Intermediate Waypoint Generation for Robots in Constrained Navigation
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Solution Overview
Problem
Robotic devices often become stuck or trapped when navigating constrained environments due to unaccounted obstacles that are too large for low-level navigation systems to navigate around, despite relying on high-level map data for static obstacles.
Innovation Solution
A method for generating intermediate waypoints using a sparse graph and Voronoi cells to plan a coarse path around dynamic obstacles, ensuring the robot can efficiently navigate through yaw restricted zones by adding intermediate waypoints where line of sight is lost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-level map data is used for navigation, then the robot can plan routes based on static obstacles, but it cannot account for dynamic obstacles that are too large for low-level navigation to handle
Solution Approach 1:
The navigation system is segmented into multiple levels: high-level navigation for static obstacles, low-level navigation for small dynamic obstacles, and an intermediate waypoint generation layer for large dynamic obstacles. This segmentation allows each level to specialize in handling specific types of obstacles, improving overall reliability while maintaining adaptability.
Solution Approach 2:
The system performs preliminary action by generating intermediate waypoints in advance when potential conflicts with large dynamic obstacles are detected. These pre-computed waypoints guide the robot to safe positions before entering constrained environments, preventing the robot from becoming stuck or trapped.
2Ease of operation
If the robot follows high-level waypoints directly, then the navigation route is simple to execute, but the robot may become stuck when encountering unaccounted obstacles in constrained environments
Solution Approach 1:
Intermediate waypoints act as mediators between high-level waypoints and the robot's actual path. These intermediary points are dynamically generated to guide the robot through constrained environments safely, maintaining the simplicity of high-level navigation while adding the reliability needed to handle unexpected obstacles.
3Adaptability or versatility
If intermediate waypoints are generated in real-time, then the robot can adapt to dynamic obstacles, but the computational complexity and processing time increase
Solution Approach 1:
The system applies local quality by generating intermediate waypoints only in specific regions where constraints are detected, rather than computing waypoints throughout the entire environment. This localized approach reduces computational complexity while maintaining real-time adaptability where it is most needed.
Solution Approach 2:
The system performs partial action by generating intermediate waypoints only when necessary - specifically when the robot is approaching constrained environments or when obstacles are detected. This selective generation reduces overall computational load while maintaining sufficient adaptability for critical situations.
Data Source
AI summary
A method for generating intermediate waypoints for a navigation system of a robot includes receiving a navigation route. The navigation route includes a series of high-level waypoints that begin at a starting location and end at a destination location and is based on high-level navigation data. The high-level navigation data is representative of locations of static obstacles in an area the robot is to navigate. The method also includes receiving image data of an environment about the robot from an image sensor and generating at least one intermediate waypoint based on the image data. The method also includes adding the at least one intermediate waypoint to the series of high-level waypoints of the navigation route and navigating the robot from the starting location along the series of high-level waypoints and the at least one intermediate waypoint toward the destination location.