Smart Lawnmower Sim-to-Real Mowing Policy for Efficient Coverage
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Solution Overview
Problem
Current robotic smart lawnmowers are inefficient and ineffective in autonomously mowing lawns, relying on randomness and persistence rather than a strategic approach, which has hindered their penetration in the landscaping and groundskeeping markets.
Innovation Solution
A smart lawnmower that utilizes a real-to-sim training phase to construct a simulated environment based on semantic information, including location signalization, and applies a mowing policy to control the cutting and drive subsystems in a sim-to-real mowing phase, ensuring efficient and effective lawn mowing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional random and persistent mowing approach is used, then the lawnmower can operate autonomously, but the mowing efficiency and effectiveness are insufficient
Solution Approach 1:
The system performs preliminary actions by constructing a simulated environment model before actual mowing operations. The real-to-sim training phase creates a digital twin of the lawn environment, allowing the mowing policy to be developed and optimized in advance. This preliminary modeling enables the lawnmower to plan efficient mowing paths and strategies rather than relying on random movement, directly improving productivity while the simulation handles the complexity.
Solution Approach 2:
The simulated environment acts as an intermediary between the complex real-world lawn and the mowing control system. Instead of directly controlling the lawnmower based on complex real-world variables, the system uses the simulated environment as a mediator to process information, develop mowing policies, and generate control commands. This intermediary approach improves mowing efficiency while containing system complexity within the simulation framework.
2Productivity
If a simulated environment and mowing policy are implemented, then mowing efficiency improves, but the system complexity increases
Solution Approach 1:
The system creates a copy of the real-world lawn environment in simulated form. This digital twin or simulated environment replicates the essential features of the actual lawn without requiring complex physical sensors and processors in the real lawnmower. The copying approach allows sophisticated mowing policies to be developed in the simulation while keeping the actual lawnmower hardware relatively simple, thus improving effectiveness without proportionally increasing physical system complexity.
Solution Approach 2:
The system replaces complex mechanical control complexity with computational simulation. Instead of requiring complex mechanical sensors, processors, and control mechanisms in the physical lawnmower, the complexity is shifted to the simulated environment and software algorithms. This substitution improves mowing effectiveness while the physical system remains relatively simple, as the heavy computational work occurs in the virtual simulation rather than in hardware.
3Extent of automation
If real-to-sim training and sim-to-real mowing phases are used, then automated mowing is achieved, but the deployment time and computational resources increase
Solution Approach 1:
The real-to-sim training phase performs preliminary actions by pre-training the mowing policy in the simulated environment before actual deployment. This advance training in virtual space allows the system to develop effective mowing strategies without requiring extensive trial-and-error in the real world, reducing the time loss associated with learning and adaptation during actual mowing operations.
Solution Approach 2:
The system performs self-service by automatically constructing the simulated environment from real-world data and autonomously developing its own mowing policy through the training phases. Rather than requiring manual programming or extensive human setup, the system self-configures and self-trains, reducing the time and resources needed for deployment while achieving high levels of automation.
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.