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Navigation Control of Ackermann Steering Robot Using Fuzzy Logic Controller
... Therefore, some scholars use fuzzy controllers and allow experts to define fuzzy rules for navigation with obstacle avoidance. (24) Lin et al. (25) proposed a wall-following strategy based on a fuzzy controller and defined fuzzy rules. They used the center of gravity method to resolve fuzzification to obtain the angle of navigation obstacle avoidance. ...
... We demonstrated the proposed navigation algorithm in three virtual scenarios. We also compared the proposed method with a reinforcement learning controller (23) and a fuzzy controller (25) to verify its performance. Three different virtual scenarios, a maze map, an irregular obstacle map, and a sharp-corner map, were constructed for simulation and validation, as shown in Fig. 9. ...
... To evaluate the obstacle avoidance and navigation capabilities of the proposed Ackermann unmanned vehicle, and verify the stability of the proposed method, we performed experiments in two different outdoor wooded environments. (23) 263.5 24.7 Fuzzy controller (25) 579.2 46.6 Proposed method B 289.5 27.3 Reinforcement learning controller (23) 271.7 25.6 Fuzzy controller (25) 245.6 22.6 Proposed method C 312.2 29.1 Reinforcement learning controller (23) 332.9 31.3 Fuzzy controller (25) 370.4 34.2 ...
... The Ackermann steering structure solves the issue of different steering angles caused by varying radii of the left and right wheels. According to Ackermann's steering geometry [4], by adjusting the crank of the four-link structure, the robot can increase the inner wheel's steering angle by 2-4° more than the outer wheel when turning along a curve. This adjustment helps position the robot's steering center, allowing smooth turns by aligning the four-wheel paths with the rear axle's extension line. ...
... Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs) and Transformer-based architectures, have significantly improved weed detection accuracy. Most modern approaches rely on pre-trained deep learning models (e.g., ResNet, YOLO, EfficientNet) trained on largescale agricultural datasets for real-time weed identification (Lin et al., 2023) [4] . However, deep learning models require high computational power and extensive datasets, which may limit their usability in field-deployable robots with constrained hardware. ...
A robot designed to identify and remove weeds from crops is known as a weed control robot. Weeds compete with primary crops for moisture, hinder their growth, and may harm both human and animal health, leading to reduced crop yields. Traditionally, herbicides and other chemicals have been used to eliminate weeds, but these methods can damage crops and pollute the environment. In this work, we propose a new semantic weed detection method based on the PC/BC-DIM network, which demonstrates superior performance and classification accuracy compared to existing approaches. We developed an autonomous weed control robot incorporating Ackermann Architecture and a delta robot. The delta robot is equipped with a camera at its base to detect weeds in real-time. First, the robot captures images using the camera, and through image processing techniques, it
differentiates weeds from crops. Detected weeds are then eliminated using an electrical discharge method, where electrodes attached to the robot’s end effector burn the targeted weeds. Additionally, we developed a path-planning and obstacle-avoidance system to help the mobile robot navigate the field. This system uses stereo vision to capture stereo images of the environment and calculate their disparity. By extracting depth information, the robot can detect obstacles, avoid them, and follow the shortest path using the A* algorithm. The results from this work are simulation-based, demonstrating effective weed detection in field images and efficient robot navigation using stereo images. The system achieved an overall accuracy of 81.25%. Although the system performs moderately well, the relatively high False Positive Rate and Root Mean Square (RMS) Error indicate the need for further improvements to reduce errors and false positives. Future work will focus on enhancing weed removal and implementing the simulated results on hardware.
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.
Because traditional methods are difficult to solve the problems related to the path planning of logistics robots, this study proposes a method of using computer multimedia 3D reconstruction technology to realize the path planning of warehouse logistics robots. Without fully considering the accurate movement path between points, according to the warehouse logistics robot, it is judged whether the starting point is at the exit. The planning problem of the movement path is converted into a TSP problem and a TS-TSP problem. Finally, the analysis of experimental results shows that the method proposed in this study converges faster than traditional algorithms and can quickly obtain the global optimal solution. At the same time, the warehousing logistics robot requires less path planning time and has strong practical application.
