000148195 001__ 148195
000148195 005__ 20250923084440.0
000148195 0247_ $$2doi$$a10.1016/j.robot.2024.104892
000148195 0248_ $$2sideral$$a141706
000148195 037__ $$aART-2024-141706
000148195 041__ $$aeng
000148195 100__ $$aMarchukov, Yaroslav
000148195 245__ $$aOccupation-aware planning method for robotic monitoring missions in dynamic environments
000148195 260__ $$c2024
000148195 5060_ $$aAccess copy available to the general public$$fUnrestricted
000148195 5203_ $$aThis paper presents a method for robotic monitoring missions in the presence of moving obstacles. Although the scenario map is known, the robot lacks information about the movement of dynamic obstacles during the monitoring mission. Numerous local planners have been developed in recent years for navigating highly dynamic environments. However, the absence of a global planner for these environments can result in unavoidable collisions or the inability to successfully complete missions in densely populated areas, such as a scenario monitoring in our case. This work addresses the development and evaluation of a global planner, (Monitoring Avoiding Dynamic Areas), aimed at enhancing the deployment of robots in such challenging conditions. The robot plans and executes the mission using the proposed two-step approach. The first step involves selecting the observation goal based on the environment’s distribution and estimated monitoring costs. In the second step, the robot identifies areas with moving obstacles and obtains paths avoiding densely occupied dynamic regions based on their occupation. Quantitative and qualitative results based on simulations and on real-world experimentation, confirm that the proposed method allows the robot to effectively monitor most of the environment while avoiding densely occupied dynamic areas.
000148195 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000148195 590__ $$a5.2$$b2024
000148195 592__ $$a1.168$$b2024
000148195 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b18 / 89 = 0.202$$c2024$$dQ1$$eT1
000148195 593__ $$aComputer Science Applications$$c2024$$dQ1
000148195 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b51 / 204 = 0.25$$c2024$$dQ1$$eT1
000148195 593__ $$aSoftware$$c2024$$dQ1
000148195 591__ $$aROBOTICS$$b13 / 48 = 0.271$$c2024$$dQ2$$eT1
000148195 593__ $$aMathematics (miscellaneous)$$c2024$$dQ1
000148195 593__ $$aControl and Systems Engineering$$c2024$$dQ1
000148195 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000148195 700__ $$0(orcid)0000-0002-0449-2300$$aMontano, Luis$$uUniversidad de Zaragoza
000148195 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000148195 773__ $$g185 (2024), 104892 [17 pp.]$$pRobot. auton. syst.$$tROBOTICS AND AUTONOMOUS SYSTEMS$$x0921-8890
000148195 787__ $$tThe code used for this research$$whttps://github.com/yamarle/mada.git
000148195 8564_ $$s2180319$$uhttps://zaguan.unizar.es/record/148195/files/texto_completo.pdf$$yPreprint
000148195 8564_ $$s2388666$$uhttps://zaguan.unizar.es/record/148195/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000148195 909CO $$ooai:zaguan.unizar.es:148195$$particulos$$pdriver
000148195 951__ $$a2025-09-22-14:50:00
000148195 980__ $$aARTICLE