000168183 001__ 168183
000168183 005__ 20260210085358.0
000168183 0247_ $$2doi$$a10.1109/LRA.2025.3557233
000168183 0248_ $$2sideral$$a143893
000168183 037__ $$aART-2025-143893
000168183 041__ $$aeng
000168183 100__ $$aRückin, Julius
000168183 245__ $$aTowards Map-Agnostic Policies for Adaptive Informative Path Planning
000168183 260__ $$c2025
000168183 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168183 5203_ $$aRobots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is adaptively replanning paths online as the terrain is explored given limited onboard compute resources. Recently, learning-based approaches emerged that train planning policies offline and enable computationally efficient online replanning performing policy inference. These approaches are designed and trained for terrain monitoring missions assuming a single specific map representation, which limits their applicability to different terrains. To address this limitation, we propose a novel formulation of the adaptive informative path planning problem unified across different map representations, enabling training and deploying planning policies in a larger variety of monitoring missions. Experimental results validate that our novel formulation easily integrates with classical non-learning-based planning approaches while maintaining their performance. Our trained planning policy performs similarly to state-of-the-art map-specifically trained policies. We validate our learned policy on unseen real-world terrain datasets.
000168183 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000168183 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000168183 700__ $$aMorilla-Cabello, David$$uUniversidad de Zaragoza
000168183 700__ $$aStachniss, Cyrill
000168183 700__ $$0(orcid)0000-0002-5176-3767$$aMontijano, Eduardo$$uUniversidad de Zaragoza
000168183 700__ $$aPopovic, Marija
000168183 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000168183 773__ $$g10, 5 (2025), 5114-5121$$pIEEE Robot. autom. let.$$tIEEE Robotics and Automation Letters$$x2377-3766
000168183 8564_ $$s5628632$$uhttps://zaguan.unizar.es/record/168183/files/texto_completo.pdf$$yPostprint
000168183 8564_ $$s3450709$$uhttps://zaguan.unizar.es/record/168183/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000168183 909CO $$ooai:zaguan.unizar.es:168183$$particulos$$pdriver
000168183 951__ $$a2026-02-10-08:35:34
000168183 980__ $$aARTICLE