Multi-Robot Coverage Path Planning With Dynamic Occupancy Mapping
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Solution Overview
Problem
Existing solutions for multi-robot coverage path planning in unknown and dynamic environments are limited and less advanced, making it challenging to efficiently cover all areas while avoiding obstacles and updating maps dynamically.
Innovation Solution
A processor-implemented method for autonomous mobile robots that involves receiving sensor data, generating a map of probable occupancy, determining a set of poses based on a visibility matrix, and optimizing these poses to create an optimal coverage path plan, ensuring high sensor visibility confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional coverage path planning methods are used in static environments, then path planning can be completed using known environmental information, but the system cannot adapt to unknown and dynamic environments where environmental information changes in real-time
Solution Approach 1:
The patent implements dynamic coverage path planning by continuously updating the environmental map as robots move and collect new sensor data. The system transitions from static pre-planned paths to dynamic replanning that adapts to changing environments, allowing robots to respond to newly discovered obstacles and open spaces in real-time
Solution Approach 2:
The system employs feedback mechanisms where sensor data from robot cameras and sensors continuously updates the environmental map, which then feeds back into the path planning algorithm. This closed-loop approach allows the system to learn from past movements and improve future path decisions based on accumulated environmental knowledge
2Productivity
If multiple robots are deployed for coverage planning, then coverage efficiency can be improved, but coordination and communication between robots increases system complexity
Solution Approach 1:
The patent merges the individual maps and sensor data from multiple robots into a single unified environmental representation. By combining information from all robots rather than treating them as independent agents, the system reduces coordination overhead while maintaining the productivity benefits of multi-robot deployment
Solution Approach 2:
The system implements a universal path planning framework that handles both single-robot and multi-robot scenarios through the same core algorithms. The visibility matrix and coverage optimization methods work equally well whether one robot or multiple robots are performing the coverage task, simplifying the overall system architecture
3Reliability
If complete coverage of the workspace is achieved, then all areas are traversed including obstacles, but task completion time increases due to the need to visit every location
Solution Approach 1:
The patent applies local quality optimization by adjusting path planning density based on local environmental characteristics. High-priority areas that require complete coverage receive more detailed path planning, while low-priority open areas use coarser coverage patterns, reducing overall traversal time while maintaining reliability where needed
Solution Approach 2:
The system uses partial coverage strategies where robots focus on covering only the necessary portions of the workspace based on task requirements. Rather than uniformly traversing every possible location, the algorithm identifies and covers only the essential areas, reducing redundant movements and completing tasks faster
4Reliability
If robots navigate to cover every cell in the workspace, then complete coverage is achieved, but motion costs and energy consumption increase
Solution Approach 1:
The patent uses preliminary visibility analysis to pre-calculate which workspace cells are visible from potential robot positions before actual movement occurs. By planning paths that maximize visibility coverage from each position, the system reduces the total number of movement actions required while ensuring complete coverage of all necessary areas
Solution Approach 2:
The system maintains continuous useful action by ensuring that each robot movement contributes to coverage of previously unvisited areas. The visibility matrix guides robots to positions where their sensors will observe new cells, eliminating redundant movements and ensuring that every unit of energy expended results in productive coverage advancement
Data Source
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.