Predicting Navigational Performance of Dynamic Obstacle Avoidance Approaches Using Deep Neural Networks

Over the past decades, countless autonomous navigation and dynamic obstacle avoidance approaches have been proposed by various research works. However, to bridge the gap between research and industries, these approaches are required to be extensively evaluated and benchmarked within various different setting, scenarios, and maps. However, conducting these test runs is tedious and time-consuming. Furthermore, simulation runs and test on real robots can not always cover all potentially occurring scenarios or are inaccurate in certain settings and circumstances especially when a high number of pedestrians or other dynamic entities are involved. In this paper, we propose an approach to predict the navigational performance of navigation approaches for new and unknown maps, scenarios, and robots without the necessity to conduct the actual test runs. Therefore, we acquire a large dataset consisting of thousands of evaluation runs within crowded environments from both simulation and real-world runs, which were conducted using the arena-bench platform of our previous works [1] and trained several neural network architectures to predict relevant navigational performance metrics such as collision rates or path efficiency. We demonstrate the feasibility of our neural networks by predicting the most relevant metrics with up to 95 percent accuracy compared to the groundtruth data acquired by an actual simulation run. Using this approach could prove beneficial for a number of applications and save valuable time and costs in that the performance of new navigation algorithms for crowded environments can be estimated and predicted on new maps, scenarios, and on new robots. We made the code publicly available at https://github.com/ignc-research/navprediction.