Autonomous Control Method for Object Grasping of Logistics Sorting Manipulator Considering Changes in Lighting Environment

Abstract

The autonomous grasping operation is the key to the intelligent logistics sorting manipulators. Since the current logistics sorting manipulators mostly use visual sensors to identify objects, they are extremely vulnerable to the changes in the lighting environment. Therefore, the research considers the influence of complex lighting environment on the autonomous grasping of logistics sorting manipulators. Furthermore, it verifies the effectiveness of SAC-AE-ICM algorithm through simulations and experiments. The experimental results show that SAC-AE-ICM algorithm can quickly achieve convergence in many experiments and can obtain global optimization. In the process of automatic capture, the success rate of SAC-AE-ICM algorithm can reach 90%, which is 25% higher than that of the method without ICM and has better convergence. The success rate was as high as 77% for unknown or irregular targets. In practical experiments, SAC-AE-ICM can effectively capture under good lighting conditions. However, under low light conditions, the probability of capturing unknown targets is about 72.5%. Overall, the success rates for single-object and multi-object capture are 88% and 85%, respectively.