Development of a Smart Lawnmower with a Mapping System Utilizing GPS and LiDAR Technology

This paper reports on the design, implementation, and field evaluation of an autonomous lawnmower that combines GPS, IMU, and LiDAR sensing within a ROS2-based control architecture to support real-time mapping, path planning, and obstacle avoidance. The hardware platform and control architecture are outlined, including a PID motor controller and an Extended Kalman Filter used for multi-sensor fusion. Two navigation configurations; Nav2 paired with the SLAM Toolbox and Nav2 paired with GPS are evaluated and compared in terms of lawn-mowing accuracy, obstacle-avoidance performance, and the ability to pass through narrow gaps. Over six outdoor trials per configuration, the SLAM Toolbox attained a mean navigation accuracy of 88.6% (SD = 7.9%), significantly higher than the 62.1% (SD = 11.1%) mean accuracy achieved by the GPS-based configuration (Welch's t-test, t = 4.76, p = 0.001); nevertheless, because GPS-based navigation does not depend on fixed structural landmarks, it proved more robust for open-field operation. The SLAM Toolbox also recorded a perfect 100% (6/6) obstacle-avoidance success rate against 80% (8/10) for GPS, and was able to pass through gaps as narrow as 40 cm. Environmental testing further showed that LiDAR mapping quality was affected by ambient light, with noise levels lower during night-time trials. Together, these results point to a trade-off between the mapping precision offered by SLAM and the landmark-independence offered by GPS, and they motivate future work on RTK-GPS integration, full-coverage path planning, and camera-based sensor fusion to strengthen robustness for open-field lawn maintenance.

autonomous lawnmower, GPS, LiDAR

1. D. D., K., Kumar D, K., T., S., & B., S. B. (2022). Design and development of grass cutter using solar renewable energy source. In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 113–117). IEEE. [Google Scholar] [Crossref]

2. Diwakaran, S., Kumar, M. D. V., Mohanreddy, P. S., Rishika, Ch., Sreenivasulu, P., & Sivasubramanian, M. (2023). Design of an autonomous mower with height adjustable cutting motor. In 2023 4th International Conference on Signal Processing and Communication (ICSPC) (pp. 348–352). IEEE. [Google Scholar] [Crossref]

3. Ramisetti, V., Chowdary, P. S., Bankuru, C., Peda, S., & Pasala, M. (2024). Automatic grass cutting robot using Arduino and ultrasonic sensor. In 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (pp. 1–4). IEEE. [Google Scholar] [Crossref]

4. Smith, A. D., Chang, H. J., & Blanchard, E. J. (2012). An outdoor high-accuracy local positioning system for an autonomous robotic golf greens mower. In 2012 IEEE International Conference on Robotics and Automation (pp. 2633–2639). IEEE. [Google Scholar] [Crossref]

5. Nishimura, Y., & Yamaguchi, T. (2023). Grass cutting robot for inclined surfaces in hilly and mountainous areas. Sensors, 23(1). [Google Scholar] [Crossref]

6. Athipatla, S., Kandati, B., Kanipaku, S., & Kalluri, D. (2023). Solar powered Bluetooth controlled grass cutting robot. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1–7). IEEE. [Google Scholar] [Crossref]

7. Hashiyana, V., Haiduwa, T., Suresh, N., Shishiiveni, T., & Mutalya, A. (2020). A prototype of an Android application controlled lawnmower. In 2020 IST-Africa Conference (IST-Africa) (pp. 1–5). IEEE. [Google Scholar] [Crossref]

8. M. K., R., V., S., K., C., R. G., R. R., D., N., & S., T. (2023). Design and implementation of IoT enabled grass cutting robot powered by solar PV system. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 1402–1406). IEEE. [Google Scholar] [Crossref]

9. A., R., A., S., A., T., B., E., P., A., & S., S. M. (2023). Electrical energy-powered grass cutter with autonomous operation and pollution mitigation. In 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) (pp. 1–6). IEEE. [Google Scholar] [Crossref]

10. Balaganesan, S. M., Abishek, S., Aravinth, R., & Maignanamoorthy, A. S. N. (2023). Solar based grass cutter robot. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1676–1680). IEEE. [Google Scholar] [Crossref]

11. Habib, Md. R., et al. (2019). PID controller based automatic solar power driven grass cutting machine. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1–4). IEEE. [Google Scholar] [Crossref]

12. Sunanda, C. V., Gowtham, G., Pradeesha, J., & Dheeraj, K. (2024). Automated solar based electric grass cutter with multi-purpose robotic vehicle. In 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS) (pp. 1–5). IEEE. [Google Scholar] [Crossref]

13. Hwang, G. H., Lee, S. W., & Jeon, J. (2024). ROS2 implementation of object detection and distance estimation using camera and 2D LiDAR fusion in autonomous vehicle. In 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE) (pp. 1–5). IEEE. [Google Scholar] [Crossref]

14. Liu, J., Sun, Q., Fan, Z., & Jia, Y. (2018). TOF lidar development in autonomous vehicle. In 2018 IEEE 3rd Optoelectronics Global Conference (OGC) (pp. 185–190). IEEE. [Google Scholar] [Crossref]

15. Liu, S., & Nakanishi, T. (2022). Obstacles detection and motion estimation by using multiple lidar sensors data. In 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) (pp. 196–201). IEEE. [Google Scholar] [Crossref]

