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Abstract: 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 is 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 show that the vertical airflow velocity gradient, a_w, and the vertical updraft velocity difference at the left and right-wing tips, τ, have advantages over the other cues. Secondly, strategies combining a_w or τ with 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, V_w, and τ, 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.