Multi-Robot Coverage Path Planning With Dynamic Occupancy Maps
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Solution Overview
Problem
Current coverage path planning for autonomous mobile robots is inadequate in unknown and dynamic environments, particularly for multi-robot systems, as existing solutions fail to efficiently cover all areas while avoiding obstacles and updating sensor data in real-time.
Innovation Solution
A processor-implemented method and system that generates a map of a predefined region divided into cells, determines optimal robot poses based on sensor data, and creates a coverage path plan by assigning sensor visibility costs to cells, enabling continuous feedback and dynamic updates to ensure maximum visibility and efficient path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-robot coverage planning is implemented in unknown and dynamic environments, then comprehensive area coverage is improved, but computational complexity and real-time adaptability deteriorate
Solution Approach 1:
The workspace is divided into discrete cells forming a grid map, transforming the continuous coverage problem into a discrete path planning problem. This segmentation enables efficient computational handling while ensuring complete coverage through systematic cell-by-cell traversal.
Solution Approach 2:
The system pre-computes visibility matrices and sensor observation quality indices for all possible robot poses before actual coverage execution. This preliminary computation stores spatial relationships and sensor characteristics, enabling rapid real-time decision-making during dynamic operation without repeated complex calculations.
2Adaptability or versatility
If real-time sensor data updating is implemented, then adaptability to dynamic obstacles is improved, but processing time and energy consumption increase
Solution Approach 1:
The system continuously updates the occupancy grid map and visibility matrix based on real-time sensor feedback from robots and environmental sensors. This feedback mechanism allows dynamic re-planning and adaptation to new obstacles while maintaining efficient computation through incremental updates rather than complete recalculation.
Solution Approach 2:
Visibility matrices and sensor observation qualities are pre-computed for all possible poses, storing spatial relationships in advance. During real-time operation, the system only needs to query pre-computed data and perform minor updates, significantly reducing processing time compared to calculating visibility from scratch.
3Measurement precision
If optimal pose determination based on visibility matrix is implemented, then sensor observation quality is improved, but computational load increases
Solution Approach 1:
The visibility matrix and sensor observation quality indices are pre-computed for all possible robot poses before coverage execution. This preliminary computation stores the quality metrics for each pose-cell combination, enabling the system to quickly determine optimal poses by simple matrix queries rather than performing complex optimization calculations in real-time.
Solution Approach 2:
The system computes visibility and observation quality for a superset of all possible poses, including poses that may not be ultimately selected. This excessive computation is performed offline and stored, allowing rapid online decision-making with minimal energy consumption during actual coverage operations.
4Reliability
If complete coverage path planning is implemented for all cells, then coverage completeness is improved, but task completion time increases
Solution Approach 1:
The coverage path plan is dynamically adjusted based on real-time sensor data and occupancy updates. The system can modify the traversal sequence, skip already-covered cells, and adapt to dynamic obstacles, maintaining complete coverage while significantly reducing task completion time compared to rigid pre-planned paths.
Solution Approach 2:
The visibility matrix and optimal pose determination are pre-computed, allowing the robot to quickly identify which cells require coverage and from which poses. This preliminary computation enables efficient real-time decision-making about the next target cell and optimal approach pose, accelerating the coverage process.
Data Source
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AI summary
The disclosure generally relates to a method and a system for heterogeneous autonomous mobile robots for coverage path planning. The method may include receiving sensor data from one or more sensor devices. The sensor data includes information corresponding to one or more robots in a predefined region. The method may further include generating a map for the one or more robots based on the received sensor data. The map includes a probable occupancy of each of the plurality of cells by the one or more robots in the predefined region. The method further includes determining a set of poses of the one or more robots based on the generated map and an optimal set of poses from the set of poses based on the visibility matrix. The method may further include generating a coverage path plan for each of the one or more robots based on the determined optimal set of poses.