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Figure 11 and Figure 12 demonstrate that when the sensor detected obstacles ahead, the UAV started from the current position, took the current flight direction as the target area and used the BINN to replan the path. When the sensors did not detect any dynamic obstacles ahead, the UAV returned to the predetermined path, and the local path planning was completed. The experimental results showed that the improved method avoided dynamic obstacles effectively and affords a collision-free flight. Furthermore, Figure 13 highlights that BINN-HHO had the best convergence value and speed, where its convergence values were reduced by 6% and 1.2% compared with those of the HHO algorithm. This demonstrated that introducing the energy cycle decline mechanism could dynamically balance the global exploration and local production performance of the HHO algorithm to obtain the optimal value quickly.
X-Plane 10 Global - 64 Bit Free Download [serial number]
2ff7e9595c
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