{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:29:41Z","timestamp":1787027381250,"version":"build-2736575974"},"reference-count":25,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T00:00:00Z","timestamp":1786752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["ORF-2026-1302"],"award-info":[{"award-number":["ORF-2026-1302"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]},{"award":["ORF-2026-1302"],"award-info":[{"award-number":["ORF-2026-1302"]}],"id":[{"id":"https:\/\/ror.org\/02f81g417","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous robotic navigation in dynamic environments depends on sensor-derived occupancy maps that are often degraded by occlusion, localization error, dynamic blockage, incomplete observation, and perception noise. These uncertainties can make a nominally short route unsafe after deployment, motivating conservative route selection for collision-aware path planning under sensor-derived map uncertainty. We formulate Conservative Robust Route Selection (CRRS) as a finite-scenario robust optimization and route-selection framework for autonomous robotic path planning under this uncertainty. CRRS constructs a heterogeneous portfolio of candidate routes, scores each route using nominal and plausible-world information only, and applies a validation-frozen conservative override rule that defaults to the scenario ensemble unless a candidate route satisfies predefined feasibility, risk, clearance, and cost-ratio guards. The evaluation protocol separates implementation auditing, candidate-pool expansion, validation-based selector design, frozen confirmation, public-benchmark validation, simulated sensor-model validation, and a controlled validation\u2013held-out mismatch stress test. On the generated 30-domain MovingAI-format benchmark, candidate-pool expansion finds strict-safe-superior candidates in 113\/150 matched groups, and the frozen selector reduces plausible collision from 0.1250 to 0.0807 and held-out collision from 0.3053 to 0.2937. On an official long-distance MovingAI subset with 260 queries and a minimum start-goal distance of 100 cells, CRRS reduces the held-out collision from 0.9648 for the scenario ensemble to 0.8822, with 141 wins, zero losses, and 119 ties. In an additional LiDAR\/SLAM-inspired simulated sensor-model validation on 200 routed official-query problems, CRRS reduces the held-out collision from 0.4059 to 0.3768 relative to the scenario ensemble. A validation\u2013held-out mismatch stress ablation isolates the conservative override rule: CRRS differs from CVaR-only on 73\/260 problems, obtains a lower or equal held-out collision in every comparison, and avoids the 18 harmful held-out losses incurred by CVaR-only relative to the scenario ensemble. The resulting claim is deliberately scoped: CRRS improves aggregate route robustness over a strong scenario-ensemble default on the evaluated robotic path-planning benchmarks, while real-time deployment, live sensor integration with calibrated sensors, physical robot validation, family-level variation, and benchmark-specific uncertainty models remain limitations.<\/jats:p>","DOI":"10.3390\/s26165172","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:00:05Z","timestamp":1786964405000},"page":"5172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sensor-Uncertainty-Aware Conservative Robust Route Selection for Autonomous Robot Path Planning Under Occupancy-Grid Map Uncertainty"],"prefix":"10.3390","volume":"26","author":[{"given":"Ali S.","family":"Allahloh","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Zakir Husain College of Engineering and Technology (ZHCET), Aligarh Muslim University, Aligarh 202002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4666-3283","authenticated-orcid":false,"given":"Atef M.","family":"Ghaleb","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering and Advanced Computing, Alfaisal University, Riyadh 11533, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5586-3546","authenticated-orcid":false,"given":"Mohammad","family":"Sarfraz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Zakir Husain College of Engineering and Technology (ZHCET), Aligarh Muslim University, Aligarh 202002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1372-9725","authenticated-orcid":false,"given":"Abdelghani","family":"Bouras","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering and Advanced Computing, Alfaisal University, Riyadh 11533, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-7027","authenticated-orcid":false,"given":"Mohammed A. H.","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4192-8713","authenticated-orcid":false,"given":"Adel","family":"Al-Shayea","sequence":"additional","affiliation":[{"name":"Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TSSC.1968.300136","article-title":"A Formal Basis for the Heuristic Determination of Minimum Cost Paths","volume":"4","author":"Hart","year":"1968","journal-title":"IEEE Trans. Syst. Sci. Cybern."