Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function ...Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function can improve search efficiency and solution quality,current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships.This study introduces Genetic Informed Trees(GIT^(*)),which improves upon Effort Informed Trees(EIT^(*))by integrating a wider array of environmental data,such as repulsive forces from obstacles and the dynamic importance of vertices,to refine heuristic functions for better guidance.Furthermore,we integrated reinforced genetic programming(RGP),which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT^(*).RGP leverages a multitude of data types,thereby improving computational efficiency and solution quality within a set timeframe.Comparative analyses demonstrate that GIT^(*)surpasses existing single-query.sampling-based planners in problems ranging from R^(4)to R^(16)and was tested on a real-world mobile manipulation task.展开更多
文摘Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.
文摘Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function can improve search efficiency and solution quality,current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships.This study introduces Genetic Informed Trees(GIT^(*)),which improves upon Effort Informed Trees(EIT^(*))by integrating a wider array of environmental data,such as repulsive forces from obstacles and the dynamic importance of vertices,to refine heuristic functions for better guidance.Furthermore,we integrated reinforced genetic programming(RGP),which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT^(*).RGP leverages a multitude of data types,thereby improving computational efficiency and solution quality within a set timeframe.Comparative analyses demonstrate that GIT^(*)surpasses existing single-query.sampling-based planners in problems ranging from R^(4)to R^(16)and was tested on a real-world mobile manipulation task.
