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BTIT* combines an anytime bidirectional heuristic search (Bi-HS) with meet-in-the-middle optimality goals by ensuring the MMP and MMoptimality. It is the first anytime MEET-style approach to use termination conditions that are efficient to evaluate, enabling early on-the-fly termination in batch-wise sampling. Experiments show faster time-to-first-solution and improved convergence on a 4D double-integrator and a 10D linearized quadrotor.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/optimal-kinodynamic-motion-planning-through-anytime-bidirectional-heuristic-search-with-tight-termination-condition/202953/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/optimal-kinodynamic-motion-planning-through-anytime-bidirectional-heuristic-search-with-tight-termination-condition/202953.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is BTIT* and what problem does it address?","Question",{"text":112,"@type":113},"BTIT* is an asymptotically optimal kinodynamic sampling-based motion planning algorithm. It targets efficient anytime bidirectional planning with provable meet-in-the-middle optimality while reducing termination-check overhead.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does BTIT* combine anytime bidirectional heuristic search with optimality guarantees?",{"text":117,"@type":113},"BTIT* integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the meet-in-the-middle property (MMP) and MMoptimality. It uses termination conditions designed to be efficient to evaluate during batch-wise sampling.",{"name":119,"@type":110,"acceptedAnswer":120},"What do experiments show about BTIT* performance?",{"text":121,"@type":113},"Experiments indicate that BTIT* achieves a strongly faster time-to-first-solution and improved convergence compared with representative non-lazy informed batch planners. Results are reported on a 4D double-integrator model and a 10D linearized quadrotor benchmark.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},202953,1788552704,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition  \nYi Wang 1 , Bingxian Mu2∗, Shahab Shokouhi 1 , and May-Win Thein 1  \narXiv :2604 . 11587v1 [ cs .RO] 13 Apr 2026  \nAbstract—This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the meet-in-the-middle property (MMP) and optimality (MMoptimality). BTIT* is the first anytime MEET-style algorithm to utilize termination conditions that are efficient to evaluate and enable early termination on-the-fly in batch-wise samplingbased motion planning. Experiments show that BTIT* achieves strongly faster time-to-first-solution and improved convergence than representative non-lazy informed batch planners on two kinodynamic benchmarks: a 4D double-integrator model anda 10D linearized Quadrotor. The source code is available here.  \nI. INTRODUCTION  \nMotion planning is a fundamental problem in roboticsand robot navigation. Given a robot and an environment with a set of obstacles, the primary target is to discover a collision-free pat from a specified start to a designated goal in the robot’s configuration space [1] . A common class of approaches are sampling-based motion-planning (SBMP) algorithms [2]–[5], which are particularly effective in highdimensional continuous configuration spaces. Broadly, these methods interleave two steps: (S1) they approximate the continuous configuration space with a discrete roadmap, an embedded graph whose vertices are sampled configurationsand whose edges correspond to collision, checked local connections and (S2) they apply a graph-search algorithm (e.g., [6], [7]) on this roadmap to find a feasible (and often low-cost) path between the start and goal configurations.  \nThe selection of graph-search algorithm is often determined by the structural and validity guarantees provided by the roadmap. For example, if each vertex and edge of the road map have been certified collision-free, then unidirectional heuristic search (Uni-HS) methods, most notably A* [7], become a natural and effective choice. In this setting, a heuristic can incorporate domain knowledge through estimating the remaining cost-to-go to the goal, allowing the planner to prioritize promising regions of the graph and substantially reduce unnecessary exploration. Representative alternatives include anytime heuristic planners [8], [9] for time-limited planning, incremental heuristic planners [10],[11] to efficiently address dynamic changes, anytime incremental heuristic planners [12], [13] that tackle both limited  \n1 Yi Wang, Shahab Shokouhi, and May-Win Thein are with the Department of Mechanical Engineering, University of New Hampshire, Durham, NH, 03824, [USA. Email:](USA. Email:{yw1055@usnh.edu})[{](USA. Email:{yw1055@usnh.edu})[yw1055@usnh.edu](USA. Email:{yw1055@usnh.edu})[}](USA. Email:{yw1055@usnh.edu}) ,{shahab.shokouhi, [may-win.thein](may-win.thein}@unh.edu)[}](may-win.thein}@unh.edu)[@unh.edu](may-win.thein}@unh.edu)[ ](may-win.thein}@unh.edu)2∗Bingxian Mu is with the Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada  \n*Corresponding author. Email: [bmu@upei.ca](bmu@upei.ca).  \nplanning time and dynamic settings, and online user-guided planners [14] for high-dimensional spaces.  \nAnother family of graph-search algorithms, bidirectional search, is to compute an optimal solution by conducting two separate searches simultaneously from both the start and the goal [15] . Its key strength lies in the potential to exponentially reduce the number of expanded states, as each search only proceeds to half the depth of the path solution [16] . This theoretical advantage naturally makes bidirectional heuristic search (Bi-HS) an appealing algorithmic approach. However, effectively coordinating the meeting o","cbCairc3c9jkglxt","https://ap.wps.com/l/cbCairc3c9jkglxt","pdf",432297,"English","# Introduction\n## Motivation and roadmap-based sampling-based motion planning\n## Unidirectional heuristic search and anytime/incremental variants\n## Bidirectional heuristic search, MM-optimality, and termination overhead\n## Sampling-based planning and discretization challenges\n## Aim of BTIT* and experimental validation","[{\"question\":\"What is BTIT* and what problem does it address?\",\"answer\":\"BTIT* is an asymptotically optimal kinodynamic sampling-based motion planning algorithm. It targets efficient anytime bidirectional planning with provable meet-in-the-middle optimality while reducing termination-check overhead.\"},{\"question\":\"How does BTIT* combine anytime bidirectional heuristic search with optimality guarantees?\",\"answer\":\"BTIT* integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the meet-in-the-middle property (MMP) and MMoptimality. It uses termination conditions designed to be efficient to evaluate during batch-wise sampling.\"},{\"question\":\"What do experiments show about BTIT* performance?\",\"answer\":\"Experiments indicate that BTIT* achieves a strongly faster time-to-first-solution and improved convergence compared with representative non-lazy informed batch planners. Results are reported on a 4D double-integrator model and a 10D linearized quadrotor benchmark.\"}]","Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition | PDF",15]