Autonomous Drone Alignment Using Voice Commands and Feedback Learning
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Solution Overview
Problem
Existing remote device control systems are slower and less precise when responding to voice commands compared to manual input methods, limiting their effectiveness in real-time and autonomous operations.
Innovation Solution
The implementation of an adjustable control system for autonomous devices that utilizes voice commands, incorporating machine learning models to interpret user utterances, determine behavior commands, and adjust actions based on real-time feedback, allowing devices to perform complex tasks autonomously and improve their performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice commands are used for remote device control, then ease of operation is improved, but precision and speed of response deteriorate compared to manual input methods
Solution Approach 1:
The patent introduces an autonomous device as an intermediary between the user and the remotely controlled device. The autonomous device receives voice commands, processes them locally using machine learning models, and executes actions without requiring continuous manual control. This intermediary role enables voice-based operation while maintaining precision through the autonomous device's environmental sensing and decision-making capabilities.
Solution Approach 2:
The patent replaces manual mechanical control systems (joysticks, buttons, mice) with voice-based acoustic input. The voice command processing system substitutes the mechanical interaction paradigm with acoustic signal processing, using machine learning models to interpret spoken instructions and translate them into device control actions, thereby improving ease of operation.
2Ease of operation
If voice commands are used for remote device control, then ease of operation is improved, but speed of response deteriorates compared to manual input methods
Solution Approach 1:
The autonomous device performs preliminary actions by continuously monitoring its environment and pre-processing sensor data before commands are needed. The machine learning models are pre-trained and ready to execute decisions immediately upon receiving voice commands. This preliminary preparation eliminates processing delays, enabling fast response times despite the additional layer of autonomous decision-making.
Solution Approach 2:
The autonomous device serves itself by independently processing environmental information and making control decisions without requiring manual intervention. The device uses its onboard sensors and machine learning models to autonomously interpret situations and execute appropriate actions, significantly reducing the time between command issuance and action execution compared to traditional remote control systems.
3Adaptability or versatility
If machine learning models are used to interpret voice commands and determine behavior, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the control system into distinct functional modules: voice command processing, environmental sensing, machine learning model execution, and actuator control. Each module operates independently but contributes to the overall autonomous behavior. This segmentation manages complexity by organizing functions into manageable components while maintaining high adaptability through the machine learning layer.
Solution Approach 2:
The autonomous device employs universal machine learning models that can handle multiple types of commands and environmental situations with a single integrated system. Rather than requiring separate specialized systems for different tasks, the machine learning model provides multi-functional capability, adapting to various voice commands and environmental conditions while managing complexity through a unified approach.
Data Source
AI summary
Embodiments provide for autonomous drone play and directional alignment by in response to receiving a command for a remotely controlled device to perform a behavior, monitoring a first series of actions performed by the remotely controlled device that comprise the behavior; receiving feedback related to how the remotely controlled device performs the behavior, wherein the feedback is received from at least one of a user, a second device, and environmental sensors; updating, according to the feedback, a machine learning model used by the remotely controlled device to produce a second, different series of actions to perform the behavior; and in response to receiving a subsequent command to perform the behavior, instructing the remotely controlled device to perform the second series of actions.