## 1 Introduction  
The development of unmanned robots for both civilian and military use is ongoing. Present day examples include robot vacuum cleaners, robot lawn mowers, and unmanned aerial, ground, and underwater vehicles for surveillance, mapping, and object manipulation. Unmanned Ground Vehicles (UGVs) may be used in a variety of hazardous or impossible-to-accompulish tasks. They may also be utilized to simplify people's lives by saving them energy, time, and money. Often, the vehicle will have a collection of sensors to study its surroundings and either make autonomous judgments about its behavior or transmit the information to a human operator in a remote location who manages the vehicle through teleoperation. Various types of unmanned robots, such as autonomous vacuum cleaners, are designed to be used indoors and outdoors with the assistance of collision detection and mapping sensors. Detecting special objects and generating a proper path regarding the object's camera coordinates can be very effective for autonomous robots that are designed to collect special shapes or objects. Collecting tennis balls autonomously can be very helpful for players as it saves their time and energy during tennis training. Ball detection, path planning, motion control, and the collecting mechanism are critical tasks that should be done by the robot. Avoiding dynamic obstacles is a common algorithm in designing robot movement strategies [1; 2; 3; 4; 5]. Edge detection algorithms are very popular in detecting tennis balls and path planning based on detection results [6]. The main challenge in the application of edge detection algorithms is that they are effective when there are a small number of balls and many edge profiles in the environment. However, the detection error rate and the processing time increase as the number of balls increases. Tennis ball collection robots were developed to [7; 8] use the fixed lines on the court for their navigation algorithm. Recently, artificial intelligence and deep learning techniques have become popular in the recognition, classification, and path planning of robots [9; 10]. Vision-guided golf ball-collecting autonomous robots have been developed by [11; 12] using external cameras and sensors mounted on golf courts that help them detect the balls. The purpose of this study was to develop a tennis ball-collecting robot that mapped its environment in parallel by detecting tennis balls with a single camera and collecting them. The robot was also enabled to classify the priority of the balls based on their distance from the robot and then collect them based on this priority. A vision-aided and automated ball-retrieving robot was proposed [13] where object detection and path planning tasks were done by a computer fixed to the court and the mobile robot motion control was implemented by an onboard microcontroller. Communication between the computer and the mobile robot was supplied using a Wi-Fi network. Using an omnidirectional wheel, LiDAR, and RGB-D camera, [14] we developed an autonomous, wheeled, mobile robot for tennis and table tennis that collects balls. Using a vision system based on the YOLO algorithm, [15] we presented an object detection method for identifying static objects that could be obstacles in the path of a mobile robot. To identify the objects and their distances, a Microsoft Kinect sensor and an Nvidia Jetson TX2 GPU were used to improve the performance of the image processing algorithm. Fuzzy logic algorithms are used for various missions in robotics, such as object detection, classification, path planning, position, and velocity control, etc. [PERSON] et al. proposed a GA-based, fuzzy, adaptive nonlinear observer for the estimation of tennis ball trajectory in [16]. With the proposed method, they predicted the ball's trajectory with greater precision than with a model-based method. In [17], a non-mode-based controller based on fuzzy logic was proposed for the real-time control of wheeled mobile robots.