In the real-world situation,the lunar missions’scale and terrain are different according to various operational regions or worksheets,which requests a more flexible and efficient algorithm to generate task paths.A mu...In the real-world situation,the lunar missions’scale and terrain are different according to various operational regions or worksheets,which requests a more flexible and efficient algorithm to generate task paths.A multi-scale ant colony planning method for the lunar robot is designed to meet the requirements of large scale and complex terrain in lunar space.In the algorithm,the actual lunar surface image is meshed into a gird map,the path planning algorithm is modeled on it,and then the actual path is projected to the original lunar surface and mission.The classical ant colony planning algorithm is rewritten utilizing a multi-scale method to address the diverse task problem.Moreover,the path smoothness is also considered to reduce the magnitude of the steering angle.Finally,several typical conditions to verify the efficiency and feasibility of the proposed algorithm are presented.展开更多
基金supported by the National Natural Science Foundations of China(No.11772185)Fundamental Research Funds for the Central Universities(No.3072022JC0202)。
文摘In the real-world situation,the lunar missions’scale and terrain are different according to various operational regions or worksheets,which requests a more flexible and efficient algorithm to generate task paths.A multi-scale ant colony planning method for the lunar robot is designed to meet the requirements of large scale and complex terrain in lunar space.In the algorithm,the actual lunar surface image is meshed into a gird map,the path planning algorithm is modeled on it,and then the actual path is projected to the original lunar surface and mission.The classical ant colony planning algorithm is rewritten utilizing a multi-scale method to address the diverse task problem.Moreover,the path smoothness is also considered to reduce the magnitude of the steering angle.Finally,several typical conditions to verify the efficiency and feasibility of the proposed algorithm are presented.
