基于贝叶斯推断实现未知目标意图与运动的实时轨迹预测
Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
- 通过联合估计意图状态与最短路径遵从度参数，实现动态意图推断
- 在500次蒙特卡洛实验中显著优于非自适应方法，实时性达547 Hz
- 适用于无人机和四足机器人等复杂系统，无需先验知识
本文提出一种自适应贝叶斯算法，用于在目标意图与运动特性未知且可能突变的情况下进行实时轨迹预测。该方法同时估计两个关键变量：将目标当前意图建模为马尔可夫隐状态，以及描述其对最短路径策略遵循程度的意图参数。通过融合这一联合更新机制，算法在应对轨迹中突发意图变化和未知运动动力学时表现出强鲁棒性。随后采用基于采样的轨迹预测机制，利用这些自适应估计生成带有量化不确定性的概率预测。通过数值实验验证：包括两种情形的消融研究及500次蒙特卡洛分析；并在四旋翼与四足平台进行硬件演示。实验结果表明，所提方法显著优于非自适应与部分自适应方法，在不需训练或目标行为详细先验知识的前提下，以约547 Hz的实时性能运行，展现出在多种机器人系统中的应用潜力。
原文摘要 · Abstract (English)
This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown and subject to change. The method concurrently estimates two critical variables: the target's current intention, modeled as a Markovian latent state, and an intention parameter that describes the target's adherence to a shortest-path policy. By integrating this joint update technique, the proposed algorithm maintains robustness against abrupt intention shifts in trajectory prediction and unknown motion dynamics. A sampling-based trajectory prediction mechanism then exploits these adaptive estimates to generate probabilistic forecasts with quantified uncertainty. We validate the algorithm through numerical experiments: Ablation studies of two cases, and a 500-trial Monte Carlo analysis; Hardware demonstrations on quadrotor and quadrupedal platforms. Experimental results demonstrate that the proposed approach significantly outperforms non-adaptive and partially adaptive methods. The method operates in real time around 547 Hz without requiring training or detailed prior knowledge of target behavior, showcasing its applicability in various robotic systems.
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