RESUMEN
The social force model (SFM) can be applied to characterize pedestrian dynamics in normal scenarios. However, its model of interactions among pedestrians deviates from actual scenarios to some extent. Thus, we propose an improved SFM where pedestrians consider avoiding potential conflicts in advance during the walking process. Meanwhile, the response range of potential conflicts is related to the response time and relative velocity vector. Simulation results demonstrate that the conflict avoidance force plays an important role in guiding pedestrian dynamics. Conflict avoidance makes pedestrian trajectories smoother and more realistic. Moreover, for high pedestrian density (without congestion), moderate values of response time may exist, resulting in the minimum evacuation efficiency. We hope to provide some insights into how to better model interactions among pedestrians during normal evacuation.
RESUMEN
The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.