In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for
motion planning under motion and sensing uncertainties. The original stochastic
motion planning problem is divided into a deterministic motion planning problem
and a graph search problem. We solve the deterministic planning problem using
sampling-based methods such as PRM or RRG to construct a graph of nominal
trajectories. Then, an informed cost-to-go heuristic for the original problem
is computed based on the nominal trajectory graph. Finally, we grow a belief
tree by searching over the graph using the proposed heuristic. IBBT interleaves
between batch state sampling, nominal trajectory graph construction, heuristic
computing, and search over the graph to find belief space motion plans. IBBT is
an anytime, incremental algorithm. With an increasing number of batches of
samples added to the graph, the algorithm finds motion plans that converge to
the optimal one. IBBT is efficient by reusing results between sequential
iterations. The belief tree searching is an ordered search guided by an
informed heuristic. We test IBBT in different planning environments. Our
numerical investigation confirms that IBBT finds non-trivial motion plans and
is faster compared with previous similar methods.

本文提出一种名为 IBBT 算法的运动规划算法，该算法利用启发式算法，将随机运动规划问题划分为确定性运动规划问题和图搜索问题，并交替进行批量状态采样、路径规划和启发式搜索，以找到满足信念空间运动规划的最优解。实验表明，IBBT 算法在不同规划环境中具有更快的运行速度和更好的性能。

基于信息批次置信树的不确定性运动规划

IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty

Integrated sensing and communication (ISAC) represents a paradigm shift,
where previously competing wireless transmissions are jointly designed to
operate in harmony via the shared use of the hardware platform for improving
the spectral and energy efficiencies. However, due to adversarial factors such
as fading and interference, ISAC may suffer from high sensing uncertainties.
This paper presents a multi-point ISAC (MPISAC) system that fuses the outputs
from multiple ISAC devices for achieving higher sensing performance by
exploiting multi-view data redundancy. Furthermore, we propose to effectively
explore the performance trade-off between sensing and communication via a
functionality selection module that adaptively determines the working state
(i.e., sensing or communication) of an ISAC device. The crux of our approach is
to derive a fusion model that predicts the fusion accuracy via hypothesis
testing and optimal voting analysis. Simulation results demonstrate the
superiority of MPISAC over various benchmark schemes and show that the proposed
approach can effectively span the trade-off region in ISAC systems.

提出了一种多点 ISAC 系统，通过利用多视数据冗余来融合多个 ISAC 设备的输出，从而实现更高的感知性能，并通过功能选择模块来有效地探索感知和通信之间的性能权衡，并导出了一个通过假设检验和最优投票分析预测融合准确度的融合模型。仿真结果表明，MPISAC 优于各种基准方案，并且该方法可以有效地跨越 ISAC 系统的权衡区域。