  Developing a safe, stable, and efficient obstacle avoidance policy in crowded
and narrow scenarios for multiple robots is challenging. Most existing studies
either use centralized control or need communication with other robots. In this
paper, we propose a novel logarithmic map-based deep reinforcement learning
method for obstacle avoidance in complex and communication-free multi-robot
scenarios. In particular, our method converts laser information into a
logarithmic map. As a step toward improving training speed and generalization
performance, our policies will be trained in two specially designed multi-robot
scenarios. Compared to other methods, the logarithmic map can represent
obstacles more accurately and improve the success rate of obstacle avoidance.
We finally evaluate our approach under a variety of simulation and real-world
scenarios. The results show that our method provides a more stable and
effective navigation solution for robots in complex multi-robot scenarios and
pedestrian scenarios. Videos are available at https://youtu.be/r0EsUXe6MZE.
