Multiple Mowing Robots With UAV Mapping And Hungarian Assignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mowing robots operate independently, leading to low energy utilization, high maintenance costs, and limited efficiency due to ineffective handling of varying terrains and vegetation states.
Innovation Solution
A collaborative operation method for multiple mowing robots that involves data acquisition, multi-region segmentation, and intelligent task assignment using UAVs and a Hungarian algorithm to optimize energy and resource utilization, considering environmental and mechanical states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple mowing robots operate independently, then each robot can function autonomously, but energy utilization rate is low and maintenance cost is high
Solution Approach 1:
The patent combines multiple independent mowing robots into a collaborative system where they share task information and coordinate operations. The central control server aggregates state data from multiple robots and assigns tasks optimally, merging their capabilities to improve overall energy utilization while maintaining individual autonomy for execution.
Solution Approach 2:
The system creates a universal task assignment framework that can allocate different types of tasks to different robots based on their current state and capabilities. The Hungarian algorithm provides a multi-functional optimization approach that considers various factors (energy, position, task type) to assign tasks universally across the robot fleet, maximizing overall system efficiency.
2Ease of operation
If multiple mowing robots operate independently, then operational simplicity is maintained, but mowing efficiency is limited
Solution Approach 1:
The system implements dynamic task assignment where task allocation is not fixed but continuously adjusted based on real-time state data from robots (position, energy level, current task progress). The Hungarian algorithm dynamically recalculates optimal assignments as conditions change, enabling the system to adapt and maximize mowing efficiency while maintaining operational simplicity through automated decision-making.
3Device complexity
If traditional task assignment is used without optimization, then system complexity is low, but task completion time increases
Solution Approach 1:
The patent introduces a central control server as an intermediary between robots and tasks. This mediator collects state data from all robots, processes it through the Hungarian algorithm for optimal task assignment, and distributes tasks accordingly. While this adds system complexity, it dramatically reduces task completion time by ensuring optimal allocation rather than random or sequential assignment.
Solution Approach 2:
The system performs preliminary optimization by pre-calculating optimal task assignments using the Hungarian algorithm before robots execute tasks. By solving the assignment problem in advance based on current system state, the system prepares optimal routes and task allocations, reducing the time robots spend on non-optimal paths and improving overall task completion speed.
4Device complexity
If robots operate without collaborative task assignment, then energy consumption monitoring is simple, but energy waste increases
Solution Approach 1:
The system implements feedback mechanisms where robots continuously report their state data (energy levels, position, task progress) to the central control server. The Hungarian algorithm uses this feedback to dynamically adjust task assignments, assigning tasks to robots that can complete them most efficiently. This feedback loop prevents energy waste by avoiding assignments to robots that are already low on energy or poorly positioned, while the monitoring complexity remains manageable through automated data collection and processing.
Data Source
AI summary
Provided are a collaborative operation method for multiple mowing robots, a device and a product, which relate to the field of collaborative operation of robots. The collaborative operation method for multiple mowing robots includes: acquiring state data of a mowing robot, environment and operation region data, and task execution data; establishing, according to the environment and operation region data, the state data of the mowing robot, state data of an unmanned aerial vehicle (UAV), starting point information of the mowing robot, and starting point information of the UAV, a complete map information with a traveling-salesman path method; performing multi-region segmentation according to the complete map information; determining costs of different tasks according to segmented regions, the state data of the mowing robot and the task execution data, and determining an optimal cost solution with a Hungarian algorithm.