Low-Energy Path Planning for Robots Using an Improved Ant Colony Algorithm in Multi-Attribute Grid Environments
To reduce the energy consumption of mobile robots in unstructured environments and enhance their task efficiency under constrained energy resources,this paper presents a low-energy path planning method based on an improved Ant Colony Algorithm.Firstly,a multi-attribute grid map modeling approach is proposed,which accounts for rough and uneven terrain in addition to conventional global obstacles.Secondly,to further minimize the costs of global path planning in unstructured envi-ronments,energy-related factors such as path length,turning frequency,slope,and surface roughness are integrated into the heuristic function of the traditional Ant Colony Algorithm,optimizing it for energy efficiency.Moreover,the pheromone update mechanism is refined by considering various dynamic energy factors during robot movement,including potential energy require-ments for uphill motion,kinetic energy recovery on downhill slopes,air resistance,rolling friction,and energy conversion effi-ciency.Simulation and experimental results show that the proposed method can effectively plan optimal low-energy paths for mobile robots in multi-attribute grid environments,offering valuable insights for real-world applications.
mobile robotunstructured environmentant colony algorithmpath planning
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