Modified Headed Social Force Model Based on Hybrid Velocity Obstacles for Mobile Robot to Avoid Disturbed Groups of Pedestrians

Abstract

The need for mobile robots that can operate in the human environment is increasing during the Covid-19 pandemic. To accomplish this task, robot navigation must be supported by collision avoidance to maintain human safety and comfort. Collision avoidance methods generally only maintain a safe distance from surrounding objects. Some methods that provide comfort still have difficulty in overcoming pedestrians that move with unexpected direction, changing speed, and unknown trajectories. This paper proposes Hybrid Velocity Obstacle-based modified Headed Social Force Model (HVO-based modified HSFM) to avoid disturbed groups of pedestrians while navigating in the complex and dense workspace. HVO is used to calculate linear and angular velocities to avoid obstacles with nonlinear trajectories. The linear velocity that computed by HVO acts as desired velocity for generating target force of modified HSFM that drive robot to target location. While interaction force of modified HSFM that guide robot to circumvent obstacles is determined by static and moving objects in the surrounding of robot. The angular velocity from HVO is used to produce steering command to avoid collision. For evaluating the proposed method, several simulation scenarios had been run by implementing HVO-based modified HSFM into a two-wheeled differential-steering mobile robot that navigate in the indoor human environment. The results show that our approach is capable to avoid collision by maintaining safety and comfort with disturbed groups of pedestrians with average value 0.14 of Threat Level Index (TLI) from two scenarios. It is envisioned that proposed method can be implemented into real transport robot that operate in human environment.