Autonomous Navigation Path Planning with Social Norm Adaptation
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Solution Overview
Problem
Current navigation systems for autonomous vehicles in populated environments fail to effectively adapt to social and physical norms, leading to inefficient and potentially disruptive interactions with humans, as they often rely on power-draining and time-consuming algorithms that do not differentiate between interactions with individuals and groups, and lack consideration for social norms.
Innovation Solution
A method for generating navigation paths that takes into account the characteristics of entities in the environment, including human and non-human entities, using social and physical norms to determine optimal waypoints and sub-paths that minimize location costs and ensure harmonious coexistence, by utilizing a processor to receive and update data on entity characteristics and adjust navigation paths accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation algorithms are used for autonomous systems in populated environments, then the system can reach its destination, but it consumes excessive power and time while failing to adapt to social norms
Solution Approach 1:
The navigation path is segmented into multiple waypoints rather than a continuous path. The system divides the journey into discrete segments between waypoints, allowing for efficient computation of individual segments while maintaining overall navigation effectiveness. This segmentation enables the system to process navigation decisions in manageable units, reducing computational power requirements.
Solution Approach 2:
The system pre-determines a set of waypoints and segmental paths before actual navigation begins. By calculating and storing the optimal path segments in advance based on social and physical norms, the system avoids real-time complex computations during navigation, thereby reducing power consumption while maintaining navigation efficiency.
2Adaptability or versatility
If traditional navigation algorithms are used, then the system can navigate to the destination, but it fails to differentiate between interactions with individuals and groups, leading to disruptive behavior
Solution Approach 1:
The system applies different navigation strategies based on local characteristics of entities encountered. When detecting human entities, the system adjusts navigation behavior according to whether they are individuals or groups, applying specific social norms appropriate to each context. This localized adaptation ensures harmonious interactions while maintaining navigation effectiveness.
Solution Approach 2:
The navigation system dynamically adjusts its behavior based on real-time characteristics of entities in the environment. The system modifies path selection, speed, and interaction protocols according to the detected entity types (individuals vs. groups) and their characteristics, enabling adaptive navigation that respects social norms while maintaining operational effectiveness.
3Reliability
If the system updates entity characteristics in real-time, then it can adapt to changing environments, but it increases computational complexity and processing requirements
Solution Approach 1:
The system performs selective updates of entity characteristics rather than continuous full-state monitoring. It updates only the most relevant characteristics needed for navigation decisions, such as entity type, location, and basic attributes, while ignoring less critical details. This partial action approach maintains navigation accuracy while reducing computational complexity.
4Adaptability or versatility
If the system defines destination based on interaction requirements, then it can provide personalized service, but it increases the complexity of destination determination
Solution Approach 1:
The system pre-determines whether interaction with human entities is required and identifies potential interaction targets before finalizing the destination. By performing this analysis in advance, the system simplifies the destination determination process while maintaining the flexibility to adapt to interaction requirements, reducing on-the-fly computational complexity.
Data Source
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AI summary
A system and a method for generating a navigation path for an autonomous system. The method comprises receiving data comprising characteristics of entities, the entities defining an environment in which the autonomous system is configured to operate; receiving first instructions causing the autonomous system to identify a destination in the environment; generating a navigation path comprising waypoints to be followed by the autonomous system to reach the destination, the waypoints being generated based on the characteristics of the entities and defining segmental paths; executing second instructions causing the autonomous system to navigate along the navigation path; and upon navigating from a first waypoint to a second waypoint: accessing updates of the characteristics of the entities located in a vicinity of a corresponding segmental path; generating a sub-path between the first waypoint and the second waypoint based on second information; and navigating along the sub-path to reach the second waypoint.