Autonomous Lawn Mower Navigation for Multi-Mower Efficiency
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Solution Overview
Problem
Commercial lawn mowing operations are labor-intensive and costly due to the need for multiple operators to manually control lawn mowers, which increases expenses for landscaping companies.
Innovation Solution
The development of autonomous lawn mowers equipped with sensors, such as cameras, radar, and IMUs, that can autonomously navigate and mow lawns by generating mow patterns and optimizing their behavior within defined boundaries, allowing for choreographed operation of multiple mowers to reduce labor and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple operators are used to manually control lawn mowers, then the lawn can be mowed, but labor costs increase significantly
Solution Approach 1:
The lawn mower is equipped with autonomous navigation capabilities including sensors, processors, and control systems that enable it to navigate, mow, and return to charging stations without human intervention. The system independently performs boundary detection, obstacle avoidance, mow pattern generation, and fleet coordination, effectively making the mower self-sufficient and eliminating the need for operators.
Solution Approach 2:
Manual mechanical control by operators is replaced with electronic and computational systems. The patent implements sensor arrays (cameras, LIDAR, GPS), onboard processors, and wireless communication modules that collectively substitute human operators, enabling autonomous navigation and mowing operations through electronic control rather than mechanical steering.
2Extent of automation
If autonomous navigation systems with sensors are added to lawn mowers, then labor costs are reduced, but device complexity increases
Solution Approach 1:
The autonomous mower system is designed as a multi-functional integrated platform that simultaneously performs navigation, obstacle detection, boundary recognition, mowing control, wireless communication, and autonomous charging. The controller executes multiple functions including generating mow patterns, coordinating with fleet mates, and managing power consumption, consolidating what could be separate systems into a unified autonomous operation platform.
Solution Approach 2:
The autonomous navigation and control system is divided into modular functional components: sensor modules (cameras, LIDAR, GPS receivers), processing modules (onboard processors running navigation algorithms), actuation modules (wheel control, blade control), and communication modules (wireless transceivers). This segmentation allows each component to be optimized independently while working together as an integrated autonomous system.
3Productivity
If multiple autonomous mowers operate together, then mowing speed and efficiency improve, but coordination complexity increases
Solution Approach 1:
The fleet coordination system continuously exchanges position, status, and environmental data among autonomous mowers and a central management system. Each mower receives real-time feedback about fleet mate locations and adjusts its mow pattern accordingly, while the central system monitors overall progress and redistributes workloads dynamically, creating a coordinated multi-agent system that optimizes collective mowing efficiency.
Solution Approach 2:
Before mowing operations begin, the system performs preliminary fleet coordination by assigning initial mow patterns, establishing communication protocols, and pre-coordinating boundary regions among multiple mowers. The central management system pre-processes lawn area data and generates initial task allocations, allowing mowers to start operations with pre-established coordination frameworks rather than negotiating in real-time.
Data Source
AI summary
An autonomous lawn mower is described which is provided with boundary information of a region, explores the region, and based on information collected while exploring, is configured to mow the region in accordance with a mow pattern. Exploration may be performed based on random motions, striping, etc. Sensor data captured during exploration is captured in order to determine the presence of any objects within the region (e.g., trees, manmade objects, lakes, and the like). Sensor data and boundary information is used to optimize a mow pattern for the lawn mower to follow when mowing the region. Additional sensor data captured while mowing may be used for obstacle avoidance, monitoring of the system, or otherwise generating notifications.