Risk Mitigation for Human-Robot Collaboration Using Artificial Intelligence

Abstract: In human-robot collaborative (HRC) scenarios where humans and robots work together sharing the same workspace, there is a risk of potential hazard that may occur. In this work, an AI-based risk analysis solution has been developed to identify any condition that may harm a robot and its environment. The information from the risk analysis is used in a risk mitigation module to reduce the possibility of being in a hazardous situation. The goal is to develop safety for HRC scenarios using different AI algorithms and to check the possibilities of improving efficiency of the system without any compromise on the safety. This report presents risk mitigation strategies that were built on top of the robot’s control system and based on the ISO 15066 standard. Each of them used semantic information (scene graph) about the robot’s environment and changed the robot’s movement by scaling speed. The first implementation of risk mitigation strategy used Fuzzy Logic System. This system analyzed the riskiest object’s properties to adjust the speed of the robot accordingly. The second implementation used Reinforcement Learning and considered every object’s properties. Three networks (fully connected network, convolutional neural network, and hybrid network) were implemented to estimate the Qvalue function. Additionally, local and edge computation architecture wereimplemented to measure the computational performance on the real robot. Each model was evaluated by measuring the safety aspect and the performance of the robot in a simulated warehouse scenario. All risk mitigation modules were able to reduce the risk of potential hazard. The fuzzy logic system was able to increase the safety aspect with the least efficiency reduction. The reinforcement learning model had safer operation but showed a more compromised efficiency than the fuzzy logic system. Generally, the fuzzy logic system performed up to 28% faster than reinforcement learning but compromised up to 23% in terms of safety (mean risk speed value). In terms of computational performance, edge computation was performed faster than local computation. The bottleneck of the process was the scene graph generation which analyzed an image to produce information for safety analysis. It took approximately 15 seconds to run the scene graph generation on the robot’s CPU and 0.3 seconds on an edge device. The risk mitigation module can be selected depending on KPIs of the warehouse operation while the edge architecture must be implemented to achieve a realistic performance.