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

• URL: http://arxiv.org/abs/2404.19403v1
• Date: Tue, 30 Apr 2024 09:48:11 GMT
• Title: Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making
• Authors: Lei Zhuang, Jingdong Zhao, Yuntao Li, Zichun Xu, Liangliang Zhao, Hong Liu,
• Abstract summary: Transformer-Enhanced Motion Planner (TEMP) is a novel deep learning-based motion planning framework. TEMP synergizes an Environmental Information Semantic (EISE) with a Motion Planning Transformer (MPT)
• Score: 6.867637277944729
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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