Target tracking with a mobile robot has numerous significant applications in both civilian and military. Practical challenges such as limited field-of-view, obstacle occlusion, and system uncertainty may all adversely affect tracking performance, yet few existing works can simultaneously tackle these limitations. To bridge the gap, we introduce the concept of belief-space probability of detection (BPOD) to measure the predictive visibility of the target under stochastic robot and target states. An Extended Kalman Filter variant incorporating BPOD is developed to predict target belief state under uncertain visibility within the planning horizon. Furthermore, we propose a computationally efficient algorithm to uniformly calculate both BPOD and the chance-constrained collision risk by utilizing linearized signed distance function (SDF), and then design a two-stage strategy for lightweight calculation of SDF in sequential convex programming. Building upon these treatments, we develop a real-time, non-myopic trajectory planner for visibility-aware and safe target tracking in the presence of system uncertainty. The effectiveness of the proposed approach is verified by both simulations and real-world experiments.

翻译：利用移动机器人进行目标跟踪在民用和军事领域具有众多重要应用。实际挑战，如有限的视野、障碍遮挡及系统不确定性，均可能对跟踪性能产生不利影响，然而现有研究鲜能同时应对这些局限性。为填补这一空白，我们引入信念空间检测概率（BPOD）概念，用于在随机机器人及目标状态下度量目标的预测可见性。开发了一种融合BPOD的扩展卡尔曼滤波变体，以在规划时域内预测不确定可见性条件下的目标信念状态。此外，我们提出一种高效算法，利用线性化有符号距离函数（SDF）统一计算BPOD与机会约束碰撞风险，并设计了两阶段策略以实现序列凸规划中SDF的轻量级计算。基于上述处理，我们构建了一种实时非短视轨迹规划器，用于在系统不确定性存在时实现可见性感知且安全的目标跟踪。通过仿真与真实世界实验验证了所提方法的有效性。