Motion Path Planning Method for Logistics Robots Based on Improved Grey Wolf Algorithm
The development of information technology in Internet of Things(IoT)has made logistics robots face problems such as local extreme traps and algorithm convergence in current motion path planning.In addition,traditional motion path planning methods are difficult to meet the requirements of complex and changing logistics environments.Therefore,it is urgent to explore active and ef-fective motion planning methods.Based on this,the grey wolf optimization algorithm is used to plan the global path and analyze the mixed path,and introduces the collaborative quantum and improved artificial potential fields,achieving the update of the convergence factor of the algorithm and the execution of crossover strategies.Through simulating and analyzing logistics robots,the results show that the algorithm has a good convergence in test function,and the search path length in a single obstacle is reduced by about 5％,with an average cost consumption of 23.65,which can better detect dynamic obstacles and effectively escape local minimum traps.And its running time in dynamic environments is shortened by 46.37％,with good optimization and obstacle avoidance performance.The proposed path planning algorithm can effectively provide a reference and value for the development of logistics industry and auto-mated scheduling.
万方数据
维普
