{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T13:57:44Z","timestamp":1771941464235,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This study benchmarks three ROS 2 Navigation2 local controllers\u2014Dynamic Window Approach Based (DWB), Regulated Pure Pursuit (RPP), and Model Predictive Path Integral (MPPI)\u2014under three complementary operational stressors in simulation: (i) a structured corridor with a transient dynamic obstacle, (ii) a sloped environment where terrain inclination biases a planar 2D LiDAR costmap through spurious occupancy projections, and (iii) a narrow corridor that amplifies inflation effects. A reproducible rosbag2-based protocol records five key performance indicators per trial: time-to-goal, lateral tracking RMSE, stopped time, heading oscillations, and control effort. With 15 independent repetitions per cell (scene \u00d7 controller \u00d7 direction), the design yields 270 trials. The results expose complementary value profiles: RPP minimizes mission time, DWB produces the fewest heading oscillations through critic-based shaping, and MPPI achieves the lowest control effort via smooth trajectory generation. In the sloped scene, the tracking RMSE differences compress across all controllers\u2014a signature of a perception-limited regime in which costmap bias overshadows controller logic. These findings translate into an actionable controller-selection guide and a reproducible baseline for quantifying gains from upstream perception and cost-representation improvements. In concrete terms, we contribute (i) a controlled benchmark with fixed planning, localization, and costmaps, (ii) full configuration disclosure (controller parameters, costmap settings, and software versions with package pinning), and (iii) a scene-specific costmap distortion index that links slope-induced local cost bias to measurable performance shifts, underpinning a decision matrix for controller selection in semi-structured environments.<\/jats:p>","DOI":"10.3390\/systems14030228","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T12:19:23Z","timestamp":1771935563000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Theoretical Analysis and Systematic Comparison of Local Navigation Control Strategies in Semi-Structured Environments: A Systems Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-8928","authenticated-orcid":false,"given":"Claudio","family":"Urrea","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170020, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3552-1709","authenticated-orcid":false,"given":"Kevin","family":"Valencia-Arag\u00f3n","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170020, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"eabp9742","DOI":"10.1126\/scirobotics.abp9742","article-title":"CERBERUS in the DARPA Subterranean Challenge","volume":"7","author":"Tranzatto","year":"2022","journal-title":"Sci. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, X., Lin, X., Yao, W., Ma, H., Zheng, J., and Ma, B. (2023). A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation. Remote Sens., 15.","DOI":"10.3390\/rs15010186"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dreissig, M., Scheuble, D., Piewak, F., and Boedecker, J. (2023, January 4\u20137). Survey on LiDAR Perception in Adverse Weather Conditions. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA.","DOI":"10.1109\/IV55152.2023.10186539"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105850","DOI":"10.1016\/j.autcon.2024.105850","article-title":"RGB-LiDAR sensor fusion for dust de-filtering in autonomous excavation applications","volume":"168","author":"Parsons","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, S., Xie, Y., Xiong, T., and Wu, M. (2024). A Review of Sensing Technologies for Indoor Autonomous Mobile Robots. Sensors, 24.","DOI":"10.3390\/s24041222"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Konieczna-Fu\u0142awka, M., Koval, A., Nikolakopoulos, G., and Fumagalli, M. (2025). Autonomous Mobile Inspection Robots for Underground Mining: Current Trends and Future Directions. Sensors, 25.","DOI":"10.3390\/s25123598"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s44443-025-00221-0","article-title":"Localization and mapping method for forestry mobile platforms based on enhanced Hector SLAM","volume":"37","author":"Yao","year":"2025","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Urrea, C., Valencia-Arag\u00f3n, K., and Kern, J. (2025). Semantic Priority Navigation for Energy-Aware Mining Robots. Systems, 13.","DOI":"10.3390\/systems13090799"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104493","DOI":"10.1016\/j.robot.2023.104493","article-title":"From the desks of ROS maintainers: A survey of modern & capable mobile robotics algorithms in the robot operating system 2","volume":"168","author":"Macenski","year":"2023","journal-title":"Robot. Auton. Syst."},{"key":"ref_10","unstructured":"Navigation 2 Project (2026, January 26). Controller Server\u2014Navigation2 Documentation. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-controller-server.html."},{"key":"ref_11","unstructured":"Navigation 2 Project (2026, January 26). Navigation2 Documentation (ROS 2 Nav2). Available online: https:\/\/docs.nav2.org\/."