Robust dynamic robot scheduling for collaborating with humans in manufacturing operations

Abstract

The advent of collaborative robotics in industry has created a closer collaboration between humans and robots. This has led to the need to optimally schedule human and robot tasks to be robust enough to handle variability induced by time-related operator errors caused by the inability to accurately forecast the stochastic nature of human behavior. This article proposes an explicit scheme for tackling time-related variability in human tasks online in applications where humans intervene at a given time in the collaborative workspace. The planning problem is reformulated as a Travelling Salesman Problem combined with a 0/1-Knapsack Problem in order to actively define robot behavior when there is an unmodelled shift in the human execution time sequence. The method uses a two-level adaptation scheme. The first one (offline) inputs the predicted human behaviour in terms of time required for different activities at each work cycle, and then computes an overall task schedule to minimize the robot's operation time and idle time. The second one (online) involves the real-time detection of the human's timing to either stop the prescribed plan or enhance it in order to minimize robot and human idle times, thereby optimizing the sense of ease and fluency in the interaction. The system is simulated in different scenarios where the human predicted time is set to be wrong, and thus the system needs to account for such variation. The effect of the human predicted time on the task schedule is presented and helps to demonstrate the effectiveness of the proposed approach in dealing with human variability without prior modeling knowledge of the human task time distribution.