Diabetes is an ailment in which glucose level increase in at high rates in blood due to body’s inability to metabolize it. This happens when body does not produce sufficient amount of insulin or it does not respond to it properly. Critical and long-term health issues arise if diabetes is not handled or properly treated which includes: heart problems, disorders of the lungs, skin and liver complications, nerve damage, etc. With increasing number of diabetic patients, its early detection becomes essential. In this paper, our major focus areas are data mining and fuzzy logic techniques used in diabetes diagnosis. Data mining is used for locating patterns in huge datasets using a composition of different methods of machine learning, database manipulations and statistics. Data mining offers a lot of methods to inspect large data considering the expected outcome to find the hidden knowledge. Fuzzy logic is similar to human reasoning system and hence it can handle the uncertainties found in the data of medical diagnosis. These systems are called expert systems. The fuzzy expert systems (FES) analyze the knowledge from the available data which might be vague and suggests linguistic concept with huge approximation as its core to medical texts. In this paper, the methodology section delivers the pipeline of various tasks such as selecting the dataset, preprocessing the data by applying numerous methods such as standardization, normalization etc. After that, feature extraction technique is implemented on the dataset for improving the accuracy and finally dataset worked on data mining and fuzzy logic various classification algorithms. While analyzing different data mining methods, the accuracy computed through random forest classifiers as high as 99.7% and in case of numerous fuzzy logic approaches, high precision and low complexity was found to contribute a fairly high accuracy of 96 %.
In this paper we address the problem of online tra-jectory optimization and cooperative collision avoidance when multiple mobile service robots are operating in close proximity to each other. Using cooperative trajectory optimization to obtain smooth transitions in multi-agent path crossing scenarios applies to the demand for more flexibility and eciency in industrial autonomous guided vehicle (AGV) systems. We introduce a general approach for online trajectory optimization in dynamic environments. It involves an elastic-band based method for time-dependent obstacle avoidance combined with a model predictive trajectory planner that takes into account the robot's kinematic and kinodynamic constraints. We augment that planning approach to be able to cope with shared trajectories of other agents and perform an potential field based cooperative trajectory optimization. Performance and practical feasibility of the proposed approach are demonstrated in simulation as well as in real world experiments carried out on a representative set of path crossing scenarios with two industrial mobile service robots.
This paper describes the design of a fuzzy logic based intelligent controller for wall following behavior of a differentially steered mobile robot. The distance of robot from the wall and its orientation with respect to the wall is read with the help of three ultrasonic sensors mounted on left side of robot. The measured sensor values are fed to fuzzy logic controller that generates motor commands for the robot to follow wall on its left side at a set distance. The designed fuzzy logic controller is implemented using a low cost single chip AT89C52 microcontroller and its performance is evaluated for different wall trajectories. Experimental results are presented to show the validity of designed controller for wall following behavior of a mobile robot.
This paper describes four-wheeled robotic chassis of the autonomous mobile robot Bender II utilizing Ackermann steering and independent rear drive units. The robot is designed for the use in outdoor environment. Independent rear drive gives better traction in uneven terrain with the cost of doubling the motion hardware (motors, gears and controllers). Hardware and software implementation details as well as practical experiences of this approach deployment are presented.
We have been developing MKR (Muratec Keio Robot), an autonomous omni-directional mobile transfer robot system for hospital applications. This robot has a wagon truck to transfer luggage, important specimens, and other materials. This study proposes an obstacle collision avoidance technique for the wagon truck pulling robot which uses an omni-directional wheel system as a safe movement technology. Moreover, this paper proposes a method to reach the goal along a global path computed by path planning without colliding with static and dynamic obstacles. The method is based on virtual potential fields. Several modules with different prediction times are processed in parallel to change the robot response according to its relative velocity and position with respect to the obstacles. The virtual force calculated from each potential field is used to generate the velocity command. Some experiments were carried out to verify the performance of the proposed method. From the experimental results in a hospital it was confirmed that the robot can move along its global path, and reach the goal without colliding with static and moving obstacles.
