The Development of Mapping, Covering and Control Strategies for a Vacuum Cleaner Robot

Nội dung text: The Development of Mapping, Covering and Control Strategies for a Vacuum Cleaner Robot

1. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 The Development of Mapping, Covering and Control Strategies for a Vacuum Cleaner Robot Thi Thoa Mac*, Nguyen Thanh Hung Hanoi University of Science and Technology, Hanoi, Vietnam *Email: thoa.macthi@hust.edu.vn Abstract Modern households are becoming more and more convenient and intelligent by applying new technology to reduce the time spent on house chores. In this study, the authors proposed the mapping, covering strategies, and control algorithms for vacuum cleaner robot. The robot will automatically implement the cleaning task in a single pass. The sensor system includes infrared sensor, 9 Dof MPU 9250, Delta Lidar 2A, ultrasonic sensor to help robots navigate, build maps and detect obstacles. ROS system (Robot Operating System) is used to control and simulate vacuuming operation in real-world environments. The experiments are conducted in order to illustrate the superiority of the proposed approach. Keywords: covering strategy, localization, mapping, cleaner robot, PID controller, system identification. 1. Introduction1 This paper is organized as follows. In section 2, we explain the design of the robot. In addition, the In recent years, mobile robots have been system identification, control, covering strategies and developed in many active research areas such as mapping are introduced. In section 3, we give a brief precision agriculture, swarm robot [1], autonomous description of experiment results and discussions. navigation [2], cleaning, tidying up, or doing the Finally, conclusion is provided in section 4. laundry in the domestic environment [3]. Cleaning tasks, in particular, are the most frequent household 2. System Identification, Control, Covering chores in modern environments. In household life, strategies and Mapping with the development of intelligent technology, A description of the vacuum cleaner robot’s robotic vacuum cleaners and other smart appliances main characteristics, robot model, system are more and more popular [4]. A vacuum cleaner identification and controller design, sensory robot is an intelligent machine commonly used for equipment, system setup and localization are cleaning floors and carpets by suction [5-6]. presented in this section. In order to save time for people, the cleaning strategies of the robot are investigated in various ways. Floor cleaning robots are expected to archive a market value of 2.5 billion by 2020 [7-8]. The remarkable market players consist of iRobot Roomba, Xiaomi, robot iLife. Normally, the cleaning robots are operated with random movement. However, there is no guarantee that the floor is fully covered [9]. Zheng et al. in [10] investigates Mocap system for the multiple floor cleaning robot platforms. A development of a low-cost Arduino cleaning robot using mapping algorithm is proposed in [11]. However, the proposed approach is not efficient because they neglect the unknown environment. This work focuses on system identification, covering strategies, the controller and mapping for a low-cost vacuum cleaner robot. The proposed Fig. 1. The vacuum cleaner robot design. approach does not require information of the room 2.1 System Design map in advance, therefore it works in a real time effectively. The designed robot has a differential drive system with two independent wheels and a caster for stability as shown in Fig.1. The localization of the vacuum ISSN: 2734-9373 cleaner robot in the global coordinate Oxy is determined by the position of the mass center of Received: 11 January 2021; accepted: 21 February 2021 59
2. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 robot P(x, y); and the angle between the local frame cosθθ cos Pxmym and the global frame θ as depicted in Fig. 2. 22 vtx ( )  sinθθ sin vP vty ( ) =  (1) 22v θ t 11 − LL The robot is equipped with infrared/ultrasonic and lidar sensor. MPU sensor that determines the most accurate direction is used to guide the robot. Combined with lidar, infrared sensor is used to detect obstacles and evaluate different places. These sensors will send signals to the Raspberry Pi 3+ microcomputer for proper processing and navigation Fig. 2. The vacuum cleaner robot’s kinematic model. methods. During the operation, the Arduino The robot’s parameters include: microcontroller plays an intermediate role in the communication between the microcomputer with - vt : the linear velocity of the left wheel sensors and motors as shown in Fig. 3. - vp : the linear velocity of the right wheel 2.2 System Identification - v : the linear velocity of the robot In order to obtain the transfer function of the system, the characteristic of the open-loop system is - ωt : the angular velocity of the left wheel investigated. A plot of the step response is shown in - ωp : the angular velocity of the right wheel Fig. 4. - L : the distance between two wheels The time constant is chosen us: - R : the distance from the center of robot to the T = 0.25 (s) (2) center of instantaneous velocity The transfer functions is: - I : the center of the instantaneous velocity 130 (3) The kinematic model of the robot is: C(s) = 0.25 s +1 The PID controller parameters are determined by the root locus method as shown in Fig. 5 with Kp = 0.25, Ki = 0.5. Fig. 3. The robot control circuit diagram. 60
3. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 Fig. 4. The step response. Fig. 5. The root locus of the system. 2.3 Mapping 2.4 Localization Exploration for mapping in an unknown In order to develop a reasonable path planning, the environment is an important task of autonomous robot needs to locate its current position and direction navigation in general and for vacuum cleaner robot in in the working space as described in Fig. 7. In this particular. In this study, the method of detecting the study, the absolute positioning method based on border between the defined space and the Monte Carlo algorithm (Monte Carlo Localization) – undiscovered space is implemented to construct the MCL is used to find the robot position. map. The algorithm diagram is shown in Fig. 6. The algorithm uses a set of N random samples to When the robot moves to a border, it determines determine the exact position of the robot, which is a the features to create a new map by repeating the discrete approximation of the robot's position. above process. The defined space is continuously (4) expanded until it reaches the terminated condition. N= [ xy,,θ ] , p At this point, the map of the working environment is completely constructed. 61
4. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 Fig. 6. Exploration diagrams and map construction. Fig. 7. Localization process. where [x, y, θ] is the position and orientation in the robot can cover the required area with this trajectory. working space, p ≥ 0 is the discrete probability, with The time required to cover the entire area is reduced. N = The zig-zag covering strategy is a paradigm ∑pn 1. For more detail, please refer [12]. n=1 shift proposition. The robot starts from a corner of the map and travels zig-zag until the entire map is 2.5 Covering Strategies covered. If an obstacle is in the robot's trajectory, it In this subsection, we will discuss three different will move around the object as shown in Fig. 9. methods of covering a floor as shown in Fig. 8, 9, 10. The third solution is an inward spiral. With the Having their advantages and disadvantages, the starting position from the corner of the floor, the proposed solutions are particularly proper for robot moves toward the center as shown in Fig. 10. different situations. Unlike the zig-zag model, this method can be derived The robot moves from the starting position from any starting point. Similar to the zig-zag following an outward spiral as shown in Fig. 8a. method, when detecting an obstacle, the robot When detecting an obstacle, the robot avoids and switches the obstacle ring and continues to follow the follows the spiral trajectory as shown in Fig. 8b. The planned trajectory. 62
5. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 (a) (b) Fig. 8. The outward spiral covering strategies. (a) (b) Fig. 9. The zig-zag covering strategies. (a) (b) Fig. 10. The inward spiral covering strategies. 3. Results and Discussions the robot in the environment is shown in Fig. 11. The robot localization is illustrated in Fig. 12. After the In this study, the ROS and SLAM are robot executes in the working environment, the data implemented for the vacuum cleaning robot. The collected by its sensors matches the mapped data. dimensions of the robot are set as [[-0.20, -0.20], [-0.20, 0.20], [0.20, 0.20], [0.20, -0.20]]. The In the experiments, Fig.13, 14, 15 presents the dimensions of the room are 4x5m2. The localization results of the robot travels according to the square method is applied based on Monte Carlo algorithm. trajectory (2mx2m), zig-zag trajectory, and spiral This is the algorithm that has many advantages over trajectory, respectively. The total errors of position of previous algorithms due to a significant reduction in different trajectories are summaried in Table 1. computational burden. The approximated position of 63
6. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 Table 1. Error position of different trajectories. Trajectory Total Path Error Square 8 (m) ∆X = 0.092 (m) ∆Y = 0.135 (m) Zig-zag 10 (m) ∆X = 0.094 (m) ∆Y = 0.098 (m) Spiral 11 (m) ∆X = 0.120 (m) ∆Y = 0.121 (m) (a) Fig. 11. Approximated position of the robot in the environment. (b) (c) Fig. 13. (a) Square trajectory, (b) The real position of the robot in comparison with the set position, and (c) the real angle of rotation in comparison with the set Fig. 12. Robot localization using MCL. angle. 64
7. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 (a) (a) (b) (b) (c) Fig. 15. (a) Spiral trajectory, (b) The real position of the robot in comparison with the set position, and (c) (c) the real angle of rotation in comparison with the set Fig. 14. (a) Zig-zac trajectory, (b) The real position of angle. the robot in comparison with the set position, and (c) Fig. 16 presents the mapping results in a real the real angle of rotation in comparison with the set working environment. Fig. 17 and 18 show the angle. covering results by the combination of the algorithms described in the subsection 2.5. In the experiments, the proposed approach always generates a suitable trajectory depending on the start position in the working environment. The covering percentage is 86%. 65
8. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 Fig. 18. Robot covering results. Fig. 16. Robot mapping results. the virtual environment in ROS and the real-time experiment demonstrate the feasibility of the proposed strategy. The conducted results have a covering percentage of 86%. Acknowledgments This work is granted by the National Foundation for Science and Technology Development of Vietnam NAFOSTED, Vietnam under project number 107.01- 2018.331. References [1] TT Mac, C Copot, CY Lin, HH Hai, CM Ionescu, Towards The Development of a Smart Drone Police: Illustration in Traffic Speed Monitoring, Journal of Physics: Conference Series, 2020, 12-29 Fig. 17. Trajectory covered in real environment. [2] TT Mac, C Copot, C. M. Ionescu, Detection and 4. Conclusion Estimation of Moving obstacles for a UAV, IFAC- In this paper, we proposed an autonomous PapersOnLine, Volume 52, Issue 1, 2019, Pages 22-27. approach for a vacuum cleaner robot. The main achievements for this work are: (i) the mechanical [3] Nicholls, Y. Strengers, Robotic vacuum cleaners save design, identification system methodology and energy? Raising cleanliness conventions and energy controller development; (ii) the proposed localization demand in Australian households with smart home method based on MCL and mapping in the ROS; (iii) technologies, Energy Research & Social Science, Vol. the proposed covering strategy to maximum the 50 (2019) 73-81. covered floor area. The performed simulation using 66
9. JST: Smart Systems and Devices Volume 31, Issue 1, May 2021, 059-067 [4] T.B. Asafa, T.M. Afonja, E.A. Olaniyan, H.O. Alade, [9] J.Kim, A.K.Mishra, R.Limosani, M.Scafuro, N. Cauli, Development of a vacuum cleaner robot, Alexandria J. Victor, B. Mazzolai, F. Cavallo, Control strategies Engineering Journal, Vol. 57(4) (2018) 2911-2920. for cleaning robots in domestic applications: A comprehensive review, International Journal of Advanced Robotic Systems. July 2019. [5] Estela D. Vicente, Ana M. Vicente, Margarita Evtyugina, Ana I. Calvo, Fernanda Oduber, Carlos Blanco Alegre, Amaya Castro, Roberto Fraile, Teresa [10] K. Zheng, G. Chen, G. Cui, Y. Chen, F. Wu, X. Chen, Nunes, Franco Lucarelli, Giulia Calzolai, Silvia Nava, Performance metrics for coverage of cleaning robots Célia A. Alves, Impact of vacuum cleaning on indoor with mocap system Intelligent Robotics and air quality, Building and Environment, Vol.180 (2020) Applications, Springer International Publishing 107059 (2017), pp. 267-274, 10.1007/978-3-319-65298-6_25 [6] Mohamed Amine Yakoubi, Mohamed Tayeb Laskri, [11] R.J.Ong and K.N. F Ku Azir, Low Cost Autonomous The path planning of cleaner robot for coverage region Robot Cleaner using Mapping Algorithm based on using Genetic Algorithms, Journal of Innovation in Internet of Things (IoT), 2020 IOP Conf. Ser.: Mater. Digital Ecosystems, Vol. 39(1) (2016), 37-43. Sci. Eng. 767 012071 [7] M. A. Yakoubi, M. T. Laskri, The path planning of [12] F. Dellaert, D. Fox, W. Burgard and S. Thrun, Monte cleaner robot for coverage region using Genetic Carlo localization for mobile robots, Proceedings 999 Algorithms, Journal of Innovation in Digital IEEE International Conference on Robotics and Ecosystems, Vol.3(1) (2016) 37-43. Automation (Cat. No.99CH36288C), Detroit, MI, USA, (1999), vol.2, pp. 1322-1328. [8] V. Prabakaran, M. Elara, T. Pathmakumar, S. Nansai, Floor cleaning robot with reconfigurable mechanism, Automation in Construction, Vol. 91 (2018) 155-165. 67