by a wall-following fuzzy controller. The input parameter of this controller is the distance between the robot and the wall, which is determined by the lidar sensor, and the output parameter of the controller is the steering angle of the robot for it to reach the destination without collision. To prevent the robot from entering an endless loop, an endless loop escape mechanism is added to the proposed system. The simulation and experimental results of this study indicate that the proposed navigation control system can effectively assist an Ackerman robot to complete the navigation task successfully in unknown environments.

Fundamental Development of a Navigation Control System of a Wheelchair Robot with an Autonomous Mobile Robot

Fundamental Development of a Navigation Control System of a Wheelchair Robot with an Autonomous Mobile Robot

БЕРЕЖЛИВОЕ МЫШЛЕНИЕ И СИТУАЦИОННОЕ УПРАВЛЕНИЕ В ОЦЕНКЕ ПОТЕРЬ ПРОВОЗНОЙ СПОСОБНОСТИ АВТОМОБИЛЬНОГО ПАРКА

When planning daily shifts in a motor transport enterprise, the main task is to maximize the use of the vehicle fleet’s carrying capacity, estimated by the amount of cargo that can be transported per shift (day) in the absence of all types of unproductive losses. The solution to this problem can be found within the framework of the concept of lean thinking, the center of which is the identification and elimination of unproductive losses. To identify the factors that caused losses, causal research is used, an example of which, along with others, is the situational approach. The problematic situation is a cause-and-effect complex of factors, the elimination of which ensures an increase in the efficiency of cargo transportation. The situation model contains a quantitative assessment of the goal of eliminating the problem situation, technical and operational indicators characterizing certain types of losses and recommended measures to eliminate them. The purpose of the study is to develop tools for assessing losses in the carrying capacity of a vehicle fleet, based on situational modeling in the implementation of the principles of lean thinking in relation to the transportation of metallurgical cargo. The study used methods of situational analysis, mathematical modeling, and analysis of technical and operational indicators of the transport process. To study the state of the issue, an analysis of scientific literature was carried out. The main results of scientific novelty are: a situational model of vehicle fleet capacity losses; coefficients of loss of vehicle fleet carrying capacity when transporting metallurgical cargo. Calculation of vehicle fleet capacity loss coefficients must be made using the capabilities of intelligent navigation control systems. Further research is expected to be conducted in the direction of developing specific applications of the concept of lean thinking and situational approach to increasing the efficiency of cargo transportation, as well as in the direction of developing digital systems for managing road transport.

Deep learning-based visual navigation control method for autonomous trajectory of UAVs

Abstract In this paper, a UAV intelligent visual navigation system is designed based on deep learning. To convert the pixel gray values, a Gaussian smoothing function is employed, which ensures that the main features of the visual image are preserved. A convolutional neural network is employed to mark the target with a frame using image pixels and obtain the coordinate position of the center point. Finally, the initial particles generated near the beacon are analyzed by particle filtering with color histograms, which are used to predict the position of the UAV at each autonomous trajectory point location. The control method proposed in this paper can keep the UAV attitude angle control error within 15%, and the minimum velocity error is 0.07%, as shown in the results. A deep learning-based visual navigation control system can guarantee that the UAV can accurately recognize the target in every autonomous trajectory.

In situ skid estimation for mobile robots in outdoor environments

AbstractSkidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.

Sailboat navigation control system based on spiking neural networks

In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.

基于PID-模糊控制的单目视觉农田机器人视觉导航系统设计

This study proposes a monocular vision navigation control system based on PID-fuzzy control, which travels along the edge of the path. It collects path image information through monocular vision, identifies the path edge through image processing to determine the preview point, and uses a combination of PID and fuzzy control to design a controller to track the preview point for path navigation. Firstly, coordinate calibration and conversion were performed on the monocular camera, achieving coordinate conversion from the image coordinate system to the world coordinate system. The accuracy of the calibration results was verified through experiments. According to the navigation strategy of driving along the edge of the path, the world coordinate equation of the path edge is obtained through image processing technology, and the preview point tracked by the navigation system is determined. The navigation parameters are determined based on the position of the preview point. The PID fuzzy controller system designed in this study can switch different control methods based on the position of the preview point. Finally, an experimental verification was conducted on the monocular visual navigation system of the control system. The verification results showed that the average error of the navigation control system in tracking the path when driving in a straight line was 0.039 m, the average error when turning left was 0.079 m, and the average error when turning right was 0.121 m. The error range can meet the basic requirements of agricultural robot farmland operations. Research has shown that the navigation strategy based on PID-fuzzy joint controller to track the preview point along the path edge has a good effect on the visual navigation control system of agricultural robots. This study provides important reference value for the research and development of monocular visual navigation systems of agricultural robots.