},{"key":"ref_2","unstructured":"Koenig, S., and Likhachev, M. (August, January 28). D* Lite. Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, AB, Canada."},{"key":"ref_3","first-page":"767","article-title":"ARA*: Anytime A* with Provable Bounds on Sub-Optimality","volume":"16","author":"Likhachev","year":"2003","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","unstructured":"Nash, A., Daniel, K., Koenig, S., and Felner, A. (2007, January 22\u201326). Theta*: Any-Angle Path Planning on Grids. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/2.30720","article-title":"Using Occupancy Grids for Mobile Robot Perception and Navigation","volume":"22","author":"Elfes","year":"1989","journal-title":"Computer"},{"key":"ref_6","unstructured":"Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics, MIT Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TRO.2006.889486","article-title":"Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters","volume":"23","author":"Grisetti","year":"2007","journal-title":"IEEE Trans. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dellaert, F., Fox, D., Burgard, W., and Thrun, S. (1999). Monte Carlo Localization for Mobile Robots. Proceedings of the IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ROBOT.1999.772544"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/70.508439","article-title":"Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces","volume":"12","author":"Kavraki","year":"1996","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_10","unstructured":"LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Computer Science Department, Iowa State University. Technical Report 98-11."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1177\/0278364911406761","article-title":"Sampling-Based Algorithms for Optimal Motion Planning","volume":"30","author":"Karaman","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1177\/0278364913488805","article-title":"CHOMP: Covariant Hamiltonian Optimization for Motion Planning","volume":"32","author":"Zucker","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1287\/mnsc.17.11.712","article-title":"Finding the K Shortest Loopless Paths in a Network","volume":"17","author":"Yen","year":"1971","journal-title":"Manag. Sci."},{"key":"ref_14","unstructured":"Bhattacharya, S., Likhachev, M., and Kumar, V. (2010, January 11\u201315). Search-Based Path Planning with Homotopy Class Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1177\/0278364908098411","article-title":"Path Deformation Roadmaps: Compact Graphs with Useful Cycles for Motion Planning","volume":"27","author":"Jaillet","year":"2008","journal-title":"Int. J. Robot. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1109\/TRO.2011.2161160","article-title":"Chance-Constrained Optimal Path Planning with Obstacles","volume":"27","author":"Blackmore","year":"2011","journal-title":"IEEE Trans. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21","DOI":"10.21314\/JOR.2000.038","article-title":"Optimization of Conditional Value-at-Risk","volume":"2","author":"Rockafellar","year":"2000","journal-title":"J. Risk"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1177\/02783649251315203","article-title":"Scenario-Based Motion Planning with Bounded Probability of Collision","volume":"44","author":"Ferranti","year":"2025","journal-title":"Int. J. Robot. Res."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Tai, L., Paolo, G., and Liu, M. (2017). Virtual-to-Real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2017.8202134"},{"key":"ref_21","first-page":"1649","article-title":"Sensor Based Planning, Part I: The Generalized Voronoi Graph","volume":"Volume 2","author":"Choset","year":"1995","journal-title":"Proceedings of the IEEE International Conference on Robotics and Automation"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MRA.2012.2205651","article-title":"The Open Motion Planning Library","volume":"19","author":"Moll","year":"2012","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MRA.2015.2448276","article-title":"Benchmarking Motion Planning Algorithms: An Extensible Infrastructure for Analysis and Visualization","volume":"22","author":"Moll","year":"2015","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/LRA.2021.3133603","article-title":"MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets","volume":"7","author":"Chamzas","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TCIAIG.2012.2197681","article-title":"Benchmarks for Grid-Based Pathfinding","volume":"4","author":"Sturtevant","year":"2012","journal-title":"IEEE Trans. Comput. Intell. 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