Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments
- URL: http://arxiv.org/abs/2603.23649v1
- Date: Tue, 24 Mar 2026 18:49:13 GMT
- Title: Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments
- Abstract summary: We formulate safety constraints using defender-induced engagement zones.<n>A smooth aggregate safety function is constructed using a log-sum-exp operator.<n>A smooth switching guidance strategy is then developed to coordinate interception and safety objectives.
- Score: 0.711144397510333
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We address target interception in contested environments in the presence of multiple defenders whose interception capability is limited by finite ranges. Conventional methods typically impose conservative stand-off constraints based on maximum engagement distance and neglect the interceptors' actuator limitations. Instead, we formulate safety constraints using defender-induced engagement zones. To account for actuator limits, the vehicle model is augmented with input saturation dynamics. A time-varying safe-set tightening parameter is introduced to compensate for transient constraint violations induced by actuator dynamics. To ensure scalable safety enforcement in multi-defender scenarios, a smooth aggregate safety function is constructed using a log-sum-exp operator combining individual threat measures associated with each defender's capability. A smooth switching guidance strategy is then developed to coordinate interception and safety objectives. The attacker pursues the target when sufficiently distant from threat boundaries and progressively activates evasive motion as the EZ boundaries are approached. The resulting controller relies only on relative measurements and does not require knowledge of defender control inputs, thus facilitating a fully distributed and scalable implementation. Rigorous analysis provides sufficient conditions guaranteeing target interception, practical safety with respect to all defender engagement zones, and satisfaction of actuator bounds. An input-constrained guidance law based on conservative stand-off distance is also developed to quantify the conservatism of maximum-range-based safety formulations. Simulations with stationary and maneuvering defenders demonstrate that the proposed formulation yields shorter interception paths and reduced interception time compared with conventional methods while maintaining safety throughout the engagement.
Related papers
- Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control [14.05083688629287]
This paper proposes a hierarchical overtaking framework that separates maneuver-level decision making from safety-certified trajectory control.<n>A reinforcement learning policy adapts the discrete-time CBF decay parameter online, enabling context-dependent modulation of safety margins without directly controlling vehicle inputs.
arXiv Detail & Related papers (2026-07-15T00:19:14Z) - Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation [57.9825059129472]
We propose a planner-agnostic attack framework for tolerance-aware manipulation.<n>Our approach shifts the evaluation paradigm to task-level feasibility over goal regions.<n>Online, we formulate the attack as a budgeted maximum-coverage optimization.
arXiv Detail & Related papers (2026-07-04T08:14:44Z) - Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance [13.82721066487409]
This paper proposes a safe reinforcement learning framework guided by a Hamilton-Jacobi reachability based motion safety set.<n>The proposed method achieves higher goal-reaching rates, produces smoother avoidance maneuvers, and maintains larger unified safety margins than baseline methods.
arXiv Detail & Related papers (2026-06-13T13:57:30Z) - Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints [3.1685575193428845]
We propose an online adaptive impedance control framework that enforces joint-state safety while achieving compliant interaction under uncertain dynamics.<n>The approach combines a quadratic-program-based safety filter with a novel composed position-velocity non-smooth control barrier function.<n>A disturbance-observer-enhanced safety mechanism improves robustness against modelling errors and external interaction forces.
arXiv Detail & Related papers (2026-05-27T12:04:03Z) - GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic [51.85782253444399]
Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving systems.<n>They remain vulnerable to diverse safety threats, particularly in accident-prone scenarios.<n>We propose GuardAD, a model-agnostic safeguard that formulates AD safety as an evolving Markovian logical state.
arXiv Detail & Related papers (2026-05-11T11:28:03Z) - Control Barrier Corridors: From Safety Functions to Safe Sets [7.396792281840145]
Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods.<n>This paper introduces a new notion of control barrier corridors, which unifies these two approaches.<n>We show that individual state safety can be extended locally over control barrier corridors for convex barrier functions.
arXiv Detail & Related papers (2026-03-06T17:23:56Z) - BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
arXiv Detail & Related papers (2026-02-23T18:19:46Z) - RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
arXiv Detail & Related papers (2025-12-24T15:01:26Z) - Boundary-to-Region Supervision for Offline Safe Reinforcement Learning [56.150983204962735]
Boundary-to-Region (B2R) is a framework that enables asymmetric conditioning through cost signal realignment.<n>B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories.<n> Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks.
arXiv Detail & Related papers (2025-09-30T03:38:20Z) - Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments [4.788163807490197]
We propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime.<n>We demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.
arXiv Detail & Related papers (2025-03-11T14:33:55Z) - Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications [73.58451824894568]
The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states.
In safety-critical domains, such behaviors could lead to disastrous outcomes.
We propose Objective Suppression, a novel method that adaptively suppresses the task reward maximizing objectives according to a safety critic.
arXiv Detail & Related papers (2024-02-23T23:22:06Z) - Failing with Grace: Learning Neural Network Controllers that are
Boundedly Unsafe [18.34490939288318]
We consider the problem of learning a feed-forward neural network (NN) controller to safely steer an arbitrarily shaped robot in a compact workspace.
We propose an approach that lifts such assumptions on the data that are hard to satisfy in practice.
We provide a simulation study that verifies the efficacy of the proposed scheme.
arXiv Detail & Related papers (2021-06-22T15:51:52Z) - Risk-Constrained Interactive Safety under Behavior Uncertainty for
Autonomous Driving [0.0]
Safety envelopes which restrict the allowed planning region yield interpretable safety under the presence of behavior uncertainty.
Humans balance safety and efficiency in dense traffic by accepting a probabilistic risk of violating the safety envelope.
arXiv Detail & Related papers (2021-02-05T08:33:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.