{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:52:15Z","timestamp":1770742335254,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guoqiang Research Institute of Tsinghua University","award":["2020GQI1003"],"award-info":[{"award-number":["2020GQI1003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path\u2019s odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot\u2019s pose and position data.<\/jats:p>","DOI":"10.3390\/s23010517","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T02:54:55Z","timestamp":1672800895000},"page":"517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model"],"prefix":"10.3390","volume":"23","author":[{"given":"Junjie","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4444-4805","authenticated-orcid":false,"given":"Jing-Shan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergey Yurievich","family":"Misyurin","sequence":"additional","affiliation":[{"name":"Moscow Engineering Physics Institute, National Research Nuclear University MEPhI, Moscow 115409, Russia"},{"name":"Blagonravov Mechanical Engineering Research Institute RAS, Malyi Kharitonievsky per.4, Moscow 101990, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1053-9686","authenticated-orcid":false,"given":"Daniel","family":"Martins","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of Santa Catarina, Florian\u00f3polis 88040-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4412","DOI":"10.1007\/s10489-018-1216-0","article-title":"A swarm intelligence approach for the colored traveling salesman problem","volume":"48","author":"Pandiri","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1111\/itor.12609","article-title":"The intermittent travelling salesman problem","volume":"27","author":"Pham","year":"2018","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10479-019-03412-x","article-title":"The single line moving target traveling salesman problem with release times","volume":"289","author":"Hassoun","year":"2020","journal-title":"Ann. Oper. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10732-019-09406-z","article-title":"The selective traveling salesman problem with draft limits","volume":"26","author":"Gelareh","year":"2020","journal-title":"J. Heuristics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TITS.2020.2972389","article-title":"Delaunay-Triangulation-Based Variable Neighborhood Search to Solve Large-Scale General Colored Traveling Salesman Problems","volume":"22","author":"Xu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, K., Xu, B.L., Lu, M.L., Shi, J., and Li, Z. (2022, January 15\u201319). An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots. Proceedings of the 13th International Conference on Swarm Intelligence (ICSI), Xi\u2019an, China.","DOI":"10.1007\/978-3-031-09726-3_5"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Valero-Gomez, A., Valero-Gomez, J., Castro-Gonzalez, A., and Moreno, L. (2011, January 7\u201311). Use of genetic algorithms for target distribution and sequencing in multiple robot operations. Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), Phuket, Thailand.","DOI":"10.1109\/ROBIO.2011.6181716"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nemoto, K., and Aiyama, Y. (2019, January 18\u201320). Planning Method of Near-Minimum-Time Task Tour for Industrial Point-to-Point Robot. Proceedings of the 9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS)\/IEEE Conference on Robotics, Automation and Mechatronics (RAM), Bangkok, Thailand.","DOI":"10.1109\/CIS-RAM47153.2019.9095547"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Seo, J., Yim, M., and Kumar, V. (2016, January 16\u201321). Assembly sequence planning for constructing planar structures with rectangular modules. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487761"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, J.B., Liu, G.D., Xu, J.P., Zhao, Z.Y., Chen, Z.H., Yang, M., Wang, X.Y., and Bai, Y.T. (2022). A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment. Sensors, 22.","DOI":"10.3390\/s22072429"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xie, X.Y., Wang, Y.L., Wu, Y.J., You, M., and Zhang, S.Y. (2022, January 7\u201310). Random Patrol Path Planning for Unmanned Surface Vehicles in Shallow Waters. Proceedings of the 19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA), Guilin, China.","DOI":"10.1109\/ICMA54519.2022.9856340"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11370-017-0244-7","article-title":"Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment","volume":"11","author":"Faridi","year":"2018","journal-title":"Intell. Serv. Robot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"162583","DOI":"10.1109\/ACCESS.2019.2950725","article-title":"A New Path Evaluation Method for Path Planning with Localizability","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107274","DOI":"10.1016\/j.compag.2022.107274","article-title":"Many-objective evolutionary algorithm based agricultural mobile robot route planning","volume":"200","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","first-page":"113","article-title":"UMBmark: A benchmark test for measuring odometry errors in mobile robots","volume":"2591","author":"Borenstein","year":"1995","journal-title":"Proc. SPIE\u2014Int. Soc. Opt. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1007\/s12206-011-0334-y","article-title":"Accurate calibration of kinematic parameters for two wheel differential mobile robots","volume":"25","author":"Lee","year":"2011","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_17","first-page":"1606","article-title":"Accurate relative localization using odometry","volume":"2","author":"Doh","year":"2003","journal-title":"IEEE Int. Conf. Robot. Autom."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s10514-006-6474-8","article-title":"Relative localization using path odometry information","volume":"21","author":"Doh","year":"2006","journal-title":"Auton. Robot."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69070","DOI":"10.1109\/ACCESS.2019.2919335","article-title":"The Impact of Parametric Uncertainties on Mobile Robots Velocities and Pose Estimation","volume":"7","author":"Carvalho","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"94981","DOI":"10.1109\/ACCESS.2021.3093978","article-title":"A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator","volume":"9","author":"Martin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102078","DOI":"10.1016\/j.rcim.2020.102078","article-title":"A vision-based fast base frame calibration method for coordinated mobile manipulators","volume":"68","author":"Fan","year":"2021","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3854","DOI":"10.1109\/TIE.2021.3075852","article-title":"ST-FMT*: A Fast Optimal Global Motion Planning for Mobile Robot","volume":"69","author":"Wu","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7244","DOI":"10.1109\/TIE.2020.2998740","article-title":"MOD-RRT*: A Sampling-Based Algorithm for Robot Path Planning in Dynamic Environment","volume":"68","author":"Qi","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"692","DOI":"10.2991\/ijcis.11.1.53","article-title":"A Genetic Algorithm with New Local Operators for Multiple Traveling Salesman Problems","volume":"11","author":"Lo","year":"2018","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ijpe.2018.05.025","article-title":"Software vendors travel management decisions using an elitist nonhomogeneous genetic algorithm","volume":"202","author":"Jana","year":"2018","journal-title":"Int. J. Prod. Econ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6177","DOI":"10.1007\/s12652-019-01635-1","article-title":"Path planning and control of soccer robot based on genetic algorithm","volume":"11","author":"Chen","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1007\/s11227-021-04031-9","article-title":"A new hybrid algorithm for path planning of mobile robot","volume":"78","author":"Zhang","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.jbiomech.2018.01.024","article-title":"Validation of an ambient system for the measurement of gait parameters","volume":"69","author":"Dubois","year":"2018","journal-title":"J. Biomech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110461","DOI":"10.1016\/j.measurement.2021.110461","article-title":"Obtaining lower-body Euler angle time series in an accurate way using depth camera relying on Optimized Kinect CNN","volume":"188","author":"Guo","year":"2022","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:57:07Z","timestamp":1760119027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,3]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010517"],"URL":"https:\/\/doi.org\/10.3390\/s23010517","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,3]]}}}