Smart Lawnmower Simulation Training for Efficient Mowing Control
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Solution Overview
Problem
Existing robotic smart lawnmowers are inefficient and ineffective in landscaping and groundskeeping due to their reliance on randomness and persistence, failing to penetrate the market.
Innovation Solution
A smart lawnmower that constructs a simulated environment based on real-world data using semantic information and applies a mowing policy to control cutting and drive subsystems, synchronized with real-world inputs, enabling efficient and effective lawn mowing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic smart lawnmowers use randomness and persistence to mow grass, then they can operate autonomously, but their efficiency and effectiveness are insufficient
Solution Approach 1:
The patent creates a simulated environment that copies the real-world lawn geometry and features. The robotic lawnmower is trained in this virtual replica to learn optimal mowing policies without risking damage in the real world. This allows the system to achieve high productivity through learned intelligent behavior while maintaining autonomous operation.
Solution Approach 2:
The system performs preliminary training in the simulated environment before deploying the lawnmower to the real lawn. By pre-learning mowing strategies, path planning, and obstacle avoidance in virtual space, the lawnmower arrives at the real world with pre-established knowledge, eliminating the need for random exploration and significantly improving mowing efficiency from the start.
2Productivity
If a simulated environment is constructed using semantic information and location signalization, then mowing policy can be applied for efficient control, but device complexity increases
Solution Approach 1:
The patent introduces a simulated environment as an intermediary between the simple physical lawnmower and the complex task of intelligent mowing. This virtual intermediate layer handles the complexity of learning, policy development, and path planning, while the physical lawnmower remains relatively simple in structure. The simulation acts as a mediator that translates real-world geometry into learnable virtual representations.
Solution Approach 2:
The patent replaces complex mechanical decision-making and path-planning mechanisms with a software-based simulated environment and machine learning policies. Instead of implementing complex mechanical sensors and actuators for navigation, the system uses virtual reality modeling and computational algorithms to achieve intelligent control, substituting mechanical complexity with informational processing.
Data Source
AI summary
A smart lawnmower and system and method for use of the same are disclosed. In one embodiment of the smart lawnmower, in a real world-to-simulated world (“real-to-sim”) training phase, the smart lawnmower constructs a simulated environment corresponding to a mowing-relevant portion of a real-world environment relative to semantic information, which may include received location signalization at an antenna In a simulated world-to-real world (“sim-to-real”) mowing phase, a mowing policy is applied to control the cutting subsystem and the drive subsystem in response to the semantic information, which may include the location signalization. In each of the real-to-sim training phase and the sim-to-real mowing phase, the smart lawnmower may provide a user interface including the simulated environment. Further, in the sim-to-real mowing phase, the smart lawnmower may synchronize the real world and the simulated world.