Learning-based Fast Path Planning in Complex Environments
This addresses the challenge of fast and reliable path planning for robots in complex environments, representing an incremental improvement over existing methods.
The paper tackles the problem of slow or failed path planning in complex environments by proposing a learning-based framework that combines a CNN prediction module with a sampling-based planner, achieving a processing speed of 60 FPS and outperforming conventional methods in planning time, success rate, and path length.
In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly find a feasible path in complex environments or even fail. However, our proposed framework can overcome this difficulty by using a learning-based prediction module and a sampling-based path planning module. The prediction module utilizes an auto-encoder-decoder-like convolutional neural network (CNN) to output a promising region where the feasible path probably lies in. In this process, the environment is treated as an RGB image to feed in our designed CNN module, and the output is also an RGB image. No extra computation is required so that we can maintain a high processing speed of 60 frames-per-second (FPS). Incorporated with a sampling-based path planner, we can extract a feasible path from the output image so that the robot can track it from start to goal. To demonstrate the advantage of the proposed algorithm, we compare it with conventional path planning algorithms in a series of simulation experiments. The results reveal that the proposed algorithm can achieve much better performance in terms of planning time, success rate, and path length.