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 algorithm maintains robustness against abrupt intention shifts 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 framework 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 270 Hz without requiring training or detailed prior knowledge of target behavior, showcasing its applicability in various robotic systems.

翻译：本研究提出一种基于意图推断的自适应贝叶斯实时轨迹预测算法，适用于目标意图与运动特性未知且可能动态变化的场景。该方法同步估计两个关键变量：将目标当前意图建模为马尔可夫隐状态，以及描述目标遵循最短路径策略程度的意图参数。通过集成这种联合更新技术，算法在面对意图突变和未知运动动力学时保持鲁棒性。基于采样的轨迹预测机制利用这些自适应估计生成具有量化不确定性的概率预测。我们通过数值实验验证该框架：两种场景的消融研究及500次蒙特卡洛分析；在四旋翼与四足机器人平台上的硬件演示。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。该方法以约270赫兹的频率实时运行，无需训练或先验目标行为细节知识，展现了其在各类机器人系统中的适用性。