Robotic Soil Cultivation Path Planning Using Soil Grid Clustering
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Solution Overview
Problem
Existing robotic vehicles for soil cultivation face inefficiencies in operation due to the need for precise positioning and navigation in diverse soil conditions and obstacles, leading to suboptimal energy use and incomplete coverage of work areas.
Innovation Solution
A robotic vehicle system that uses a controller with memory and processor to plan movable operations by clustering discretized coordinate grid points based on soil parameters, optimizing movement trajectories to ensure efficient cultivation only where necessary, and employing GNSS, optical, and acoustic systems for accurate location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robotic vehicle operates in areas with different soil conditions or obstacles, then the adaptability to diverse environments is improved, but the navigation complexity and positioning precision requirements increase
Solution Approach 1:
The work area is divided into discrete grid points that can be independently processed and clustered. This segmentation allows the vehicle to handle diverse soil conditions in different regions without requiring complex global navigation planning, as each grid point can be evaluated and processed separately based on local soil parameters.
Solution Approach 2:
The system applies different processing strategies to different grid points based on local soil conditions. By clustering grid points with similar soil parameters and applying location-specific cultivation operations, the system adapts to diverse environments without requiring uniform complex navigation for the entire area.
2Productivity
If the robotic vehicle performs soil cultivation in all grid points, then the coverage completeness is improved, but the energy consumption increases
Solution Approach 1:
The system evaluates soil parameters at each grid point and applies cultivation operations only where necessary. By clustering grid points with similar soil characteristics and comparing them against target parameters, the vehicle performs localized cultivation only in areas that require it, maintaining complete coverage of necessary areas while avoiding unnecessary energy expenditure in already-suitable regions.
Solution Approach 2:
Instead of uniformly cultivating all grid points, the system applies partial action by selectively processing only those grid points that deviate from target soil parameters. This approach ensures complete coverage of areas needing treatment while avoiding excessive energy consumption from unnecessary operations in already-compliant areas.
3Productivity
If the robotic vehicle uses clustering of grid points based on soil parameters, then the operation efficiency is improved, but the measurement precision requirements for soil parameters increase
Solution Approach 1:
The system measures soil parameters at discrete grid points rather than continuously across the entire area. This segmented measurement approach improves operation efficiency by reducing the total number of measurements required, while the clustering process groups these discrete measurements to represent broader areas, effectively managing the precision requirements through statistical aggregation.
Solution Approach 2:
The system merges multiple grid points with similar soil parameters into clusters, where a single measurement or average representation can suffice for the entire cluster. This merging reduces the total number of precise measurements needed, as one measurement can represent multiple grid points within a cluster, thereby improving operation efficiency while maintaining adequate measurement precision through aggregation.
4Loss of energy
If the robotic vehicle optimizes movement trajectories based on clustered grid points, then the energy consumption is reduced, but the navigation system complexity increases
Solution Approach 1:
The navigation system plans trajectories between clustered grid points rather than individual points, segmenting the navigation problem into larger, more manageable units. This reduces the total number of navigation decisions required, optimizing energy consumption by minimizing unnecessary movements while keeping the navigation system relatively simple by working with clustered targets rather than individual grid points.
Solution Approach 2:
The system merges multiple grid points into clusters and plans trajectories to these clusters rather than to each individual point. This merging approach reduces energy consumption by allowing more flexible, efficient path planning that can traverse multiple clustered points in a single trajectory, while simultaneously reducing navigation system complexity by decreasing the total number of destination targets from individual grid points to clustered groups.
Data Source
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AI summary
The invention relates to a robotic vehicle (100) for movable operation in a work area (108), the movable operation comprising a soil cultivation, the work area (108) being represented by a set of discretized coordinate grid points (112), the vehicle (100) comprising a controller (118), the controller (118) comprising a memory (122) and a processor (120), the memory (122) comprising instructions (124), wherein execution of the instructions (124) by the processor (120) causes the vehicle (100) for planning the movable operation, the planning comprising clustering of at least some of the grid points (112), wherein the clustering results in sets (1302) comprising some of the grid points (112), wherein for all grid points (112) in the set a value of a parameter characterizing the soil represented by said grid points (112) satisfies a soil criterion for performing the movable operation, wherein execution of the instructions (124) by the processor (120) further causes the vehicle (100) for performing the movable operation based on the sets (1302) of grid points (112).