Indoor Robot Path Planning Using Social-Norm Node Specification
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Solution Overview
Problem
Current path planning methods for autonomous robots in indoor environments do not adequately consider social norms and environmental factors, leading to inefficient and potentially threatening movements.
Innovation Solution
A node and edge specification principle based on social norms is defined for global path planning, using right-hand traffic principles, speed adjustments, and node types like corridor entry and door pass nodes, to generate efficient and human-friendly navigation paths for robots in indoor spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional path planning methods are used for robot navigation in indoor environments, then the robot can move from point A to point B, but the movement may not adhere to social norms and may appear threatening or inefficient to humans
Solution Approach 1:
The path planning system is segmented into distinct node types (corridor entry nodes, door pass nodes, junction nodes, etc.) and edge types (caution edges, speedy edges, basic edges). This segmentation allows the robot to apply different navigation rules to different spatial contexts, making the overall system more human-friendly while managing complexity through modular classification.
Solution Approach 2:
Different regions of the indoor environment are assigned different quality characteristics through node and edge specifications. For example, corridor areas have specific traversal rules, door areas have caution zones, and junctions have priority rules. This local differentiation enables the robot to adapt its behavior to local social norms without requiring a complete redesign of the global path planning system.
2Reliability
If the robot adjusts speed frequently to adhere to social norms and navigate safely, then human-friendly navigation is achieved, but navigation efficiency and travel time increase
Solution Approach 1:
The system performs preliminary classification of edges into caution edges, speedy edges, and basic edges during map processing. This preliminary action allows the robot to pre-know which areas require speed adjustments, enabling smooth speed transitions rather than reactive braking. The robot can maintain higher speeds on speedy edges (robot lanes) while automatically slowing down for caution edges (door areas, junctions), optimizing both safety and efficiency.
Solution Approach 2:
The path planning system dynamically adjusts the robot's speed based on the type of edge being traversed. The speed is not fixed but varies according to the local environment characteristics encoded in the edge specifications. This dynamic speed adjustment allows the robot to be safe in cautious areas while maintaining efficiency in open corridors and robot lanes.
3Productivity
If the robot follows simple shortest path algorithms, then navigation speed is high, but the robot may not interact properly with indoor infrastructure like elevators and may collide with obstacles or humans
Solution Approach 1:
The navigation space is segmented into different types of nodes and edges that encode interaction rules with indoor infrastructure. Corridor entry nodes handle interactions at corridor ends, door pass nodes handle door-related interactions, and junction nodes handle intersection logic. This segmentation allows the robot to systematically handle infrastructure interactions without deviating from an efficient path.
Solution Approach 2:
The node and edge specification system acts as an intermediary layer between the simple shortest path algorithm and the physical robot navigation. Instead of modifying the core pathfinding algorithm, the specification system translates abstract path nodes into context-aware navigation instructions, mediating between computational efficiency and safe physical interaction with the environment.
Data Source
AI summary
A method and a system generate global path planning of a robot according to a node specification principle that is defined based on social norms for an indoor space using an indoor map to assist human-friendly navigation of the robot.