Waypoint Planning Networks
This work addresses path planning efficiency and robustness for robotics or autonomous systems, but it is incremental as it builds on existing hybrid approaches.
The paper tackles the challenge of improving path planning success rates by proposing Waypoint Planning Networks (WPN), a hybrid algorithm combining LSTMs with classic methods like A*, which reduces search space and works on partial maps while generating near-optimal results.
With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success rates of classic algorithms. We propose waypoint planning networks (WPN), a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm. WPN produces a more computationally efficient and robust solution. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value iteration networks (VIN). In this paper, the design and experiments have been conducted for 2D environments. Experimental results outline the benefits of WPN, both in efficiency and generalization. It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results. Additionally, WPN works on partial maps, unlike A* which needs the full map in advance. The code is available online.