[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83562-en":3,"doc-seo-83562-105":30,"detail-sidebar-cat-0-en-105":92},{"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},83562,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration","Collaborative capture of dynamic targets is studied for robotic teams operating in complex, obstacle-dense environments. The approach leverages an ambush-inspired strategy to enable multiple slower pursuers to efficiently capture a faster evader with varying intelligence. A parameterized ambush framework incorporates workspace topology, truncated line-of-sight visibility, speed ratio, and limited capture range. Hybrid Monte Carlo Tree Search optimizes these parameters via long-term planning, while offline-trained neural acceleration replaces rollout scoring to speed online planning without sacrificing success quality. Extensive simulations and hardware experiments validate robustness against capable adversaries.","AMBUSH: Collaborative Capture in Complex Environments  \nwith Neural Acceleration  \nJunfeng Chen 1 ,‹, YinHang Luo 1 ,‹, Xinyi Wang2 , Junrui Li 1 , and Meng Guo 1  \narXiv :2607 .0 1029v 1 [ cs .RO] 1 Jul 2026  \nAbstract—Collaborative capture of dynamic targets is common in nature as an essential strategy for weaker species against the strong. Similar concepts have shown to be useful for numerous robotic applications, such as security and surveillance, search and rescue. However, most existing works focus on analytical and geometric solutions or end-to-end reinforcement learning methods, which are largely constrained to obstacle-free environments or scenarios with sparse, regularly distributed obstacles. This work tackles the problem from a unique perspective: the renowned strategy of “ambush” alone would suffice for multiple slower pursuers to capture one faster evader with different levels of intelligence efficiently in complex environments. A parameterized strategy of ambush (including discrete and continuous parameters) is designed first, which takes into account the topological properties of the workspace, the truncated line-of-sight visibility, the relative speed ratio and the limited capture range. Then, a Hybrid Monte Carlo Tree Search (H-MCTS) algorithm is proposed to optimize the associated parameters through longterm planning, enabling the identification of highly promising parameters for future capture. Lastly, the neural acceleration is trained offline to learn the ranking of different choices of parameters across various environments, and to directly predict scores, replacing the rollout process in H-MCTS. The neural acceleration is adopted during online H-MCTS to accelerate the planning procedure while guaranteeing the planning quality. Its efficiency and effectiveness are validated in extensive simulationsand hardware experiments, against evaders with different capabilities and intelligence levels, including two-times higher velocity and human-controlled behavior.  \nNote to Practitioners—This work is motivated by the practical challenges of enabling robotic teams to reliably capture an agile evader in complex environments, which are critical for security patrols, intruder interception, and search-and-rescue operations. Existing analytical methods often fail in obstacledense settings, while learning-based approaches require extensive environment-specific retraining. We demonstrate that the ambush strategy enables slower and fewer pursuers to capture an evader moving at two times higher speeds, including human-controlled adversaries exploiting environmental complexity. Our solution combines a parameterized ambush framework adapting to the environmental topology and visibility constraints with an HMCTS planner enhanced by offline-learned heuristics. This reduces the computation latency while maintaining high success rates. Through extensive simulations and hardware experiments, we demonstrate the practical viability and robustness of our approach across diverse real-world scenarios. Practitioners can directly deploy this framework for the perimeter security in urban environments, wildlife protection against poachers in dense terrain, or unauthorized drone interception in cluttered airspace without environment-specific adaptation. The current framework uses centralized planning, and extending it to fully decentralized execution is an important direction for distributed robotic teams.  \nIndex Terms—Dynamic capture, Ambush strategy, Hybrid optimization, Learned heuristics.  \nThe authors are with 1the School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871, China; and 2the Distributed Autonomous Systems and Control Lab, University of Michigan, Ann Arbor 48109-1079, [USA.](USA. meng.guo@pku.edu.cn)[ meng.guo@pku.edu.cn](USA. meng.guo@pku.edu.cn)  \nFig. 1: Top: Illustration of the proposed ambush strategy. The pursuer (in purple) hides in concealed positions and makes a surprise attack on the eva","cbCail9mmvFJP8ZE","https://ap.wps.com/l/cbCail9mmvFJP8ZE","pdf",9875422,5,1,20,"English","en",105,"# Introduction\n## Dynamic and collaborative capture\n## Related work in pursuit-evasion\n## Limitations in complex environments\n# Ambush framework overview\n## Parameterized ambush strategy\n## Hybrid planning and neural acceleration","[{\"question\":\"What problem does this work address?\",\"answer\":\"It targets collaborative capture of a dynamically evading target in obstacle-dense, complex environments where many existing methods lose guarantees or require heavy retraining.\"},{\"question\":\"How does the proposed ambush strategy improve multi-agent capture?\",\"answer\":\"It uses a parameterized ambush concept that accounts for workspace topology, truncated visibility, relative speed ratio, and capture range to guide pursuers’ coordinated actions against a faster evader.\"},{\"question\":\"What role does neural acceleration play in planning?\",\"answer\":\"Offline-trained neural acceleration learns to rank parameter choices and predict scores, replacing the rollout process in H-MCTS to accelerate online planning while maintaining planning quality and high success rates.\"}]",1784188852,50,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"ambush-collaborative-capture-in-complex-environments-with-neural-acceleration","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ambush-collaborative-capture-in-complex-environments-with-neural-acceleration/83562/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What 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