Multi-layer robotic controller for enhancing the safety of mobile robot navigation in human-centered indoor environments

TL;DR

This paper introduces a three-layer robotic navigation system to improve safety and reduce costs in human-filled indoor spaces.

Contribution

A novel three-tiered robotic control system that integrates human input, virtual barriers, and semantic data for safer and more efficient navigation.

Findings

The system uses a human-in-the-loop approach to detect and avoid dangers in real time.

Dynamic virtual barriers prevent robot access to hazardous zones during specific times.

Semantic data from BIM models enables efficient path planning without real-time sensor mapping.

Abstract

This research proposes a multi-layer navigation system for indoor mobile robots when they share space with vulnerable individuals. The primary objectives are increasing or maintaining safety measures and curtailing operational costs, emphasizing reducing reliance on intricate sensor technologies and computational resources. The developed system employs a three-tiered control approach, with each layer playing a pivotal role in the navigation process. The “online” control layer integrates a human-in-the-loop strategy, where the human operator detects missing obstacles or approaching danger through a user interface and sends a trigger to the robot’s controller. This trigger enables the system to estimate the coordinates of the danger and update the robot’s navigation path in real time, minimizing reliance on complex sensor systems. The “semi-online” control layer generates dynamic virtual barriers to restrict the robot’s navigation in specific areas during specific times. This ensures the robot avoids hazardous zones that could pose temporary risks to the human or robot. For example, areas with temporary obstructions or potential danger, such as kids’ play zones or during cleaning, are temporarily restricted from the robot’s path, ensuring safe navigation without relying solely on real-time sensor data. The “offline” control layer centers around the use of semantic information to control the robot’s behavior according to user-defined space management and safety requirements. By leveraging Building Information Models (BIM) as digital twins, this layer combines semantic and geometric data to comprehensively understand the environment. It enables the robot to navigate according to precise user requirements, utilizing the semantic context for path planning and behavior control. This layer obviates the need for a real-time sensor mapping process, making the system more efficient and adaptable to user needs. This research represents a significant step forward in enhancing the navigational capabilities of robots within human-centric indoor environments, with a core focus on safety, adaptability, and cost-effectiveness.

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