Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies, particularly in drone swarms. The deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition assessments. In this context, this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring tasks. The primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles, such as buildings. The algorithm incorporates three key components to optimize the obstacle detection, navigation, and energy efficiency within a drone swarm. Firstly, the algorithm utilizes a method for calculating the position of a virtual leader, acting as a navigational beacon to influence the overall direction of the swarm. Secondly, the algorithm identifies observers within the swarm based on the current orientation. To further refine obstacle avoidance, the third component involves the calculation of angular velocity using fuzzy logic. This approach considers the proximity of detected obstacles through operational rangefinders and the target’s location, allowing for a nuanced and adaptable computation of angular velocity. The integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically, ensuring practical obstacle avoidance. The proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive simulations. The results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications. • Introduced an innovative algorithm for navigating drone swarms in urban landscapes. • Utilized a virtual leader for directional influence and enhanced swarm coordination. • Incorporated Fuzzy Logic for dynamic, adaptable obstacle avoidance. • Demonstrated improved obstacle detection and navigation accuracy in simulations. • Optimized energy efficiency and real-time adaptation to diverse urban conditions.