提出动态环境下的联合估计预测规划框架，避免错误信息回传导致碰撞。
DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments
- 用有向因子约束信息流向，防止规划预测污染状态估计。
- 无有向因子时机器人在多数实验中发生碰撞，验证其关键作用。
- 支持协同物体行为建模，提升复杂场景下路径规划安全性。
DynoJEPP 是一种基于因子图的框架，将动态环境中的状态估计、预测与规划联合建模并同步优化。传统因子图方法中，预测与规划的信息会反馈至状态估计，导致估计失真、行为异常和计划不安全。为解决此问题，DynoJEPP 引入新型有向因子，强制因子图内的信息单向流动，阻止预测与规划对状态估计造成干扰。我们在静态与动态环境中评估有向因子对模块间交互的影响，结果表明其对安全运行至关重要：未使用有向因子时，机器人在多数实验中发生碰撞。在此基础上，我们进一步提出 Cooperative DynoJEPP，使本体机器人能够将协同物体的行为纳入预测与轨迹规划中。
原文摘要 · Abstract (English)
DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environments. In conventional factor-graph-based approaches that jointly formulate estimation, prediction, and planning, information from prediction and planning feeds back into state estimation, yielding corrupted estimates, undesired behaviors, and unsafe plans. To address this, DynoJEPP introduces a novel directed factor that enforces directional information flow within the factor graph, preventing prediction and planning from corrupting state estimation. We evaluate the impact of directed factors on inter-module interactions during navigation in both static and dynamic environments. Our results demonstrate that these factors are critical for safe operation, as without them, the robot collides in the majority of experiments. Building on this, we further introduce Cooperative DynoJEPP, which enables the ego robot to incorporate cooperative object behavior into its prediction and trajectory planning.
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