Mobile Robot Path Planning Using Diffusion Maps
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Solution Overview
Problem
Existing path planning algorithms for autonomous mobile robots rely on prior maps, which are inadequate for dynamic environments with changing obstacles, limiting their ability to adapt and avoid collisions effectively.
Innovation Solution
The use of diffusion maps combined with a receding horizon approach allows mobile robots to plan and adjust paths in real-time without a prior map, utilizing sensor data to determine transition probabilities and navigate through dynamic environments while avoiding collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If path planning algorithms use prior maps for navigation, then navigation routes can be pre-defined and followed, but the ability to adapt to dynamic environments with changing obstacles is limited
Solution Approach 1:
The patent applies dynamics by transitioning from static map-based path planning to dynamic real-time path planning. The system continuously updates the path plan based on current sensor data and detected obstacles, making the navigation system adaptive to changing environments. The path planning module recalculates routes dynamically as obstacles appear or disappear, ensuring both adaptability and collision avoidance.
Solution Approach 2:
The system implements feedback by using sensor data to continuously monitor the environment and feed this information back to the path planning module. The sensor module detects obstacles and environmental changes in real-time, and this feedback is used to adjust and update the path plan dynamically, improving both adaptability and safety.
2Productivity
If pre-defined navigation routes are used, then navigation efficiency is improved, but the system cannot effectively respond to newly constructed buildings or moving obstacles
Solution Approach 1:
The system maintains navigation efficiency by using a structured path planning framework while incorporating dynamic updates. The path planning module can quickly recalculate routes when environmental changes are detected, balancing efficient navigation with adaptability to new obstacles or changes in the environment.
Solution Approach 2:
The system changes parameters by updating path plan parameters in real-time based on sensor data. When obstacles or environmental changes are detected, the system modifies navigation parameters such as route coordinates, speed, and direction to adapt to new conditions while maintaining overall navigation efficiency.
3Adaptability or versatility
If real-time sensor data is processed continuously without prior maps, then adaptability to dynamic environments is improved, but computational complexity increases
Solution Approach 1:
The system applies segmentation by dividing the environment into discrete grid cells or spatial zones for processing. This segmentation allows the path planning algorithm to process sensor data more efficiently by working with divided spatial representations rather than continuous data, reducing computational complexity while maintaining real-time adaptability.
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for path planning for autonomous moving devices. Aspects of the invention include planning a path for a mobile robot to move autonomously in an environment that includes other static and moving obstacles, such as, for example, other mobile devices and pedestrians, without reference to a prior map of the environment. A planned path for a mobile robot can be determined, adjusted, and adapted using diffusion maps to avoid collisions while making progress towards a global destination. Path planning can include using transition probabilities between grid points to find a feasible path through parts of the environment to make progress towards the global destination. In one aspect, diffusion maps are used in combination with a receding horizon approach, including computing diffusion maps at specified time intervals.