Multi-robot target tracking finds extensive applications in different scenarios, such as environmental surveillance and wildfire management, which require the robustness of the practical deployment of multi-robot systems in uncertain and dangerous environments. Traditional approaches often focus on the performance of tracking accuracy with no modeling and assumption of the environments, neglecting potential environmental hazards which result in system failures in real-world deployments. To address this challenge, we investigate multi-robot target tracking in the adversarial environment considering sensing and communication attacks with uncertainty. We design specific strategies to avoid different danger zones and proposed a multi-agent tracking framework under the perilous environment. We approximate the probabilistic constraints and formulate practical optimization strategies to address computational challenges efficiently. We evaluate the performance of our proposed methods in simulations to demonstrate the ability of robots to adjust their risk-aware behaviors under different levels of environmental uncertainty and risk confidence. The proposed method is further validated via real-world robot experiments where a team of drones successfully track dynamic ground robots while being risk-aware of the sensing and/or communication danger zones.

翻译：多机器人目标追踪在环境监测与野火管理等不同场景中具有广泛应用，这些场景要求多机器人系统在不确定且危险的环境中具备实际部署的鲁棒性。传统方法通常仅关注追踪精度的性能，未对环境进行建模与假设，忽视了潜在的环境危害，导致实际部署中出现系统故障。为应对这一挑战，我们研究了考虑不确定感知与通信攻击的对抗环境下多机器人目标追踪问题。我们设计了特定策略以规避不同危险区域，并提出了一种危险环境下的多智能体追踪框架。我们通过近似概率约束并构建实用的优化策略，以高效应对计算挑战。我们在仿真中评估了所提方法的性能，证明了机器人在不同环境不确定性与风险置信度下调整其风险感知行为的能力。该方法进一步通过真实机器人实验得到验证，实验中无人机编队成功追踪动态地面机器人，同时对感知和/或通信危险区域保持风险感知。