Area Coverage Trajectory Planning With Refill Point Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle navigation systems for agricultural and construction tasks are inefficient due to excessive non-productive time spent on maneuvering and servicing, particularly in fields with irregular shapes and obstacles, and lack integrated replenishment planning for consumable materials.
Innovation Solution
A method for area coverage planning that involves multivariate optimization to determine optimal track directions and offsets within a work area, incorporating obstacle avoidance and static obstacles, while also integrating replenishment planning by calculating logistic points and generating trajectories for autonomous vehicles to efficiently traverse the area and replenish materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation planning algorithms are used for autonomous vehicles, then the vehicle can traverse the work area, but excessive non-productive time is spent on maneuvering and servicing
Solution Approach 1:
The system performs preliminary calculations of material consumption rates and pre-determines optimal refill points along the coverage path before the vehicle begins operation. This allows the vehicle to plan refill stops in advance rather than making unplanned maneuvers, reducing non-productive time spent on impromptu servicing operations
Solution Approach 2:
The path planning algorithm acts as an intermediary that integrates both coverage requirements and material replenishment needs into a unified trajectory. By mediating between the vehicle's operational path and refill station locations, the system optimizes the sequence of operations to minimize total non-productive time
2Productivity
If the autonomous vehicle traverses the entire work area with sufficient material, then complete coverage is achieved, but the vehicle weight increases and fuel consumption rises
Solution Approach 1:
The system pre-calculates the optimal amount of material to carry based on the planned coverage area and identifies strategic refill points along the path. This allows the vehicle to carry only the necessary amount of material for each segment rather than loading excessive reserves, reducing overall fuel consumption while ensuring complete coverage
Solution Approach 2:
The system dynamically adjusts the material load parameter along the operational path by identifying optimal refill points where the vehicle can replenish supplies. This parameter change strategy allows the vehicle to maintain minimal necessary material levels during traversal, optimizing the balance between coverage completeness and energy consumption
3Ease of operation
If refill stations are placed throughout the work area, then replenishment is convenient, but the system complexity and infrastructure requirements increase
Solution Approach 1:
Instead of uniformly distributing refill stations throughout the work area, the system identifies specific local positions along the coverage path where refill operations are most beneficial. These localized refill points are determined based on material consumption rates and path geometry, providing adequate replenishment accessibility without requiring extensive infrastructure
4Productivity
If the vehicle carries large amounts of material to cover the entire area, then complete coverage is ensured, but the vehicle requires larger capacity and higher fuel consumption
Solution Approach 1:
The system pre-calculates the optimal material distribution strategy by analyzing the coverage path and identifying where refill operations should occur. This preliminary planning allows the vehicle to carry smaller amounts of material for each segment rather than loading the entire required quantity at once, reducing the necessary carrying capacity while ensuring complete coverage through strategic replenishment
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A method of area coverage planning with replenishment planning includes receiving information of a boundary of the work area, location information of one or more refill stations, and information of a current amount of the material left in the autonomous vehicle, laying a plurality of tracks within the boundary of the work area so as to minimize a total distance of the plurality of tracks, generating a coverage trajectory, and based on (i) the coverage trajectory, (ii) the location information of the one or more refill stations, (iii) the current amount of the material left in the autonomous vehicle, and (iv) a nominal full amount and a nominal consumption rate of the material by the autonomous vehicle, determining one or more logistic points along the coverage trajectory at which a remaining amount of the material reaches a threshold, for each logistic point, generating a replenishment trajectory.