Robot Intelligence Kernel for Dynamic Autonomy and Platform Portability
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Solution Overview
Problem
Current robot architectures lack a generic framework for dynamic autonomy and portable robot intelligence, limiting their ability to make decisions independently and adapt to various platforms and behaviors, relying heavily on human operators for guidance and control.
Innovation Solution
A robot intelligence kernel with a generic robot architecture that provides a framework for dynamic autonomy, enabling robots to adjust motion based on environmental conditions and adapt to different platforms by using a system controller, perceptors, and locomotors, allowing for seamless porting of behaviors across various robot platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic robot architecture framework is implemented, then adaptability across different robot platforms is improved, but device complexity increases due to the need for multiple abstraction layers and porting mechanisms
Solution Approach 1:
The robot architecture is divided into distinct abstraction layers including hardware abstraction layer, robot attribute abstraction layer, and behavior abstraction layer. Each layer handles specific functions and can be independently developed and ported, reducing the complexity burden on any single component while enabling cross-platform adaptability.
Solution Approach 2:
The patent implements universal abstraction layers that can interface with multiple different hardware platforms and robot types. The hardware abstraction layer provides unified access to diverse sensors and actuators, while the behavior abstraction layer enables porting of intelligent behaviors across different robot platforms without modification.
2Extent of automation
If robot intelligence kernel provides dynamic autonomy framework, then extent of automation is improved, but device complexity increases due to additional control systems and decision-making algorithms
Solution Approach 1:
The robot intelligence kernel implements dynamic autonomy where the robot can adjust its level of independence and decision-making capability based on environmental conditions and task requirements. The system dynamically transitions between different autonomy modes, managing complexity by activating only the necessary level of intelligent processing for each situation.
Solution Approach 2:
The patent introduces an intermediary intelligence kernel layer that sits between the hardware abstraction and behavior abstraction layers. This kernel provides standardized interfaces and decision-making frameworks that simplify the implementation of complex autonomous behaviors, acting as a mediator that manages the complexity of automation while providing clean interfaces to higher-level behaviors.
3Adaptability or versatility
If behavior functionality is ported across different robot platforms, then adaptability is improved, but manufacturing precision requirements increase to ensure consistent performance across platforms
Solution Approach 1:
The patent implements parameterized behavior abstractions that can be configured with platform-specific parameters while maintaining the same behavioral logic. By separating the behavioral algorithms from hardware-specific parameters, the system achieves consistent performance across different platforms without requiring identical manufacturing precision, as parameters can be adjusted to account for platform variations.
Data Source
AI summary
Robot platforms, methods, and computer media are disclosed. The robot platform includes perceptors, locomotors, and a system controller, which executes instructions for a robot to follow a target in its environment. The method includes receiving a target bearing and sensing whether the robot is blocked front. If the robot is blocked in front, then the robot's motion is adjusted to avoid the nearest obstacle in front. If the robot is not blocked in front, then the method senses whether the robot is blocked toward the target bearing and if so, sets the rotational direction opposite from the target bearing, and adjusts the rotational velocity and translational velocity. If the robot is not blocked toward the target bearing, then the rotational velocity is adjusted proportional to an angle of the target bearing and the translational velocity is adjusted proportional to a distance to the nearest obstacle in front.