Autonomous Vehicle Path Planning Using Direction and Angle Parameters
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Solution Overview
Problem
Current methods for autonomous vehicle control lack reliable and reproducible motion planning, leading to inefficiencies and potential damage in agricultural and similar applications, as they often require human intervention and result in suboptimal path calculations.
Innovation Solution
A method that determines outer and inner boundaries of a working area, calculates working paths and connecting paths based on direction and angle parameters, allowing for autonomous and unmanned operation by a vehicle, which can perform functions like mowing or ploughing, and includes a robot system for aftermarket customization of existing vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous control methods are implemented, then human intervention is reduced, but path planning reliability and accuracy deteriorate
Solution Approach 1:
The path planning is segmented into distinct components: outer boundary determination, inner boundary determination, direction parameter setting, and angle parameter setting. This segmentation allows each component to be optimized independently while maintaining overall system reliability through modular verification and control.
2Productivity
If complex path calculations are performed autonomously, then operation efficiency improves, but calculation accuracy and reproducibility worsen
Solution Approach 1:
The method performs preliminary actions by determining boundaries and parameters before actual path execution. The outer and inner boundaries are established in advance, along with direction and angle parameters, enabling accurate and reproducible path calculations without requiring complex real-time computations during vehicle operation.
Solution Approach 2:
The invention utilizes parameter changes by systematically varying direction parameters and angle parameters to generate multiple working paths. This allows the system to optimize path arrangements for different working conditions while maintaining calculation accuracy through controlled parameter adjustments rather than complex algorithms.
3Loss of energy
If working paths are optimized for minimal overlap, then resource efficiency improves, but path planning complexity increases
Solution Approach 1:
The method employs curved connecting paths between working paths rather than sharp angular transitions. This curvature approach minimizes overlap and improves resource efficiency while maintaining relatively simple planning logic, as the curved paths naturally optimize the transition zones without requiring complex optimization algorithms.
Data Source
AI summary
Method of autonomous path planning for a vehicle, comprising the steps of a) determining an outer boundary (2) and an inner boundary (4) of a working area (1a) for a vehicle to operate on, b) providing a direction parameter indicating a primary working direction (6) along which the working area (1a) is to be worked on; c) providing an angle parameter indicating an angle (α) between a secondary working direction (8) and the primary working direction (6), wherein the secondary working direction (8) indicates a direction along which a plurality of working paths (10) are to be arranged within the inner boundary (4). The method further comprising the steps of d) calculating the plurality of working paths (10) within the inner boundary (4) based on the direction parameter and the angle parameter; and e) further calculating one or more connecting paths (16) between the outer boundary (2) and the inner boundary (4), each connecting path connecting two subsequent working paths (10).