Fuzzy inference system-assisted human-aware navigation framework based on enhanced potential field

Abstract

With the advent of Autonomous Mobile Robots (AMRs) in public areas such as malls and airports, their harmonious coexistence with humans is crucial. AMRs must operate in a manner that ensures human safety, comfort, and acceptability to reduce stress. This is called Human Aware Navigation. This study introduces a framework for AMR navigation that prioritizes safety and human comfort in such environments, utilizing an enhanced Potential Field approach augmented by Fuzzy Inference Systems. To achieve a smooth AMR trajectory, the framework employs these systems based on AMR, human, and obstacle information. The proposed approach is tested across various scenarios, including complex, cluttered environments that mimic practical situations. Simulation results demonstrate that AMRs using the proposed method navigate human-rich environments safely and comfortably while mitigating common issues associated with Potential Field-based approaches, such as local minima and obstacles near the goal.

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

“…Fuzzy inference systems (FIS) form another well-established paradigm for control systems, specifically useful to model imprecise or non-numerical information and decisions. FIS are applied for traditional robot navigation [292][293][294][295][296] and social robot navigation tasks [297][298][299][300]. They can also be integrated with other approaches, e.g., Q-learning [301] or Reinforcement Learning [302].…”

“…Rules that they designed switch the robot's motion behavior based on its distance to human proxemics zones. Recently, Sampathkumar et al [300] proposed a framework integrating an Artificial Potential Field and FIS for navigation that prioritizes safety and human comfort.…”