Robot Coverage Path Planning With Priority-Based Viewpoint Selection
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Solution Overview
Problem
Existing coverage path planning for autonomous mobile devices is not optimized, as traditional approaches do not consider observation quality of objects, leading to inefficient viewpoint generation and increased time and energy consumption due to abrupt transitions between viewpoints.
Innovation Solution
A method that assigns a priority cost to each viewpoint based on detected objects and their region of interest, using objective functions to identify a set of optimal viewpoints and generate a coverage path that maximizes information gain while ensuring smooth transitions and minimizing kinematic constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional viewpoint generation approaches are used to ensure maximum coverage of the region, then coverage completeness is improved, but observation quality of objects in the viewpoints is not optimized
Solution Approach 1:
The patent applies local quality by differentiating viewpoint evaluation into two distinct dimensions: coverage quality (global) and observation quality (local). Each viewpoint is assessed not only for its coverage area but also for its ability to observe objects of interest, with priority costs assigned based on object importance and observation quality metrics. This allows the system to optimize for both coverage completeness and observation quality simultaneously.
2Productivity
If TSP is used to generate a coverage path representing a set of admissible viewpoints, then coverage path is generated, but the coverage path is not optimized due to kinematic constraints of the robot
Solution Approach 1:
The patent applies dynamics by making the coverage path adaptive and dynamic rather than static. The path is generated by solving TSP on a cost matrix that incorporates kinematic constraints, and it can be updated online as the robot executes the path and new information becomes available. This dynamic approach allows the system to optimize transition times and energy consumption while maintaining coverage efficiency.
Solution Approach 2:
The patent changes the parameters used in TSP from simple distance metrics to comprehensive cost functions that include transition time, energy consumption, and kinematic feasibility. By modifying the cost parameters to reflect actual robot operational constraints, the resulting coverage path is optimized for both efficiency and feasibility.
3Productivity
If abrupt transitions between viewpoints are allowed to maintain coverage, then coverage completeness is maintained, but additional time and energy are consumed
Solution Approach 1:
The patent changes the cost parameters in the path optimization to include energy consumption and transition time metrics. By incorporating these parameters into the TSP cost matrix, the system naturally selects paths that minimize energy consumption and transition time while maintaining coverage completeness.
Data Source
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AI summary
Embodiments of the present disclosure disclose a system and method for optimizing coverage path planning of robots. Data is received from sensors associated with robots in a region. The data is captured in a plurality of poses from a plurality of viewpoints in the region. A priority cost is assigned for each viewpoint based on: objects detected in the data and region of interest in the objects captured by the sensors in the viewpoint. Further, a set of optimal viewpoints are determined from the plurality of viewpoints using objective functions. The objective functions is based on the priority cost associated with each viewpoint and a coverage value for each corresponding viewpoint. The coverage value for a viewpoint indicates an area of the region captured by the sensors from the corresponding viewpoint. Thereafter, a coverage path is generated for the robots based at least on the set of optimal viewpoints.