Autonomous environmental perception, path planning and dynamic landing method and system of unmanned aerial vehicle

An autonomous environmental perception, path planning and dynamic landing method of an unmanned aerial vehicle, comprising the following steps:

• S1. obtaining depth information and two-dimensional plane information of an environment in real time by the unmanned aerial vehicle, and generating three-dimensional environmental information in space through a three-dimensional reconstruction algorithm based on the depth information and the two-dimensional plane information;
• S2. determining a global starting point and a global end point, and generating an initial path according to the global starting point and the global end point;
• S3. optimizing the initial path in real time based on a local path optimization algorithm to obtain a first optimized path, and judging in real time whether a perception threshold of the current position of the unmanned aerial vehicle is greater than a preset threshold;
• when the perception threshold is greater than the preset threshold, proceeding into S4;
• otherwise, continuing to perform S3, until the global end point is reached, and proceeding into S7;
• wherein a calculation formula of the perception threshold is as follows:
• τ
• per
• =
• Γ
• optimiz
• ⁢
• e
• ⁢
• d
• Γ
• i
• ⁢
• n
• ⁢
• i
• ⁢
• t
• ;
• wherein τper represents the perception threshold, Γinit represents the initial path within the field of view of the unmanned aerial vehicle, Γoptimized represents the first optimized path within the field of view of the unmanned aerial vehicle, and τ represents the preset threshold, when τper>τ, proceeding into S4;
• S4. perceiving the frontier of the field of view of the unmanned aerial vehicle, storing the perceived frontier in the form of space points and recording as frontier points, and performing distance optimization on the frontier points to obtain a frontier space point set P;
• S5. according to the frontier space point set P, optimizing the initial path in real time based on a frontier-perceived path optimization method to obtain a second optimized path and a local end point;
• S6. switching to optimizing the initial path in real time based on the local path optimization algorithm when the unmanned aerial vehicle advances along the second optimized path to the local end point;
• and
• S7. carrying out dynamic landing based on a deep reinforcement learning algorithm after the unmanned aerial vehicle arrives at the global end point.