Designing and Analysis of an Architecture of Motion Control for a Mobile Robot Using Simulation

In recent decades, the control of motion and navigation of mobile robots has received the eminent attention of researchers. Motion control is an important technique that allows a mobile robot (MR) to move adaptively and precisely. The main aim of this research work is to design and analyze the architecture of the navigation and motion control systems for a mobile robot by using Simulink modeling. An encoder sensor is used to analyze the motion control. Experimental and simulation techniques are used to prove the application of the encoder sensor and dead reckoning approach to control MR motion. The modeling of the Simulink model for a motion control system for an MR through MATLAB 2022a software has been presented in this article. The main objective of this research work is to control the motion of the robot in forward and backward directions. The presented model in this paper has been verified through real-time simulation, and it shows the performance of the control system in the predefined optimal time range standard. Finally, this article provides a comprehensive survey of future research trends for the control system of MR.

Asynchronous Motor Imagery BCI and LiDAR-Based Shared Control System for Intuitive Wheelchair Navigation

Mapping drivers’ thoughts directly to mobility system control would make driving more intuitive as if the mobility system is an extension of their own body. Such a system would allow patients with motor disabilities to drive, as it would not require any physical movement. In this paper, we therefore propose a brain-controlled mobility system that analyzes real-time neural signals elicited from motor imagery, an imagination of different body movements. As such asynchronous brain-computer interfaces (BCIs) are prone to error, our system contains shared control capabilities that take into consideration continuously updated information of the surrounding environment along with electroencephalogram (EEG) signals to improve navigating performance without precise and accurate control from the driver. With our shared control method that uses a wheelchair with light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensors, we held a comparative study in which participants drove our wheelchair with and without our shared control approach using either our brain-controlled system or a keyboard in a physical environment. The experimental results show that among the five participants, the three participants that failed the driving task with the asynchronous BCI-based system could also successfully complete it using our shared control approach. Furthermore, our approach narrows the gap between driving with neural signals and driving with a widely used interface in terms of both elapsed time and safety. These results show not only the potential of brain signals for driving, but also the applicability of BCIs to real-life situations.

Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network

So as to study the influence of speed factors on the stability of tractor automatic navigation system, combined with neural network control theory, the author proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using back propagation neural network to establish two-wheel tractor-path dynamics model and straight-line path tracking deviation model, the overall system simulation was carried out by using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed: when the tractor was tracked with the automatic control of linear path under the condition of the variable speed, the maximum deviation of the lateral position deviation was 12.7cm, and the average absolute deviation was kept within 4.88cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the condition of constant speed and variable speed, using the joint sliding mode control method designed by the author, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.

Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields

Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.

ANALYSIS OF AUTOMATIC CONTROL SYSTEMS OF A MULTIPURPOSE AUTONOMOUS UNMANNED UNDERWATER VEHICLE WITH COMPLEX MOTION DYNAMICS

The overview article is devoted to the analysis of control systems of autonomous unmanned underwater vehicles, which belong to robotic underwater complexes and are used to provide search and deep-sea emergency and rescue operations, survey of various underwater objects (main pipelines, bottom structures, ship hulls, etc.), conducting scientific oceanographic surveys research, monitoring of the marine environment. The work examines scientific literary sources, which provide the results of development and research of orientation, navigation and motion control systems of multi-purpose autonomous unmanned underwater vehicles with complex movement dynamics, in particular when performing missions in a limited space with movement along complex trajectories. Special attention is focused on such modes of movement of devices as trajectory tracking, path-following and way point tracking. An analysis of the means of navigation and control, which can be used to ensure different modes of movement, was carried out, as well as the directions of development of both the unmanned underwater vehicles themselves and their control systems were considered.
 In order to substantiate the development of a control system for a multi-purpose maneuverable mini-class autonomous vehicle, the main requirements are formulated. It is proposed to conduct a synthesis and research of the control system based on the simulation of the dynamics of the movement of the mini-vehicle for different modes of movement in terms of parameters and trajectories, to develop algorithms for the adaptive control of the vehicle, to improve the system orientation and navigation, built on the basis of MEMS sensors. An information model of the development process is proposed and substantiated, which specifies the sequence of determining important parameters and characteristics of the vehicle and performing important stages of synthesis and analysis of the control system.

Quadcopter Navigation System With Waypoint Method Through Flight Controller at Ground Station

In a quadcopter, a navigation system is needed to monitor the position and control the movement of the quadcopter. In recent years, a navigation system based on longitude and latitude coordinates has been developed, namely a waypoint system. This flight control system works to maintain the actual position and direction of the quadcopter to its destination. Thus, the authors designed a navigation control system using the waypoint method on a quadcopter which can walk autonomously from one coordinate point to another coordinate point or destination. This navigation system works using GPS (Global Positioning System), and GY-81 as a magnetometer sensor. The application of the waypoint method can help the robot to determine the direction toward the destination and how far the target is from the robot's current position. This is evidenced in the movement of the robot with an accuracy value of up to 99.48% and an average time to reach the target in about 50-65 seconds. The application of the PID method gives better heading results compared to those without using the PID method. This is evidenced by the stable robot movement and fairly accurate compass sensor readings. Based on the test results using the PID method on the seacoast, the error = 2.15% and the accuracy = 97.85%, while in the test without the PID method on the land surface, the error = 3.33% and the accuracy = 96.6%. For testing using the PID method in paddy fields, an error result = 1.85% and an accuracy result = of 98.1%, while testing without the PID method on the surface of the water obtained an error result = 2.75% and an accuracy result = 97.25%.