Hexagonal Map Path Planning for Dynamic Robot Context Switching
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Solution Overview
Problem
Conventional AI technologies for robotic navigation require large volumes of training data, are complex, time-consuming, and energy-intensive, and struggle with dynamic environments, whereas mammalian brains efficiently handle minimal data and context switching with less energy.
Innovation Solution
The method involves representing environments as hexagonal grid maps, identifying objects and contexts using RGB values and spatial similarity measures, and dynamically planning paths based on these contexts, mimicking the brain's efficient spatial information processing for robotic navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AI techniques (machine learning, deep learning) are used for robotic navigation, then the system can identify and make decisions for new incoming data, but the system requires huge volume of training data, is very complex, time consuming, and consumes a lot of energy
Solution Approach 1:
The patent segments the navigation problem into distinct components: spatial map representation using hexagonal grids, object identification through RGB comparison, context determination via spatial similarity quotient, and path planning based on identified contexts. This segmentation eliminates the need for large-scale machine learning by breaking down the navigation task into manageable, rule-based subtasks that consume minimal energy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the environment into a hexagonal grid map and pre-identifying objects and contexts before actual navigation. The spatial similarity quotient is calculated in advance to determine relevant contexts, allowing the robot to make rapid navigation decisions without real-time computational overhead or energy-intensive learning processes.
2Adaptability or versatility
If conventional AI techniques are used for robotic navigation, then the system can handle dynamic environments, but the system needs to be re-trained every time features of training data change
Solution Approach 1:
The patent implements a dynamic context-based navigation system where the robot continuously updates its spatial map and re-evaluates contexts based on current environmental features. The spatial similarity quotient allows dynamic adaptation to new objects and environments without re-training, as the system compares current spatial configurations against stored contextual patterns in real-time.
Solution Approach 2:
The patent changes key parameters from fixed training datasets to dynamic spatial measurements. Instead of relying on pre-trained neural networks with fixed weight parameters, the system uses adjustable parameters such as RGB value comparisons, hexagonal grid coordinates, and spatial similarity quotients that can be recalculated instantly when environmental features change, enabling rapid adaptation without time-consuming re-training.
3Adaptability or versatility
If conventional AI techniques are used for robotic navigation, then the system can learn from training data, but the process is not only very complex and time consuming but also consumes a lot of energy
Solution Approach 1:
The patent replaces the mechanical learning system (neural networks requiring extensive training data and computation) with a cognitive-inspired system using hexagonal spatial maps and context-based reasoning. This substitution dramatically reduces system complexity by using geometric representations and similarity comparisons instead of multi-layer neural networks, while maintaining the ability to adapt to new environments through spatial pattern recognition.
Data Source
AI summary
Path planning for a robot is a compute intensive task. For a dynamic environment this is more cumbersome where position and orientation of objects changes often. Embodiments of the present disclosure provide systems and methods for context based path planning for vector navigation in hexagonal spatial maps. A 2-D environment is represented into a hexagonal grid map that includes hexagonal grid cells, objects are identified based on a comparison of RGB value associated with contiguous cells. Candidate contexts are determined based on objects identified. The hexagonal grid map is rotated at various angles and compared with pre-defined map(s) to determine quantitative measure of similarity for contexts identification from the candidate contexts, based upon which a path is dynamically planned for easy and efficient vector navigation within the hexagonal grid map. The embodiments further enable generating paths for different contexts using navigable common object(s) identified between intersections of the different contexts.