Abstract
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.
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We acknowledge the Turkish government for funding the doctoral scholarship of Fethi Candan.
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Conceptualization, methodology, software, F.Candan; Validation, F.Candan and A.Beke; writing–original draft preparation, F.Candan, L. Mihaylova and M.Mahfouf; writing–review and editing F.Candan, L. Mihaylova and M.Mahfouf. All authors have read and agreed to the published version of the manuscript.
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Appendix A: Simulation Results with Multiple Agents
Appendix A: Simulation Results with Multiple Agents
These results demonstrate that the proposed collision avoidance algorithm achieves a comparable performance as in the case of three UAVs.
In this Appendix presents simulation results from the proposed and compared methods with multi-agents, 14 agents in the considered example. The sampling time has been selected and fixed as 0.05s. Then, 1000 time tests independently with multi-agents have been shown in Table 4, and also, in Fig. 16, one of the tested results has been represented.
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Candan, F., Beke, A., Mahfouf, M. et al. A Real-time Fuzzy Interacting Multiple-Model Velocity Obstacle Avoidance Approach for Unmanned Aerial Vehicles. J Intell Robot Syst 110, 61 (2024). https://doi.org/10.1007/s10846-024-02075-6
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DOI: https://doi.org/10.1007/s10846-024-02075-6