Robotic Lawn Mower Fleet Charging Using Hierarchical Control
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Solution Overview
Problem
Robotic lawn mowers lack effective communication capabilities for operating in groups, limiting their mowing capacity and efficiency.
Innovation Solution
A method for operating a mowing system involving multiple robotic lawn mowers that includes determining when to charge based on current charge level, location, and occupancy of base stations, utilizing hierarchical schemas and machine learning to optimize operation without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robotic lawn mowers operate independently without communication, then device complexity is reduced, but productivity is limited due to inability to coordinate charging and mowing tasks
Solution Approach 1:
The system segments communication responsibilities by introducing a hierarchical structure where a master controller coordinates multiple slave controllers. Each robotic mower maintains independence in mowing operations while only the master controller manages complex charging coordination across the fleet, reducing individual device complexity while enabling group-level productivity improvements.
Solution Approach 2:
A master robotic mower acts as an intermediary between base stations and slave robotic mowers. The master controller receives charging status information from base stations and distributes coordinated charging commands to slave mowers, mediating the communication burden and enabling multiple mowers to operate efficiently as a group without each unit needing direct complex communication with all others.
2Reliability
If robotic lawn mowers charge immediately when battery level is low, then reliability of continuous operation is improved, but loss of time occurs due to frequent interruptions in mowing tasks
Solution Approach 1:
The system performs preliminary charging actions by having slave robotic mowers return to base stations proactively when their battery levels reach predetermined thresholds before complete depletion. This anticipatory charging approach ensures reliability by preventing operational failures while optimizing the timing to minimize mowing interruptions, as mowers charge in advance rather than waiting for failure conditions.
Solution Approach 2:
Base stations continuously monitor and transmit charging status information to the master controller, which processes this feedback to determine optimal charging schedules. The master controller adjusts charging commands based on real-time battery level feedback from slave mowers and base stations, dynamically balancing reliability requirements against productivity goals by charging only when necessary and coordinating to minimize interruptions.
3Productivity
If a single base station charges one robotic lawn mower at a time, then device complexity is minimized, but productivity is reduced when multiple mowers need charging simultaneously
Solution Approach 1:
The base station system is segmented into multiple independent charging units, each capable of charging one robotic mower simultaneously. This segmentation increases charging throughput to match fleet size while keeping each individual base station unit simple in design, avoiding the need for complex multi-port charging systems. The master controller coordinates which mower charges at which station, managing the distributed infrastructure without requiring complex communication between base stations themselves.
4Productivity
If robotic lawn mowers operate without hierarchical control, then ease of operation is improved, but productivity is limited due to uncoordinated charging and mowing schedules
Solution Approach 1:
Control autonomy is segmented between master and slave robotic mowers. Slave mowers maintain operational independence for mowing tasks, returning to base stations autonomously when needed. The master controller provides high-level coordination for charging schedules and task distribution, allowing slaves to operate autonomously within defined parameters while achieving group-level optimization that individual mowers cannot accomplish alone.
Solution Approach 2:
The master robotic mower serves as an intermediary that coordinates charging schedules and task assignments across the fleet without micromanaging individual operations. It receives status information from base stations and slave mowers, processes this information centrally, and distributes coordinated commands, enabling productive group operation while preserving operational autonomy at the individual unit level.
Data Source
AI summary
A method of operating a mowing system, the method including mowing within a work area, wherein mowing is performed by a robotic lawn mower, and wherein the work area comprises a plurality of base stations each configured to charge the robotic lawn mower; determining when to charge the robotic lawn mower in view of a current charge level and a current location of the robotic lawn mower.