Becaused　of　the　actual　navigation　requirements　of　unmanned　surface　vehicles,　the　planned　path　should　meet　the　criteria　of　smoothness　and　economy.　Therefore,　a　novel　path　planning　strategy　based　on　an　improved　crow　search　algorithm　combining　straight　lines　and　circular　arc　turns　is　proposed.　A　new　path　fitting　method　is　introduced　to　optimize　the　number　of　turning　points　and　address　the　issue　of　arc　transition　at　turning　points.　This　method　overcomes　the　problem　of　frequent　direction　adjustments　caused　by　B-spline　curve　paths　for　unmanned　surface　vehicles,　while　ensuring　that　they　can　achieve　steering　while　maintaining　a　stable　speed,　thereby　improving　navigation　stability　and　economy.　Based　on　this,　an　improved　crow　search　algorithm　is　introduced　to　optimize　the　location　of　path　turning　points.　The　improvement　of　the　algorithm　is　mainly　reflected　in　three　aspects:　the　use　of　a　reverse　learning　strategy　to　optimize　the　quality　and　the　diversity　of　the　initial　population,　the　proposal　of　a　dynamically　changing　awareness　probability　to　improve　the　global　search　ability　of　the　initial　segment　and　the　local　search　ability　of　the　final　segment　of　the　algorithm,　and　the　utilization　of　the　Levy　flight　strategy　to　improve　the　directionality　and　the　effectiveness　of　the　search.　The　simulation　results　show　that　the　proposed　new　path　fitting　method　is　superior　to　the　B-spline　curve　fitting　method　and　the　straight　line　segment　fitting　method.　Building　on　this　fitting　method,　the　improved　crow　search　algorithm,　the　standard　crow　search　algorithm,　the　differential　evolution　algorithm,　and　the　genetic　algorithm　are　used　to　optimize　the　location　of　the　path　turning　point.　Iterative　calculation　and　variance　analysis　results　demonstrate　that　the　proposed　improved　crow　search　algorithm　exhibits　higher　convergence　accuracy　and　robustness　compared　to　the　other　three　algorithms,　effectively　addressing　practical　problems　in　unmanned　surface　vehicle　path　planning.　©　2024　Chongqing　University.　All　rights　reserved.