Abstract
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.
Abstract (translated)
多机器人目标跟踪在不同的场景中具有广泛的应用，如环境监视和野火管理，这些场景需要多机器人系统在不确定和危险的环境中的实际部署的稳健性。传统的解决方案通常关注跟踪准确性的性能，没有对环境进行建模和假设，忽视了在现实部署中导致系统故障的潜在环境危险。为了应对这个挑战，我们研究在不确定环境中进行多机器人目标跟踪，考虑了感知和通信攻击的不确定性。我们设计了一些特定的策略来避开不同的危险区域，并针对危险环境提出了一种多代理跟踪框架。我们近似概率约束并提出了有效的优化策略来应对计算挑战。我们在仿真中评估了我们提出方法的性能，以证明机器人在不同环境不确定性和风险信心水平下调整其风险意识行为的能力。所提出的方法通过现实世界的机器人实验进一步验证，一支无人机团队在考虑感知和/或通信危险区域的情况下成功跟踪了动态地面机器人。
URL
https://arxiv.org/abs/2404.07880
