Robot Navigation Heuristics for Real-Time Dynamic Path Planning
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Solution Overview
Problem
Existing navigation techniques for robots in dynamic environments fail to account for environmental changes and do not always produce the shortest path, leading to inefficiencies and inaccuracies in path planning.
Innovation Solution
A system and method utilizing heuristic learning to determine a robot's preferred path, position, and orientation based on human-robot interaction and path scaling factors, incorporating past navigation data and user knowledge to adapt to dynamic changes in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If A* search algorithm is used for path planning, then the path finding speed is improved, but the path optimality cannot be guaranteed
Solution Approach 1:
The patent modifies the cost function parameters in A* algorithm by introducing learned heuristic values and path scaling factors that adapt to environmental conditions, thereby improving path optimality while maintaining computational efficiency
Solution Approach 2:
The system implements feedback mechanisms where the robot learns from past navigation experiences and environmental changes, continuously updating heuristic values to improve both path quality and adaptability in dynamic environments
2Device complexity
If pre-computed heuristics are used for navigation, then the path planning complexity is reduced, but the adaptability to environmental changes deteriorates
Solution Approach 1:
The patent transforms static pre-computed heuristics into dynamic adaptive heuristics that update based on environmental changes and robot experiences, maintaining low computational complexity while improving adaptability through incremental learning
Solution Approach 2:
The system performs preliminary learning of heuristic values during initial navigation and updates them progressively, preparing adaptive path planning capabilities in advance while maintaining simplicity in the core pathfinding algorithm
3Device complexity
If heuristics based on distance are used, then the path planning is simplified, but the accuracy in learning exact poses deteriorates
Solution Approach 1:
The patent applies different levels of heuristic precision at different locations and contexts, using simple distance-based heuristics for general path planning and more precise learned heuristics for critical navigation decisions and pose estimation
Data Source
AI summary
Disclosed is a system for controlling navigation of a robot in a dynamic environment based on heuristic learning. The system comprising: a heuristic learning unit configured to determine at least a preferred path, a preferred position, and a preferred orientation for the robot based on human robot interaction (HRI) during navigation of the robot and a path scaling factor and a navigation control unit configured to generate at least one of: an optimal path, an optimal position, and an optimal orientation, for navigation of the robot in the dynamic environment in real-time, during navigation of the robot, based on at least one of: the preferred path, the preferred position, the preferred orientation or a previous navigation data associated with the robot.