Autonomous Coverage Path Planning With Replenishment Optimization
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Solution Overview
Problem
Existing technologies lack an efficient method for area coverage planning and replenishment planning for autonomous vehicles, particularly in agricultural and construction applications, where the vehicle needs to traverse a work area while minimizing distance and time, and replenish consumable materials like fertilizer or seeds during operation.
Innovation Solution
A method for area coverage planning that involves receiving information about the boundary and obstacles of a work area, performing multivariate optimization to determine optimal track directions and offsets, and generating a trajectory for the autonomous vehicle to traverse these tracks. Additionally, the method includes replenishment planning by determining logistic points along the trajectory where the vehicle needs to be replenished and generating replenishment trajectories to nearby refill stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional route planning methods are used for autonomous vehicles in agricultural fields, then the vehicle can complete basic coverage tasks, but the total travel distance and time are not optimized, leading to reduced productivity and increased energy consumption
Solution Approach 1:
The system performs preliminary optimization of track directions and offsets before the autonomous vehicle begins its coverage task. By pre-calculating the optimal track layout that minimizes total travel distance and avoids obstacles, the vehicle can execute the planned path efficiently without real-time decision delays, thus improving productivity while reducing time loss.
Solution Approach 2:
The optimization algorithm dynamically adjusts track directions and offsets based on field boundaries, obstacle locations, and vehicle characteristics. This dynamic adaptation ensures that the track layout is optimized for each specific work area, maximizing coverage efficiency and minimizing unnecessary travel distance and time.
2Productivity
If the autonomous vehicle traverses the entire work area to complete coverage tasks, then full area coverage is achieved, but the vehicle consumes consumable materials (fertilizer, seeds) that require replenishment, causing operational interruptions
Solution Approach 1:
The system pre-calculates optimal refill station locations and replenishment timing based on the track layout and vehicle consumption rates. By planning replenishment points in advance along the optimal tracks, the vehicle can refuel or replenish materials with minimal interruption to the coverage task, maintaining high productivity while reducing time loss during replenishment operations.
3Reliability
If the track spacing is reduced to ensure complete area coverage, then coverage completeness is improved, but the total number of tracks increases, leading to longer total travel distance and reduced operational efficiency
Solution Approach 1:
The optimization algorithm dynamically adjusts track spacing parameters based on field characteristics, obstacle locations, and vehicle width. By optimizing the spacing parameter to be neither too wide (which would leave gaps) nor too narrow (which would increase track count excessively), the system achieves complete coverage while minimizing the total number of tracks and total travel distance, thus maintaining both reliability and productivity.
Data Source
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Figure 3A~3B
AI summary
A method of area coverage planning for an autonomous vehicle includes, at a computer system, receiving information of a boundary of a work area, and laying a plurality of tracks within the boundary of the work area. The plurality of tracks is spaced apart from each other by a spacing. Laying the plurality of tracks includes, based on the information of the boundary of the work area, performing a multivariate optimization to: (i) determine an optimal direction of the plurality of tracks, and (ii) an optimal offset for a first track from the boundary, so as to minimize a total distance of the plurality of tracks. The method further includes generating a trajectory that is traversable by the autonomous vehicle to traverse the plurality of tracks.