A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications

In the last couple of decades, autonomous human assistance robots have been enormously attracting the industrial sector. For this purpose, numerous researchers have contributed towards designing efficient and robust human assistance mechanisms. However, their proposed approaches do not provide a cost-effective solution due to the deployment of exorbitant sensors and sophisticated infrastructure. Besides, it was quite challenging for existing human-following robots to track their assigned human companion in different illusional states and luminous conditions while detecting obstacles and taking respective maneuvers (i.e., abrupt turns, etc.). Moreover, self-driving solutions need to take fast and real-time actions to avoid collisions in the designated environment. For this purpose, literature has shown the efficiency of YOLOv3 with respect to providing real-time results in latency-sensitive applications. Hence, to overcome this dilemma, we propose to develop an economically efficient deep learning- based smart labor assistance trolley that uses the YOLO v3 as a core detective deep learning technique with advance and efficient perception and motion planning modules. The perception module succours the autonomous trolley to precisely detect and classify the objects. While the motion planning module uses the specific intended target detection technique to follow the targeted person in crowded environment. These techniques make the autonomous trolley able to take expeditious, meticulous, and conspicuous action in real-time. The labor assistant robot detects and tracks the respective person using YOLOv3. To validate the efficiency of the proposed solution, we have performed a series of experiments considering different test cases. Our proposed work achieved a mean average precision of 0.81%.