Fuzzy logic in real-time decision making for autonomous drones

The rapid advancement of drone technology has expanded their applications across various sectors, necessitating robust real-time decision-making systems. Traditional algorithms often falter in dynamic and unpredictable environments. This paper introduces a fuzzy logic-based approach to enhance the decision-making capabilities of autonomous drones. Utilizing Monte Carlo simulations, the proposed model was evaluated through three distinct experiments involving 300, 600, and 950 scenarios respectively. The first experiment demonstrated an obstacle avoidance efficiency of 82.00%, an 8.00% reduction in energy consumption, a decision accuracy of 95.33%, and a mission success rate of 79.33%. The second experiment showed an avoidance efficiency of 82.50%, maintaining the energy consumption reduction at 8.00%, with a decision accuracy of 95.83% and a mission success rate of 78.33%. The third experiment achieved an avoidance efficiency of 82.11%, with an 8.00% reduction in energy consumption, a decision accuracy of 95.26%, and a mission success rate of 78.31%. These results highlight the superior performance of fuzzy logic in real-time decision-making for autonomous drones compared to traditional methods.