Automatic Mower Path Replanning Around Unknown Obstacles
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Solution Overview
Problem
Existing power machines, such as zero-turn mowers, face challenges in automatically navigating around unknown obstacles during mowing operations, as they rely on pre-defined paths that may not account for dynamic changes in the terrain or presence of unseen objects, leading to inefficiencies and potential damage.
Innovation Solution
Implementing an electronic controller system that uses sensor data to detect unknown objects and dynamically determine new work paths by decomposing the geographical area into cells, determining the shortest path between them, and adjusting the mowing trajectory to avoid obstacles, allowing the machine to continue mowing efficiently and safely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a pre-defined work path is used for automatic mowing, then the mowing operation can be automated and performed without human intervention, but the system cannot adapt to unknown obstacles or dynamic changes in the terrain
Solution Approach 1:
The system dynamically adjusts the work path during mowing operations based on real-time sensor data. The electronic processor continuously receives sensor inputs, detects unknown objects, and modifies the planned path on-the-fly, transitioning from a static pre-defined path to a dynamic adaptive path that responds to environmental changes.
Solution Approach 2:
The system implements a feedback loop where sensor data from the mowing area is continuously fed back to the electronic processor. This feedback mechanism allows the system to detect unknown objects and environmental changes, then adjust the work path accordingly, enabling the automated system to adapt to conditions not present in the original path planning.
2Adaptability or versatility
If the system continuously detects and reroutes around obstacles, then adaptability and safety improve, but computational complexity and processing requirements increase
Solution Approach 1:
The geographical area is decomposed into discrete cells, and the path planning problem is segmented into finding the shortest path between these cells. This segmentation simplifies the computational complexity by breaking down the continuous path planning problem into discrete steps, making real-time obstacle avoidance more manageable.
Solution Approach 2:
The system determines the shortest path only for the remaining unmowed area after detecting an obstacle, rather than recalculating the entire path from scratch. This partial action approach reduces computational requirements by focusing only on the necessary portion of the path that needs adjustment.
3Reliability
If the mowing path is dynamically adjusted to avoid obstacles, then complete coverage of the mowing area can be maintained, but the time required to complete the mowing event may increase
Solution Approach 1:
The system pre-decomposes the geographical area into cells and pre-calculates the shortest path between them before actual mowing begins. This preliminary action prepares the path structure in advance, enabling faster real-time adjustments when obstacles are detected, thereby reducing the time penalty associated with dynamic rerouting.
Solution Approach 2:
When an obstacle is detected, the system recalculates the path only for the remaining unmowed area rather than the entire area. This partial recalculation minimizes the additional time required while still ensuring complete coverage of the remaining mowing area.
Data Source
AI summary
Methods and systems are provided for controlling automatic travel of a power machine, including a mower, along a work path associated with a geographical area. As unknown objects are detected along a current work path, new work paths can be determined for a remaining portion of the geographical area (e.g., an area not yet mowed).