Energy-Efficient Collision Avoidance in Unmanned Aerial Vehicles: Enhancing Path Planning with a Modified Tentacles Algorithm

The increasing deployment of Unmanned Aerial Vehicles (UAVs) in surveillance, environmental monitoring, disaster management, logistics, and smart transportation has intensified the need for efficient autonomous navigation systems capable of avoiding obstacles while conserving onboard energy resources. Conventional path planning algorithms often prioritise collision avoidance without adequately addressing energy efficiency, resulting in excessive maneuvering, increased power consumption, and reduced flight endurance. This study presents an Energy-Efficient Modified Tentacles Algorithm (EEMTA) for enhancing collision avoidance and path planning in UAV systems operating within dynamic and obstacle-rich environments. The proposed approach extends the conventional Tentacles Algorithm by incorporating adaptive trajectory generation, energy-aware cost functions, dynamic obstacle weighting, and trajectory smoothness optimization. A MATLAB/Simulink-based simulation framework was developed to evaluate the performance of the proposed model under varying obstacle densities and mission conditions. The algorithm was assessed using path length, energy consumption, collision rate, computation time, and trajectory smoothness as key performance indicators. Simulation results revealed that the proposed EEMTA achieved an average path length of 1185 m, compared to 1290 m, 1365 m, and 1420 m obtained using the Conventional Tentacles, A-star, and Artificial Potential Field (APF) algorithms, respectively. Energy consumption was reduced to 298 Wh, representing a 15.3% improvement over the Conventional Tentacles Algorithm (352 Wh). The proposed model also recorded the lowest collision rate of 1.1%, compared to 3.4%, 5.2%, and 8.5% for Conventional Tentacles, A-star, and APF methods, respectively. Additionally, the computation time remained low at 17 ms, demonstrating suitability for real-time implementation. The results confirm that the proposed EEMTA significantly improves navigation safety, energy efficiency, and flight endurance, making it a viable solution for intelligent autonomous UAV operations in complex environments.