Automatic Path Planning Optimized by Improved Potential Field and Ant Colony
To solve the problems of local optimal solution in autonomous path planning combined by potential field and ant colony,the initial path selection randomly leads to low efficiency and poor environmental adaptability,an improved algorithm based on adaptive domain and parameter adaptive setting is proposed.The potential field target unreachable problem is firstly im-proved based on the adaptive domain,and the path is smoothed by the oscillating points filtering.And then,through the adaptive setting of the parameters of the state transition function and pheromone update,the balance between the convergence efficiency and the search ability is improved,thereby improving the adaptability to the complexity of the obstacle environment.The experi-mental results show that,the proposed algorithm can effectively avoid local optima,unreachable targets and adaptability prob-lems in complex environments,and is superior to the existing algorithms used in experiments in terms of path length and algo-rithm efficiency,thus verifying the effectiveness of the algorithm
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