Robot Path Planning Nodes for Socially Aware Indoor Navigation
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Solution Overview
Problem
Current path planning methods for robots in indoor environments fail to ensure human-friendly navigation and efficient movement, as they do not adequately consider social norms and environmental factors, leading to potential collisions and inefficient routes.
Innovation Solution
A method and system for global path planning that defines node and edge principles based on indoor environment requirements, using an indoor map generated by a mapping robot, which applies social norm principles to specify optimal paths that minimize interference with humans and account for environmental factors, ensuring safe and efficient robot movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional path planning methods are used for robot navigation, then the robot can move from point A to point B, but the robot may collide with humans or fail to follow social norms
Solution Approach 1:
The patent segments the path planning process into multiple components: global path planning using A* algorithm, local path adjustment, and social norm compliance checking. By dividing the navigation system into discrete functional modules, the robot can independently optimize for both collision avoidance and social norm following without compromising either aspect.
Solution Approach 2:
The patent implements dynamic path adjustment where the robot continuously monitors its environment and modifies its trajectory in real-time based on detected humans and social context. The path planning is not static but adapts dynamically to changing conditions, allowing the robot to maintain both safety and social appropriateness throughout navigation.
2Adaptability or versatility
If the robot follows strict social norms and human-friendly navigation rules, then the robot achieves better social acceptance, but the navigation time and path length increase
Solution Approach 1:
The patent applies social norm principles selectively rather than uniformly throughout the entire path. The robot identifies specific zones or situations where social norms are critical (e.g., areas with high human density, designated pedestrian zones) and applies enhanced navigation rules only in those local contexts, while maintaining efficient routing in other areas.
Solution Approach 2:
The patent modifies path planning parameters dynamically based on social context. When social norm compliance is required, the system adjusts parameters such as path selection criteria, speed profiles, and stopping distances. These parameter changes allow the robot to balance social appropriateness with navigation efficiency by adapting to situational requirements rather than following rigid rules.
3Productivity
If the robot prioritizes efficient movement and shortest paths, then navigation speed increases, but the robot may interfere with human activities or fail to account for environmental factors
Solution Approach 1:
The patent implements continuous feedback loops where the robot monitors environmental conditions, detects humans and obstacles, and adjusts its path accordingly. The system uses sensor data feedback to identify situations where efficient routing might conflict with human activities, and automatically modifies its trajectory to prevent interference while maintaining overall navigation efficiency.
Solution Approach 2:
The patent performs preliminary path validation and environmental assessment before the robot begins movement. By pre-identifying potential conflict zones and planning alternative routes in advance, the system can avoid human interference without requiring frequent mid-course corrections, thus maintaining movement efficiency while preventing harmful interactions.
Data Source
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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.