Optimization of Artificial Potential Field Using Genetic Algorithm for Human-Aware Navigation of Autonomous Mobile Robots

Sampathkumar, Shurendher Kumar; Chhabra, Anirudh; Choi, Daegyun; Kim, Donghoon

doi:10.1007/978-3-031-46778-3_15

Cited by 1 publication

(2 citation statements)

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“…While the primary aim of robot motion planning is to guide the robot to its destination, HAN prioritizes AMR' s ability to minimize stress and prevent harm to humans. Therefore, it can be defined as "navigation of the AMRs based on human-centered PFs, aiming to minimize discomfort and prevent harm" [19] .…”

Section: Human Aware Navigationmentioning

confidence: 99%

“…However, their study observed longer navigation times compared to [17] . In another study by Sampathkumar et al, the authors adopted the Artificial Potential Field (APF) for path planning, integrating human-associated factors through adjustable coefficients [19] . They optimized the path planning model using a genetic algorithm, fine-tuning coefficients such as scaling factors and Potential Field (PF) orders to minimize the AMR' s path length while adhering to human-prescribed rules.…”

Section: Introductionmentioning

confidence: 99%

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

Sampathkumar,

Choi,

Kim

2024

Complex Engineering Systems

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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.

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Section: Human Aware Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%