This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs' transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on Teaching Learning Based Optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations' assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.

Semantic segmentation is one of the most critical modules in road scene understanding. In this paper, we focus on the challenging task of pedestrian's relative location perception in the semantic graph of complex driving scenes. Prevalent research on semantic segmentation mainly concentrate on improving the segmentation accuracy with less attention paid to computational efficiency. Furthermore, little effort has been made in pedestrian location perception in complex driving scenes. For example, current semantic segmentation methods classify all pedestrians as a mono category, regardless of whether the pedestrians are penetrating into the vehicular lane or standing still in the safe sidewalk area. We propose a pedestrian location perception network (P-LPN). P-LPN can produce real-time semantic segmentation while simultaneously providing location inference for each pedestrian in semantic maps. This enables autonomous driving system to categorize pedestrians into different safety levels. We comprehensively evaluated P-LPN on CityScapes benchmark through comparative studies. Our proposal achieved competitive performance in both accuracy and efficiency. It yields quality inference with real-time speed at ∼22 fps.