In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive contr...In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework.The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach.This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds.The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.展开更多
A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS sea...A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.展开更多
文摘In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework.The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach.This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds.The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.
基金National Natural Science Foundation of China(No.62241503)Natural Science Foundation of Shanghai,China(No.22ZR1401400)。
文摘A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.
