Abstract: To achieve high-speed,agile,and autonomous flight,it is necessary to extend the UAV endurance,reduce command transmission delay,and enhance the UAV's quick response capability.Meanwhile,in complex scenarios,UAVs highly depend on obstacle detection information,and various errors will reduce UAV flight safety.Based on the above problems,an obstacle avoidance strategy is formulated through a local planning obstacle avoidance method with predefined rules.The obstacle avoidance method is optimized using Kalman filtering and Bayesian linear regression model respectively.Experimental results show that the Bayesian linear regression-based method has a prediction speed 2.8 times faster than the Kalman filtering-based method,which not only improves prediction efficiency but also ensures high prediction accuracy and stability.Additionally,to obtain both low-power and safe obstacle avoidance speeds,the obstacle avoidance speed is set as the decision variable,and the endurance time and confidence  are set as the target vectors.The optimal obstacle avoidance speed is obtained by finding the knee point to minimize the trade-off loss between endurance time and confidence level.Finally,the improved local planning-based obstacle avoidance method is verified in the UAV obstacle avoidance environment.The results show that this system can promptly avoid dynamic obstacles,and the total time delay is reduced by approximately 7% on average compared with the obstacle avoidance method  under the same experimental conditions.

Key words: UAV obstacle avoidance;trajectory prediction;spiking neural network;Bayesian theory;multi-objective optimization ,