AI Algorithm for Predicting and Optimizing Trajectory of UAV Swarm

TL;DR

This paper introduces a novel AI-based approach for UAV swarm trajectory prediction and collision avoidance, utilizing a new activation function and an integrated strategy to improve accuracy and efficiency.

Contribution

The paper proposes a new activation function, AdaptoSwelliGauss, and an integrated collision avoidance strategy, enhancing UAV trajectory prediction and collision management capabilities.

Findings

The new activation function outperforms existing functions in accuracy.

The integrated collision strategy reduces trajectory manipulations and takeoff time.

Enhanced robustness against noise in UAV trajectory prediction.

Abstract

This paper explores the application of Artificial Intelligence (AI) techniques for generating the trajectories of fleets of Unmanned Aerial Vehicles (UAVs). The two main challenges addressed include accurately predicting the paths of UAVs and efficiently avoiding collisions between them. Firstly, the paper systematically applies a diverse set of activation functions to a Feedforward Neural Network (FFNN) with a single hidden layer, which enhances the accuracy of the predicted path compared to previous work. Secondly, we introduce a novel activation function, AdaptoSwelliGauss, which is a sophisticated fusion of Swish and Elliott activations, seamlessly integrated with a scaled and shifted Gaussian component. Swish facilitates smooth transitions, Elliott captures abrupt trajectory changes, and the scaled and shifted Gaussian enhances robustness against noise. This dynamic combination is specifically designed to excel in capturing the complexities of UAV trajectory prediction. This new activation function gives substantially better accuracy than all existing activation functions. Thirdly, we propose a novel Integrated Collision Detection, Avoidance, and Batching (ICDAB) strategy that merges two complementary UAV collision avoidance techniques: changing UAV trajectories and altering their starting times, also referred to as batching. This integration helps overcome the disadvantages of both - reduction in the number of trajectory manipulations, which avoids overly convoluted paths in the first technique, and smaller batch sizes, which reduce overall takeoff time in the second.