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Elbouhy, S.M., Adamanov, A., Braun, P.M., and Wilhelm Rose, H. (2025, January 14\u201318). Comparative Analysis of Local Trajectory Planning Algorithms in ROS2. Proceedings of the 2025 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Palermo, Italy.","DOI":"10.1109\/SIMPAR62925.2025.10979015"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/100.580977","article-title":"The dynamic window approach to collision avoidance","volume":"4","author":"Fox","year":"1997","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_14","unstructured":"Navigation 2 Project (2026, January 26). DWB Controller. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-dwb-controller.html."},{"key":"ref_15","unstructured":"Navigation 2 Project (2026, January 26). nav2_regulated_pure_pursuit_controller (Navigation2 Repository). Available online: https:\/\/github.com\/ros-navigation\/navigation2\/tree\/main\/nav2_regulated_pure_pursuit_controller."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10514-023-10097-6","article-title":"Regulated Pure Pursuit for robot path tracking","volume":"47","author":"Macenski","year":"2023","journal-title":"Auton. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"344","DOI":"10.2514\/1.G001921","article-title":"Model Predictive Path Integral Control: From Theory to Parallel Computation","volume":"40","author":"Williams","year":"2017","journal-title":"J. Guid. Control. Dyn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TRO.2018.2865891","article-title":"Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving","volume":"34","author":"Williams","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Williams, G., Drews, P., Goldfain, B., Rehg, J.M., and Theodorou, E.A. (2016, January 16\u201321). Aggressive driving with model predictive path integral control. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487277"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Raisi, M., Noohian, A., and Fallah, S. (2022). A fault-tolerant and robust controller using model predictive path integral control for free-flying space robots. Front. Robot. AI, 9.","DOI":"10.3389\/frobt.2022.1027918"},{"key":"ref_21","unstructured":"Open Robotics (2026, January 26). Recording and Playing Back Data. Available online: https:\/\/docs.ros.org\/en\/humble\/Tutorials\/Beginner-CLI-Tools\/Recording-And-Playing-Back-Data\/Recording-And-Playing-Back-Data.html."},{"key":"ref_22","unstructured":"ROS 2 Community (2026, January 26). rosbag2\u2014The Tool for Recording and Playback of Communications in ROS 2 Systems. Available online: https:\/\/github.com\/ros2\/rosbag2\/blob\/master\/README.md."},{"key":"ref_23","unstructured":"Xu, Z., Wen, X., Song, Y., and Yin, S. (2024). ROSfs: A User-Level File System for ROS. arXiv."},{"key":"ref_24","unstructured":"Navigation 2 Project (2026, January 26). Costmap 2D\u2014Navigation2 Documentation. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-costmaps.html."},{"key":"ref_25","unstructured":"Navigation 2 Project (2026, January 26). Inflation Layer\u2014Navigation2 Documentation. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/costmap-plugins\/inflation.html."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TRO.2023.3339989","article-title":"A Novel Graph-Based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints","volume":"40","author":"Liu","year":"2024","journal-title":"IEEE Trans. Robot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/MRA.2022.3213466","article-title":"Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]","volume":"29","author":"Xiao","year":"2022","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18334","DOI":"10.1038\/s41598-024-69040-z","article-title":"Navigation benchmarking for autonomous mobile robots in hospital environment","volume":"14","author":"Rondoni","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"K\u00e4stner, L., Carstens, R., Nahrwold, L., Liebig, C., Shcherbyna, V., Lee, S., and Lambrecht, J. (2023, January 10\u201314). Demonstrating Arena-Web: A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches. Proceedings of the Robotics: Science and Systems (RSS), Daegu, Republic of Korea.","DOI":"10.15607\/RSS.2023.XIX.088"},{"key":"ref_30","unstructured":"Shcherbyna, V., K\u00e4stner, L., Diaz, D., Nguyen, H.G., Schreff, M.H.K., Lenz, T., Kreutz, J., Martban, A., Zeng, H., and Soh, H. (2024). Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-Centric Navigation Using Generative-Model-Based Environment Generation. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7130","DOI":"10.1109\/LRA.2023.3316072","article-title":"HuNavSim: A simulator for benchmarking human-aware robot navigation","volume":"8","author":"Pardo","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sani, E., Buisan, M., Doria, A., Carpin, S., and Sgorbissa, A. (2024, January 13\u201317). Improving the ROS 2 Navigation Stack with Real-Time Local Costmap Updates for Agricultural Applications. