Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making
arxiv(2024)
摘要
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.
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