Waypoint Planning Networks

Abstract

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 1 .

Autonomous aerial robotics for package delivery: A technical review

“…On the other hand, sampling-based methods are fast but suboptimal. Machine learning has been utilized in graph-based searches to provide quick and optimal approaches to local planning to reduce computational time (Yonetani et al, 2021) and search space (Qureshi et al, 2019;Toma et al, 2021). Furthermore, value iteration networks provide differentiable path planning modules which can learn to plan (Lee et al, 2018;Tamar et al, 2017).…”

An Optimization-based Motion Planning Method for Autonomous Driving Vehicle

“…In addition, many existing methods are only constrained to simple 2D environments (Yonetani et al, 2021;Chaplot et al, 2021;Toma et al, 2021;Chang et al, 2023;Carvalho et al, 2022). In contrast, we present a learning-based potential motion planning approach, requiring no ground truth knowledge of the optimized cost objective, which we illustrate can effectively generalize to new environments through composability of potentials.…”