[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81509-en":3,"doc-seo-81509-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81509,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility","A practical algorithm detects motion planning infeasibility in kinematic robots by approximating a continuous configuration space with a discrete grid where each degree of freedom takes finite values. The method incrementally samples the obstacle region to separate free space into connected components: if start and goal fall into different free-region components, no collision-free path exists. After progressively building a partial obstacle bitmap, the approach partitions the constructed space, checks component connectivity, validates on multi-DOF scenarios up to 5-DOF, and evaluates scalability with experiments on 6-DOF and 7-DOF robots.","arXiv :2501 . 11434v3 [ cs .RO] 10 Jul 2026  \nAn Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility  \nAntony Thomasa , Fulvio Mastrogiovannib , Marco Bagliettoba Robotics Research Center, IIIT Hyderabad, Hyderabad 500032, India.  \nb Department of Informatics, Bioengineering, Robotics, and Systems Engineering,  \nUniversity of Genoa, Via All’Opera Pia 13, 16145 Genoa, Italy.  \nAbstract  \nWe present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot’s configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into different connected regions. For a path to exist between the start and goal configurations, they must lie in the same connected region of the free space. Thus, to ascertain plan infeasibility, we merely need to sample adequate points from the obstacle region that isolate start and goal. Accordingly, we progressively construct the configuration space (initially assumed to be entirely free) by sampling from the discretized space and updating the bitmap cells representing obstacle regions. Subsequently, we partition this partially built configuration space to identify different connected components within it and assess the connectivity of the start and goal cells. We illustrate this methodology on five different scenarios with configuration spaces having up to 5 degreesof-freedom (DOF) . Additionally, we discuss further optimizations designed to significantly accelerate the proposed algorithm. The scalability of our approach to higher-dimensional configuration spaces is also examined, with experimental demonstrations involving 6-DOF and 7-DOF robots.  \nEmail addresses: [antony.thomas@iiit.ac.in](antony.thomas@iiit.ac.in) (Antony Thomas), [fulvio.mastrogiovanni@unige.it](fulvio.mastrogiovanni@unige.it) (Fulvio Mastrogiovanni), [marco.baglietto@unige.it](marco.baglietto@unige.it)[ ](marco.baglietto@unige.it)(Marco Baglietto)  \nKeywords: motion planning, motion planning infeasibility, configuration space obstacles, connected components  \n1. Introduction  \nMotion planning is a fundamental problem in robotics, involving finding a path for a robot from its start configuration to a goal configuration without colliding with obstacles. A complete motion planner can either compute a collision-free path from the start to the goal or conclude that no such path exists. However, complete motion planning is challenging, and most approaches focus on finding a feasible plan with weaker notions of completeness. Resolution complete planners, typically those based on cell decomposition, offer completeness provided that the number of cells used to discretize the configuration space is sufficiently high [1] . Yet, in high-dimensional configuration spaces, such approaches tend to be computationally very expensive. Sampling-based motion planners [2, 3] are typically employed in such cases to find paths as quickly as possible. However, they are only probabilistically complete [4], meaning that ifa plan exists, they will find it given enough time, but if no plan exists, they can run forever (or until a timeout) . Therefore, a timeout is not a guarantee of infeasibility. In this work, we focus on the less examined path non-existence problem and present a simple algorithm that checks for motion planning infeasibility.  \nMotion infeasibility is a critical aspect of many robot planning methodologies. Task and motion planning [5, 6, 7, 8, 9, 10] must consider the feasibility of motion plans to achieve the associated high-level tasks. When motion planning is deemed infeasible, alternative task plans must be generated. Similarly, feasibility checks are fundamental in manipulation tasks amidst clutter or rearrangement planning [11, 12, 13, 14] . This often entails either displacing obstacles obstructing the task, usually identified t","cbCaistLb6y2qkiQ","https://ap.wps.com/l/cbCaistLb6y2qkiQ","pdf",3586336,2,1,44,"English","en",105,"# Introduction\n## Problem of motion planning infeasibility\n## Related planning completeness notions\n# Proposed approach\n## Incremental discrete configuration construction\n## Obstacle sampling and free-space segmentation\n## Connectivity-based infeasibility test\n# Experiments and results\n## Multi-DOF scenario evaluations\n## Scalability to higher-dimensional robots\n# Optimizations","[{\"question\":\"How does the method determine motion planning infeasibility?\",\"answer\":\"It discretizes the configuration space and incrementally samples the obstacle region to isolate free connected components. If the start and goal configurations lie in different connected components of the free space, the plan is infeasible.\"},{\"question\":\"Why is the entire obstacle region not required?\",\"answer\":\"The approach only needs a relevant subset of the obstacle region that separates start and goal into different free regions. This avoids constructing the full configuration space obstacle structure.\"},{\"question\":\"What kinds of robots and configuration dimensions are tested?\",\"answer\":\"The methodology is demonstrated on scenarios with up to 5 degrees of freedom, and scalability is further examined using experimental results on 6-DOF and 7-DOF robots.\"}]",1784173885,111,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-incremental-sampling-and-segmentation-based-approach-for-motion-planning-infeasibility","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/an-incremental-sampling-and-segmentation-based-approach-for-motion-planning-infeasibility/81509/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-21","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method determine motion planning infeasibility?","Question",{"text":75,"@type":76},"It discretizes the configuration space and incrementally samples the obstacle region to isolate free connected components. If the start and goal configurations lie in different connected components of the free space, the plan is infeasible.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is the entire obstacle region not required?",{"text":80,"@type":76},"The approach only needs a relevant subset of the obstacle region that separates start and goal into different free regions. This avoids constructing the full configuration space obstacle structure.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of robots and configuration dimensions are tested?",{"text":84,"@type":76},"The methodology is demonstrated on scenarios with up to 5 degrees of freedom, and scalability is further examined using experimental results on 6-DOF and 7-DOF robots.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]