Migratory birds depend on the perception of atmospheric updraft for long-distance flight.To realize more efficient autonomous soaring in an unpowered glider,different strategies for using potential sensorimotor cues t...Migratory birds depend on the perception of atmospheric updraft for long-distance flight.To realize more efficient autonomous soaring in an unpowered glider,different strategies for using potential sensorimotor cues to achieve autonomous soaring efficiency were compared and optimized.A simulation framework of autonomous soaring for an unpowered glider was developed based on a reinforcement learning algorithm.The framework was composed of three models:an updraft environment model,the glider's dynamics and control model,and a reinforcement learning agent,which learns to harvest more energy in flight.Based on the simulation,effects of different combinations of 12 potential sensorimotor cues on soaring efficiency were studied.Firstly,the absence of one particular sensorimotor cue and the use of only a single valid cue in autonomous soaring were analyzed.The results showed that the vertical airflow velocity gradient(aw)and the wing-tip updraft velocity difference(τ)have advantages over the other cues.Secondly,strategies combining aw orτwith other cues were analyzed to achieve more effective autonomous soaring,and seven potentially effective combinations of sensorimotor cues were identified.The final results showed that,among the tested combinations,the combination of vertical airflow velocity(Vw)andτ,enables the most efficient autonomous soaring.This study identified a highly effective sensorimotor cue strategy to guide an intelligent glider to achieve long-distance autonomous soaring flight.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12202384 and U2241274)the Leading Talent Project for Scientific and Technological Innovation in Zhejiang Province(No.2023R5220)the Specialized Research Projects of Huanjiang Laboratory,China。
文摘Migratory birds depend on the perception of atmospheric updraft for long-distance flight.To realize more efficient autonomous soaring in an unpowered glider,different strategies for using potential sensorimotor cues to achieve autonomous soaring efficiency were compared and optimized.A simulation framework of autonomous soaring for an unpowered glider was developed based on a reinforcement learning algorithm.The framework was composed of three models:an updraft environment model,the glider's dynamics and control model,and a reinforcement learning agent,which learns to harvest more energy in flight.Based on the simulation,effects of different combinations of 12 potential sensorimotor cues on soaring efficiency were studied.Firstly,the absence of one particular sensorimotor cue and the use of only a single valid cue in autonomous soaring were analyzed.The results showed that the vertical airflow velocity gradient(aw)and the wing-tip updraft velocity difference(τ)have advantages over the other cues.Secondly,strategies combining aw orτwith other cues were analyzed to achieve more effective autonomous soaring,and seven potentially effective combinations of sensorimotor cues were identified.The final results showed that,among the tested combinations,the combination of vertical airflow velocity(Vw)andτ,enables the most efficient autonomous soaring.This study identified a highly effective sensorimotor cue strategy to guide an intelligent glider to achieve long-distance autonomous soaring flight.
