Method for developing navigation plan in a robotic floor-cleaning device
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Solution Overview
Problem
Existing navigation methods for robotic floor-cleaning devices often result in inefficient paths that do not replicate the user's desired route, particularly in complex environments with cords and fragile objects, lacking the human ability to identify ideal paths.
Innovation Solution
A method that allows users to input and save navigation commands, enabling the robotic device to autonomously re-execute these plans, incorporating user preferences and environmental mapping, with optional suggestions and wireless communication for remote control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic navigation paths are derived by the robotic device, then the device can operate autonomously without user input, but the paths are not efficient or desirable compared to user-selected paths
Solution Approach 1:
The system performs preliminary action by capturing and storing the user's navigation commands during an initial teaching phase. The user manually guides the device through the desired path, and this pre-recorded sequence is saved for future autonomous execution, eliminating the need for the device to autonomously determine paths while preserving user-preferred efficient routes.
Solution Approach 2:
The system uses copying by replicating the user's manual navigation actions. The device records the sequence of motor commands generated during user-guided operation and reproduces this exact sequence during autonomous execution, effectively copying the user's expert path-planning decisions without requiring the device to develop its own navigation intelligence.
2Productivity
If the user manually selects navigation paths, then the paths are efficient and avoid obstacles like cords and fragile furniture, but the device requires continuous user input and cannot operate autonomously
Solution Approach 1:
The user performs the path selection action in advance during a teaching phase, and the system stores this pre-planned navigation sequence. This preliminary action allows the device to operate autonomously later by simply replaying the recorded commands, combining user expertise with autonomous operation.
Solution Approach 2:
The system enables self-service by allowing the device to serve itself through autonomous execution of the recorded navigation plan. After the initial user teaching, the device can independently replay the stored command sequence and perform cleaning tasks without requiring continuous user intervention, making the system both efficient and autonomous.
3Adaptability or versatility
If the robotic device learns navigation plans through repeated user input, then it can adapt to user preferences, but the process requires significant user time and effort for each new environment
Solution Approach 1:
The system copies the user's navigation actions into a stored command sequence during the teaching phase. This one-time copying process captures the user's preferences and environmental knowledge, allowing the device to adapt to new environments without requiring the user to repeatedly input commands for the same path.
Solution Approach 2:
The user performs the adaptation action in advance by teaching the device the desired navigation path once. This preliminary teaching action stores the environmental specifics and user preferences, eliminating the need for repeated user input when executing the same or similar navigation tasks in the future.
Data Source
AI summary
A path planning method for a robotic floor-cleaning device in which a user's commands are repeated autonomously by the robotic floor-cleaning device at a later time.