16. Liu, Y., Lu, Y., Peng, C., Bu, Q., Liang, Y. C., & Sun, J. (2023). Autonomous vehicle based on ROS2 for indoor package delivery. In 2023 28th International Conference on Automation and Computing (ICAC) (pp. 1–7). IEEE. [Google Scholar] [Crossref]

17. Suherman, Pinem, M., Zardika, A., & Siregar, Y. (2020). Location misplacement analysis on global positioning system. In 2020 4th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM) (pp. 246–249). IEEE. [Google Scholar] [Crossref]

18. Zhao, Yu., Yang, Zh., Song, Ch., & Xiong, D. (2018). Vehicle dynamic model-based integrated navigation system for land vehicles. In 2018 25th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS) (pp. 1–4). IEEE. [Google Scholar] [Crossref]

19. Wang, W., Wu, Z., & Zhang, H. (2018). An adaptive cascaded Kalman filter for two-antenna GPS/MEMS-IMU integration. In 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) (pp. 833–837). IEEE. [Google Scholar] [Crossref]

20. Zhou, S., Zhao, P., & Jin, J. (2020). Cascaded GPS/IMU integrated positioning system based on vehicle kinematics model. In 2020 Chinese Control and Decision Conference (CCDC) (pp. 1134–1139). IEEE. [Google Scholar] [Crossref]

21. Hashemi, M., & Karimi, H. A. (2016). A machine learning approach to improve the accuracy of GPS-based map-matching algorithms. In 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) (pp. 77–86). IEEE. [Google Scholar] [Crossref]

22. Nimvari, Z. E., & Mosavi, M. R. (2017). Accurate prediction of differential GPS corrections using fuzzy cognitive map. In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS) (pp. 64–68). IEEE. [Google Scholar] [Crossref]

23. Huang, J., et al. (2018). Automatic generation of road maps from low quality GPS trajectory data via structure learning. IEEE Access, 6, 71965–71975. [Google Scholar] [Crossref]

24. Chung, C.-H., Wang, K.-C., Liu, K.-T., Wu, Y.-T., Lin, C.-C., & Chang, C.-Y. (2020). Path planning algorithm for robotic lawnmower using RTK-GPS localization. In 2020 International Symposium on Community-centric Systems (CcS) (pp. 1–4). IEEE. [Google Scholar] [Crossref]

25. Fan, J. (2023). Research on real-time SLAM algorithm based on ROS node. In 2023 12th International Conference of Information and Communication Technology (ICTech) (pp. 515–520). IEEE. [Google Scholar] [Crossref]

26. Arunkumar, P. M., Muthu, N., Prathuman, V. G., Sadham Ukkashi, A. M., & Vyshnav Achuthan, P. V. (2021). Simultaneous localization and mapping technique based autonomous lawn mower. In 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 767–771). IEEE. [Google Scholar] [Crossref]

27. Catapang, A. N., & Ramos, M. (2016). Obstacle detection using a 2D LIDAR system for an autonomous vehicle. In 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 441–445). IEEE. [Google Scholar] [Crossref]

28. M., T. R., & M., A. (2019). Obstacle detection and obstacle avoidance algorithm based on 2-D RPLiDAR. In 2019 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–4). IEEE. [Google Scholar] [Crossref]

29. Maruyama, Y., Kato, S., & Azumi, T. (2016). Exploring the performance of ROS2. In 2016 International Conference on Embedded Software (EMSOFT) (pp. 1–10). IEEE. [Google Scholar] [Crossref]

30. K., M. J., Babu, A. V., Damodaran, S., James, R. K., Murshid, M., & Warrier, T. S. (2024). ROS2 - Powered autonomous navigation for TurtleBot3: Integrating Nav2 stack in Gazebo, RViz and real-world environments. In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) (pp. 1–6). IEEE. [Google Scholar] [Crossref]

31. Cen Cheng, P. D., Indri, M., Sibona, F., De Rose, M., & Prato, G. (2022). Dynamic path planning of a mobile robot adopting a costmap layer approach in ROS2. In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–8). IEEE. [Google Scholar] [Crossref]

32. Guo, J., Du, J., & Xu, D. (2018). Navigation and positioning system applied in underground driverless vehicle based on IMU. In 2018 International Conference on Robots & Intelligent System (ICRIS) (pp. 13–16). IEEE. [Google Scholar] [Crossref]

33. Liu, Z. (2021). Implementation of SLAM and path planning for mobile robots under ROS framework. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 1096–1100). IEEE. [Google Scholar] [Crossref]

34. Phueakthong, P., & Varagul, J. (2021). A development of mobile robot based on ROS2 for navigation application. In 2021 International Electronics Symposium (IES) (pp. 517–520). IEEE. [Google Scholar] [Crossref]

35. Rihan, Y., Zein, I., Alaraj, M., & Soudan, B. (2024). Campus courier: An autonomous delivery robot on-campus. In 2024 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1–6). IEEE. [Google Scholar] [Crossref]

36. Abdullah, L., Jamaludin, Z., Chiew, T. H., & Rafan, N. A. (2012). Systematic method for cutting forces characterization for XY milling table ballscrew drive system. International Journal of Mechanical and Mechatronics Engineering, 12(6), 28-33. [Google Scholar] [Crossref]

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