# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
###### Abstract  
This paper describes the design and development of a cleaning robot, using adaptive manufacturing technology and its use with a control algorithm for which there is a stability proof. The authors' goal was to fill the gap between theory and practical implementation based on available low-cost components. Adaptive manufacturing was chosen to cut down the cost of manufacturing the robot. Practical verification of the effectiveness of the control algorithm was achieved with the experiments. The robot comprises mainly three assemblies, a four-wheel-drive platform, a four-degrees-of-freedom robotic arm, and a vacuum system. The inlet pipe of the vacuum system was attached to the end effector of the robotic arm, which makes the robot more flexible to clean uneven areas, such as skirting on floors. The robot was equipped with a LIDAR sensor and web camera, giving the opportunity to develop more complex methods. A low-level proportional-integral-derivative (PID) speed controller was implemented, and a high-level controller that uses artificial potential functions to generate repulsive components, which avoids collision with obstacles. Robot operating system (ROS) was installed in the robot's on-board system. With the help of the ROS node, the high-level controller generates control signals for the low-level controller. cleaning robot; ROS; 3D printing; four-wheel drive; robotic arm; vacuum system; LIDAR sensor; low-level PID controller; high-level obstacle avoidance controller; artificial potential function +
Footnote †: 2021: 11, 8076. [[https://doi.org/10.3390/app11178076](https://doi.org/10.3390/app11178076)]([https://doi.org/10.3390/app11178076](https://doi.org/10.3390/app11178076))  
Footnote †: 2021: 11, 8076. [[https://doi.org/10.3390/app1178076](https://doi.org/10.3390/app1178076)]([https://doi.org/10.3390/app1178076](https://doi.org/10.3390/app1178076))  
vacuum system was attached to the end effector of the robotic arm. The robot is equipped with a LIDAR sensor and web camera, giving the opportunity to develop more complex methods. The touch screen on the robot helps to displays the information from the robot. A low-level proportional-integral-derivative (PID) speed controller is used to achieve the desired speeds. A high-level controller [8] helps the robot to follow a virtual robot path with avoiding obstacles. Creating a virtual model of the robot opens another field where we can verify the algorithm and potential problems in the design of the robot [9]. The FPGA-based controller also shows high potentials for the robotic arm [10] and power-saving architecture for the motions [11]. Artificial potential fields were introduced by [PERSON] [12] in 1986. In this control algorithm, attraction (to the goal) and repulsion (from the obstacle) are negated gradients of the artificial potential functions (APF). Many articles have been published using the navigation function (NF) [12; 13; 14; 15; 16; 17]. The control of multiple mobile robots based on the kinematic model [18; 19] and dynamic model [20; 21] have also been published. A sliding mode control strategy with an artificial potential field was shown in [22]. An obstacle potential field function considers the robot's size and obstacle size and accordingly changes the weight of the obstacle potential field function, which adaptively makes the robot escape from local minima [23]. Authors of [24] addressed the problem of tracking the control of multiple differentially driven mobile platforms (leader-follower approach is used). The algorithm is distinguished by its simplicity and ease of implementation. The components of the control have an obvious physical interpretation, which facilitates the tuning based on observations of the robot's behavior. The linear velocity control depends on the linear velocity of the reference robot (virtual leader) and the position error along the axis perpendicular to the axle of the robot wheels.