[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86529-en":3,"doc-seo-86529-105":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},86529,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Capture, Shield, or Neutralize: Engagement-Aware Pursuit-Evasion","Hierarchical control architecture is presented for multi-agent adversarial settings, separating strategic mission planning from rigorous safety assurance. Pursuit–evasion is posed as a zero-sum receding-horizon game and solved using iterative minimax model predictive control. Pursuers anticipate and block evader trajectories using transverse velocity penalties instead of reactive heuristic formations. A discrete-time control barrier function provides an inner-loop collision-free safety filter while preserving convex MPC structure. Simulations show adaptability: adjusting shared game-payoff weights and barrier constraints enables switching between aggressive pursuit, perimeter defense, and area denial without altering core control logic. ","Capture, Shield, or Neutralize: Engagement-Aware Pursuit-Evasion  \nAnanya Acharya RIT, Rochester, NY  \n[aa2334@rit.edu](aa2334@rit.edu)  \nAdrian Stoica RIT, Rochester, NY  \n[a.stoica@ieee.org](a.stoica@ieee.org)  \nTrenton Goyette RIT, Rochester, NY [twg8622@rit.edu](twg8622@rit.edu)  \nVikas Dhiman  \nUniversity of Maine, Orono, ME[vikas.dhiman@maine.edu](vikas.dhiman@maine.edu)  \nMasoud Ataei University of Maine, Orono, ME  \n[masoud.ataei@maine.edu](masoud.ataei@maine.edu)  \nMohammad Javad Khojasteh RIT, Rochester, NY  \n[mjkeme@rit.edu](mjkeme@rit.edu)  \narXiv :2607 . 10986v1 [ ee ss . SY] 13 Jul 2026  \nAbstract—This paper introduces a hierarchical control architecture for multi-agent adversarial environments, decoupling strategic task planning from rigorous safety assurance. The system formulates pursuit-evasion as a zero-sum receding-horizon game, solved via an iterative minimax model predictive control scheme. This allows pursuers to anticipate and block evader trajectories using transverse velocity penalties rather than relying on reactive heuristic formations. To guarantee collision-free operation without compromising the convexity of the model predictive control, a discrete-time control barrier function operates asan inner-loop safety filter. Through simulated experiments, we demonstrate the framework’s adaptability. By simply altering the weights of the shared zero-sum payoff and control barrier function constraints, the swarm can fluidly switch from aggressive pursuit-evasion tactics to strict perimeter defense and area denial, demonstrating robust performance across varying rules of engagement without structural changes to the control logic. The source code is available1.  \nI. INTRODUCTION  \nPursuit-evasion problems provide a general framework for mathematically formalizing a wide range of applications, including surveillance, navigation, analysis of biological behaviors, and conflict operations. In its simplest form, a pursuitevasion scenario involves two players or autonomous agents competing against one another. More general formulations consider multiple agents organized into two opposing teams: pursuers and evaders. The primary objective is to develop strategies that enable an autonomous agent to act effectively against its opponent [1]–[4] .  \nWe formulate the pursuit-evasion problem as a finite-horizon dynamic game between a team of pursuers and an evader. Let the joint system state be x(t) ∈ Rn , evolving according to the dynamics  \nx˙(t) = f(x(t), u (t), v (t), t), x (t0 ) = x0  \nwhere u (t) ∈ U and v (t) ∈ V represent the admissible control policies of the pursuer team and evader, respectively. The pursuit and evasion objectives are encoded through finitehorizon cost functionals. In the zero-sum case, the interaction can be written abstractly as  \nV (x, t) = min max J(x, u, v)  \nu∈U v∈V  \nThis work is supported by the Gleason Endowment, and the Provost’s Learning Innovation Grant at RIT, and the National Science Foundation under Grant No. 2218063.  \n1[https://github.com/ananya-ac/pursuit-evasion-mpc-cbf](https://github.com/ananya-ac/pursuit-evasion-mpc-cbf)  \nFig. 1: Engagement-Aware Pursuit-Evasion  \nwhere the pursuer minimizes the game payoff and the evader maximizes it. This represents a general framework for adversarial interactions. However, directly solving for a global equilibrium in continuous time is often intractable for multi-agent systems. As highlighted by various optimal control approaches in the literature, it is frequently practical to relax this full game formulation. For instance, when the primary objective is strictly capture2 , the formulation may be reduced to a onesided optimal control problem where the evader’s policy is assumed to be random or heuristic-based. Alternatively, the game can be approximated using a receding-horizon optimization scheme in which the pursuers and evader iteratively solve finite-horizon optimal control problems and apply only the first control input. This flex","cbCaieuzfQljmFSX","https://ap.wps.com/l/cbCaieuzfQljmFSX","pdf",1540246,4,1,9,"English","en",105,"# Abstract—\n# I. Introduction","[{\"question\":\"What is the main contribution of the proposed framework for multi-agent pursuit-evasion?\",\"answer\":\"It introduces an engagement-aware hierarchical control architecture that decouples high-level strategic planning from an inner-loop safety and engagement enforcement mechanism.\"},{\"question\":\"How is the pursuit-evasion problem formulated and solved?\",\"answer\":\"It is formulated as a zero-sum receding-horizon game between pursuers and an evader, then solved via an iterative minimax model predictive control scheme.\"},{\"question\":\"How does the system guarantee collision-free operation without sacrificing MPC convexity?\",\"answer\":\"A discrete-time control barrier function acts as an inner-loop safety filter, maintaining collision-free behavior while preserving the convex structure of the MPC.\"}]",1784212420,23,{"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},"capture-shield-or-neutralize-engagement-aware-pursuit-evasion","",{"@graph":36,"@context":85},[37,53,68],{"@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":20},"https://docshare.wps.com/document/capture-shield-or-neutralize-engagement-aware-pursuit-evasion/86529/",{"url":52,"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","2026-07-27","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},"What is the main contribution of the proposed framework for multi-agent pursuit-evasion?","Question",{"text":75,"@type":76},"It introduces an engagement-aware hierarchical control architecture that decouples high-level strategic planning from an inner-loop safety and engagement enforcement mechanism.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the pursuit-evasion problem formulated and solved?",{"text":80,"@type":76},"It is formulated as a zero-sum receding-horizon game between pursuers and an evader, then solved via an iterative minimax model predictive control scheme.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the system guarantee collision-free operation without sacrificing MPC convexity?",{"text":84,"@type":76},"A discrete-time control barrier function acts as an inner-loop safety filter, maintaining collision-free behavior while preserving the convex structure of the MPC.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"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":22,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]