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10610984"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, B., Tao, A., Thomas, H., Zhang, J., and Barfoot, T.D. (2024). MakeWay: Object-Aware Costmaps for Proactive Indoor Navigation Using LiDAR. arXiv.","DOI":"10.21428\/d82e957c.1aaaa47c"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104363","DOI":"10.1016\/j.robot.2023.104363","article-title":"Object-wise comparison of LiDAR occupancy grid scan rendering methods","volume":"161","author":"Godoy","year":"2023","journal-title":"Robot. Auton. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, Z., and Taguchi, R. (2023). Faster Implementation of The Dynamic Window Approach Based on Non-Discrete Path Representation. Mathematics, 11.","DOI":"10.3390\/math11214424"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gong, X., Gao, Y., Wang, F., Zhu, D., Zhao, W., Wang, F., and Liu, Y. (2024). A Local Path Planning Algorithm for Robots Based on Improved DWA. Electronics, 13.","DOI":"10.3390\/electronics13152965"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Arce, D., Solano, J., and Beltr\u00e1n, C. (2023). A Comparison Study Between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios. Sensors, 23.","DOI":"10.3390\/s23249672"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jiang, X., Kuroiwa, T., Cao, Y., Sun, L., Zhang, H., Kawaguchi, T., and Hashimoto, S. (2025). Enhanced Pure Pursuit Path Tracking Algorithm for Mobile Robots Optimized by NSGA-II with High-Precision GNSS Navigation. Sensors, 25.","DOI":"10.3390\/s25030745"},{"key":"ref_39","unstructured":"Navigation 2 Project (2026, January 26). Model Predictive Path Integral Controller. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-mppic.html."},{"key":"ref_40","unstructured":"Pezzato, C., Salmi, C., Trevisan, E., Spahn, M., Alonso-Mora, J., and Hern\u00e1ndez Corbato, C. (2023). Sampling-Based Model Predictive Control Leveraging Parallelizable Physics Simulations. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5871","DOI":"10.1109\/LRA.2024.3397083","article-title":"Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers","volume":"9","author":"Trevisan","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ifacol.2023.01.137","article-title":"2.5D Mapping, Pathfinding and Path Following For Navigation Of A Differential Drive Robot In Uneven Terrain","volume":"55","author":"Dergachev","year":"2022","journal-title":"IFAC-PapersOnLine"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Afzalaghaeinaeini, A., Seo, J., Lee, D., and Lee, H. (2022). Design of Dust-Filtering Algorithms for LiDAR Sensors Using Intensity and Range Information in Off-Road Vehicles. Sensors, 22.","DOI":"10.3390\/s22114051"},{"key":"ref_44","first-page":"100203","article-title":"A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration","volume":"5","author":"Chen","year":"2025","journal-title":"Biomim. Intell. Robot."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gomes, T., Matias, D., Campos, A., Cunha, L., and Roriz, R. (2023). A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors. Sensors, 23.","DOI":"10.3390\/s23020601"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8138","DOI":"10.1109\/LRA.2022.3187278","article-title":"GA-Nav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments","volume":"7","author":"Guan","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Frey, M., Mattamala, M., Chebrolu, N., Cadena, C., Fallon, M., and Hutter, M. (2023, January 10\u201314). Fast Traversability Estimation for Wild Visual Navigation. Proceedings of the Robotics: Science and Systems (RSS), Daegu, Republic of Korea.","DOI":"10.15607\/RSS.2023.XIX.054"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sathyamoorthy, A.J., Patel, U., Guan, T., and Manocha, D. (2020). Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds. arXiv.","DOI":"10.1109\/LRA.2020.2996593"},{"key":"ref_49","unstructured":"Navigation 2 Project (2026, January 26). OscillationCritic\u2014Navigation2 Documentation. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/trajectory_critics\/oscillation.html."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1145\/3700599","article-title":"Principles and Guidelines for Evaluating Social Robot Navigation Algorithms","volume":"14","author":"Francis","year":"2025","journal-title":"ACM Trans.-Hum.-Robot. Interact."},{"key":"ref_51","unstructured":"GazeboSim Community (2026, January 26). ros_gz: Integration Between ROS (1&2) and Gazebo. Available online: https:\/\/github.com\/gazebosim\/ros_gz?tab=readme-ov-file."},{"key":"ref_52","unstructured":"Navigation 2 Project (2026, January 26). AMCL Configuration in Navigation 2. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-amcl.html."},{"key":"ref_53","unstructured":"Foote, T. (2026, January 26). Clock and Time. Available online: https:\/\/design.ros2.org\/articles\/clock_and_time.html."},{"key":"ref_54","unstructured":"Navigation 2 Project (2026, January 26). Regulated Pure Pursuit Controller: Configuration and Usage. Available online: https:\/\/docs.nav2.org\/configuration\/packages\/configuring-regulated-pp.html."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T13:00:19Z","timestamp":1771938019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["systems14030228"],"URL":"https:\/\/doi.org\/10.3390\/systems14030228","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]}}}