A real-time fuzzy interacting multiple-model velocity obstacle avoidance approach for unmanned aerial vehicles

This paper presents a new fuzzy interacting multiple-model velocity obstacle (FIMVO) approach for collision avoidance of unmanned aerial vehicles (UAVs). The proposed approach adopts in one framework the advantages of geometric collision avoidance approaches, namely of the velocity (VO), reciprocal velocity (RVO), and hybrid reciprocal velocity obstacle (HRVO) avoidance approaches combined with fuzzy logic. This leads to a combined decision-making rule, with real-time efficiency. The developed approach is compared with geometric conventional velocity obstacle avoidance approaches: VO, RVO, and HRVO avoidance approaches. The proposed approach is carefully evaluated and validated in a simulation environment and over real UAVs. The case study includes three mini UAVs and a human teleoperator who can control only one of them. The other UAVs used the computer-based teleoperator with the proposed and compared approaches. The performance criteria have been defined in four parts: trajectory smoothness, task performance, algorithm simplicity, and reliability. In 1000 independently repeated simulations, the performance results showed that the proposed FIMVO approach was 10 times better than the VO approach in terms of the number of avoided collisions. The statistical analysis demonstrates that the proposed FIMVO approach outperforms geometric velocity obstacle avoidance approaches concerning reliability and real-time efficiency.