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About this paper
Mingao Tan, Shanze Wang, Biao Huang, Zhibo Yang, Rongfei Chen, Xiaoyu Shen, Wei Zhang
This paper presents DRL-NSUO, a robot navigation strategy that enhances safety by incorporating the 'rate of environmental change' into its Deep Reinforcement Learning reward function, guiding robots to proactively maintain a safer observation space. The method achieved a 94.0% success rate and 3.0% collision rate on the BARN dataset, demonstrating improved safety over prior distance-based DRL approaches.
Key insights
- Incorporating the 'rate of environmental change' into DRL reward functions enables robots to prioritize safety by maintaining a broader observation space, especially around blind spots.
- Curriculum learning, where environmental change penalties are gradually increased, allows robots to first acquire basic navigation skills and then refine behavior for safer interactions.
- A local planning approach, guided by environmental dynamics, can lead to superior safety and navigation performance compared to global path planning methods in dynamic, complex environments.
Summary by alphaXiv.
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