The path planning of mobile robots is an optimization problem that is difficult to solve directly, owing to its high dimensional and nonlinear characteristics. This study proposes a "global-local" Coupling Two-Stage Path Planning (CTSP) method. First, the globally optimal solution in the configuration space is given by the global planner. Then, in the local planning stage, the optimal solution of the local environment is constantly searched, which is guided by the prior information of the globally optimal solution. The strategy used in the global planning stage is the iterative optimization method based on an initial solution. The local planning stage adopts the sampling-evaluation strategy, which involves sampling the candidate paths, and then, using the evaluation function to perform path selection. The proposed method has two innovations. 1) A novel global iterative optimization method is proposed, and 2) a new cost function for evaluating the sampled paths is constructed, which improves the coupling of the global and local paths. We implement and test this method in a simulation environment, where the experimental results show that the proposed method is competitive.
This paper describes the design of a fuzzy logic controller for wall following-navigation of a differentially steered mobile robot. To maintain the distance of the mobile robot from the wall is a basic behavior that mobile robot should have. This paper proposes embedded fuzzy logic controller in order to make the mobile robot can running with smoothly and stable for wall following case. The designed fuzzy controller uses MATLAB® and it was simulated using Microsoft Visual C++. To verify our approach, a differentially steered mobile robot is implemented with ArduinoTM Nano. The experiment of simulation and test results methodology were presented to show the validity of the proposed.
The paper describes a scheme in which a fuzzy algorithm is used to control plant, in this case, a laboratory-built steam engine. The algorithm is implemented as an interpreter of a set of rules expressed as fuzzy conditional statements. This implementation on a digital computer is used online, to control the plant. The merit of such a controller is discussed in the light of the results obtained.
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
In this paper, a knowledge based genetic algorithm (GA) for path planning of a mobile robot is proposed, which uses problem-specific genetic algorithms for robot path planning instead of the standard GAs. The proposed knowledge based genetic algorithm incorporates the domain knowledge into its specialized operators, where some also combine a local search technique. The proposed genetic algorithm also features a unique and simple path representation and a simple but effective evaluation method. The knowledge based genetic algorithm is capable of finding an optimal or near-optimal robot path in both complex static and dynamic environments. The effectiveness and efficiency of the proposed genetic algorithm is demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed GA for solving robot path planning problem is demonstrated by a comparison study.
This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the "artificial potential field" concept. In this approach, collision avoidance, traditionally considered a high level planning problem, can be effectively distributed between different levels of control, allowing real-time robot operations in a complex environment. We have applied this obstacle avoidance scheme to robot arm using a new approach to the general problem of real-time manipulator control. We reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation. This method has been implemented in the COSMOS system for a PUMA 560 robot. Using visual sensing, real-time collision avoidance demonstrations on moving obstacles have been performed.
Many kinds of omnidirectional vehicles driven by DC motors have
been proposed and developed for application in robotics systems, but
they have various problems like stability or operability in practical
use, and have to be controlled by feedback system. This paper describes
the improved moving properties of an omnidirectional vehicle driven by a
stepping motor. An omnidirectional vehicle, which can move in all
directions without changing the body direction of the vehicle, has been
developed. This omnidirectional vehicle is used as the base of a
calligraphy robot which is able to write Chinese characters using a
brush. The movement and the control of this vehicle are evaluated, and
the fields where this vehicle can be used are examined. Experimental
results show that the proposed device is useful in achieving the desired
performance
This paper provides a short review of the neural network approach
to system control with reference to robotic systems. Starting with an
exposition of the main neurocontrol architectures, the paper overviews
the literature on the application of neural networks to robot
kinematics, dynamics, path planning and motion control, including some
work on neurofuzzy control. To appreciate better robot neurocontrol, an
unsupervised robot neurocontroller is presented in some detail. The
paper includes a discussion of the criticism made to the neural control
paradigms, and an outline of some interesting areas for further research
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
The structure of the kinematic and dynamic models of wheeled
mobile robots is analyzed. It is shown that, for a large class of
possible configurations, they can be classified into five types,
characterized by generic structures of the model equations. For each
type of model the following questions are addressed: (ir)reducibility
and (non)holonomy, mobility and controllability, configuration of the
motorization, and feedback equivalence