Autonomous Mower Route Planning for Energy-Efficient Area Coverage
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Solution Overview
Problem
Autonomous maintenance robots face inefficiencies in route planning for complete coverage of large areas, particularly in outdoor environments like renewable energy farms, where existing methods such as SLAM and boundary wires are limited by feature scarcity and impractical for large, remote installations.
Innovation Solution
An autonomous mowing system that uses path data to determine candidate routes with the lowest total estimated energy draw, generating mow paths based on geometric shapes and configuration data to ensure complete coverage, and optimizing routes for energy efficiency by selecting the least cost route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If SLAM techniques are used for autonomous vehicle navigation, then the vehicle can generate maps and localize itself, but the system fails in outdoor environments with few features
Solution Approach 1:
The system segments the navigation problem into two independent parts: (1) GPS-based global positioning and route following for overall navigation, and (2) computer vision-based obstacle detection and avoidance for local safety. This segmentation allows each subsystem to operate in its optimal environment, with GPS providing reliable location data in open outdoor spaces and vision systems handling local obstacle detection where GPS signals may be insufficient.
Solution Approach 2:
The system introduces GPS as an intermediary between the map data and the vehicle's position, and between the planned route and the actual navigation. GPS coordinates serve as a common reference frame that bridges the gap between the pre-planned route (in global coordinates) and the vehicle's local perception and control systems, enabling seamless integration of global navigation and local obstacle avoidance.
2Reliability
If boundary wires are installed to define mowing areas, then SLAM efficacy is improved, but the system becomes impractical for large remote areas
Solution Approach 1:
The system replaces the mechanical boundary wire system with an electronic/GPS-based virtual boundary system. Instead of physical wires that emit electromagnetic signals, the system uses GPS coordinates to define virtual boundaries and routes. This substitution eliminates the need for physical installation of wires while maintaining the ability to define and enforce mowing area boundaries through software-based geofencing and route planning.
Solution Approach 2:
The GPS-based route following system serves multiple functions simultaneously: it defines mowing area boundaries, provides navigation guidance, enables route optimization, and allows easy reconfiguration of mowing patterns. This multi-functional approach replaces the single-purpose boundary wire system while adding capabilities for efficient route planning and adaptive navigation.
3Ease of operation
If manual route training is used, then the autonomous vehicle can follow recorded routes, but the process is extremely time consuming
Solution Approach 1:
The system performs preliminary route planning using GPS coordinates and map data before the vehicle begins operation. Routes are pre-calculated based on the maintenance area boundaries, vehicle capabilities, and optimization criteria. This preliminary computational action eliminates the need for time-consuming manual training, as the vehicle receives ready-to-execute route instructions directly from the control system.
Solution Approach 2:
The control system automatically generates optimized routes based on input parameters such as maintenance area boundaries and vehicle specifications. The system serves itself by computationally determining the most efficient paths without requiring human operators to manually drive and record routes. This self-service capability significantly reduces setup time and allows rapid adaptation to different maintenance areas.
4Ease of operation
If operator-selected routes are used, then the vehicle can be trained to follow paths, but the routes may not be optimal for efficiency
Solution Approach 1:
The system optimizes route parameters such as path spacing, overlap percentage, and turning angles to maximize mowing efficiency. By computationally adjusting these parameters based on the vehicle's cutting width, speed, and the maintenance area geometry, the system generates routes that minimize travel distance and maximize productive mowing time, rather than relying on operator intuition or trial-and-error approaches.
Data Source
AI summary
An autonomous mowing system comprising: a memory configured to hold a set of path data defining a set of paths, the set of paths comprising a transit path to a maintenance area and a set of mow paths to cover the maintenance area; a processor coupled to the memory; a computer-readable medium coupled to the processor, the computer-readable medium storing a set of instructions executable by the processor, the set of instructions comprising instructions for: using the set of path data, determining a plurality of candidate routes that traverse the transit path and fully cover the maintenance area and a total cost for each of the plurality of candidate routes; determining a least cost route from the plurality of candidate routes; and configuring an autonomous mower to follow the least cost route to mow the maintenance area.