Sampling-based motion planning (SBMP) algorithms are renowned for their
robust global search capabilities. However, the inherent randomness in their
sampling mechanisms often result in inconsistent path quality and limited
search efficiency. In response to these challenges, this work proposes a novel
deep learning-based motion planning framework, named Transformer-Enhanced
Motion Planner (TEMP), which synergizes an Environmental Information Semantic
Encoder (EISE) with a Motion Planning Transformer (MPT). EISE converts
environmental data into semantic environmental information (SEI), providing MPT
with an enriched environmental comprehension. MPT leverages an attention
mechanism to dynamically recalibrate its focus on SEI, task objectives, and
historical planning data, refining the sampling node generation. To demonstrate
the capabilities of TEMP, we train our model using a dataset comprised of
planning results produced by the RRT*. EISE and MPT are collaboratively
trained, enabling EISE to autonomously learn and extract patterns from
environmental data, thereby forming semantic representations that MPT could
more effectively interpret and utilize for motion planning. Subsequently, we
conducted a systematic evaluation of TEMP's efficacy across diverse task
dimensions, which demonstrates that TEMP achieves exceptional performance
metrics and a heightened degree of generalizability compared to
state-of-the-art SBMPs.

提出了一种名为 Transformer-Enhanced Motion Planner（TEMP）的新型深度学习运动规划框架，它与 Environmental Information Semantic Encoder（EISE）和 Motion Planning Transformer（MPT）相结合，通过将环境数据转化为语义环境信息（SEI），提供更丰富的环境理解，进而利用注意机制动态重新校准其对 SEI、任务目标和历史规划数据的关注，从而改进样本节点生成，该框架的有效性经过系统评估表明，在各种任务维度上表现出卓越的性能指标和更高程度的泛化能力，相较于现有的先进的基于采样的运动规划算法（SBMPs）具有显著优势。

Transformer 增强式运动规划器：基于注意力引导的采样用于特定状态决策

Transformer-Enhanced Motion Planner: Attention-Guided Sampling for  State-Specific Decision Making

Path planning plays a crucial role in various autonomy applications, and RRT*
is one of the leading solutions in this field. In this paper, we propose the
utilization of vertex-based networks to enhance the sampling process of RRT*,
leading to more efficient path planning. Our approach focuses on critical
vertices along the optimal paths, which provide essential yet sparser
abstractions of the paths. We employ focal loss to address the associated data
imbalance issue, and explore different masking configurations to determine
practical tradeoffs in system performance. Through experiments conducted on
randomly generated floor maps, our solutions demonstrate significant speed
improvements, achieving over a 400% enhancement compared to the baseline model.

本论文提出了一种基于顶点网络的路径规划方法，该方法利用关键顶点来优化 RRT * 采样过程，提高了路径规划效率，实验结果表明相比基准模型提高了 400% 以上。

基于顶点的网络加速路径规划算法

Vertex-based Networks to Accelerate Path Planning Algorithms

This work presents an approach to learn path planning for robot social
navigation by demonstration. We make use of Fully Convolutional Neural Networks
(FCNs) to learn from expert's path demonstrations a map that marks a feasible
path to the goal as a classification problem. The use of FCNs allows us to
overcome the problem of manually designing/identifying the cost-map and
relevant features for the task of robot navigation. The method makes use of
optimal Rapidly-exploring Random Tree planner (RRT*) to overcome eventual
errors in the path prediction; the FCNs prediction is used as cost-map and also
to partially bias the sampling of the configuration space, leading the planner
to behave similarly to the learned expert behavior. The approach is evaluated
in experiments with real trajectories and compared with Inverse Reinforcement
Learning algorithms that use RRT* as underlying planner.

论文介绍了一种通过演示学习路径规划的方法，运用了全卷积神经网络来学习专家路径演示的地图，并使用 RRT* 算法来优化路径预测，本方法已在真实路径中进行试验并与逆强化学习算法进行了对比。

利用全卷积网络学习人意识路径规划

Learning Human-Aware Path Planning with Fully Convolutional Networks

During the last decade, sampling-based path planning algorithms, such as
Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have
been shown to work well in practice and possess theoretical guarantees such as
probabilistic completeness. However, little effort has been devoted to the
formal analysis of the quality of the solution returned by such algorithms,
e.g., as a function of the number of samples. The purpose of this paper is to
fill this gap, by rigorously analyzing the asymptotic behavior of the cost of
the solution returned by stochastic sampling-based algorithms as the number of
samples increases. A number of negative results are provided, characterizing
existing algorithms, e.g., showing that, under mild technical conditions, the
cost of the solution returned by broadly used sampling-based algorithms
converges almost surely to a non-optimal value. The main contribution of the
paper is the introduction of new algorithms, namely, PRM* and RRT*, which are
provably asymptotically optimal, i.e., such that the cost of the returned
solution converges almost surely to the optimum. Moreover, it is shown that the
computational complexity of the new algorithms is within a constant factor of
that of their probabilistically complete (but not asymptotically optimal)
counterparts. The analysis in this paper hinges on novel connections between
stochastic sampling-based path planning algorithms and the theory of random
geometric graphs.

本文介绍了一种新的算法 PRM * 和 RRT*，证明了它们是渐近最优的，并且在样本数增加的情况下，它们的解决方案成本几乎肯定收敛于最优值。这些算法的计算复杂度与传统的，但不是渐近最优的算法相当。