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。