Multi-Robot Control Interface With Intelligence Kernel
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robot architectures lack a scalable framework for generic robot attributes and dynamic autonomy, making it difficult to port behaviors across different robot platforms and requiring continuous human guidance, which limits their ability to make decisions independently.
Innovation Solution
A multi-robot control interface and robot intelligence kernel that provides a framework for dynamic autonomy, enabling seamless porting of behavioral intelligence across various robot platforms through a generic robot architecture with hardware and behavioral abstractions, allowing for adjustable autonomy levels and improved interaction with diverse robot behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic robot architecture with hardware abstractions is implemented, then adaptability across different robot platforms is improved, but device complexity increases due to the need for multi-layer abstraction frameworks
Solution Approach 1:
The robot architecture is segmented into distinct hierarchical layers: hardware abstraction layer, behavior abstraction layer, and intelligence kernel layer. Each layer handles specific functions independently, allowing the system to adapt to different hardware platforms without redesigning the entire architecture. The hardware abstraction layer specifically isolates platform-specific details, enabling portable behavior implementations across diverse robot platforms.
Solution Approach 2:
The intelligence kernel is designed as a universal component that provides core decision-making and behavior management capabilities applicable across multiple robot platforms. By creating a platform-independent intelligence kernel that interfaces with hardware abstractions, the system achieves multi-functionality where the same kernel can control different robot types through standardized interfaces while maintaining adaptability to specific platform requirements.
2Reliability
If continuous human guidance is provided to control robots, then reliability of robot operation is improved, but loss of time increases due to constant human oversight requirements
Solution Approach 1:
The robot system incorporates self-service capabilities through the intelligence kernel that enables autonomous decision-making and self-correction. The kernel monitors robot status, detects anomalies, and implements corrective behaviors without requiring continuous human intervention. This self-service mechanism maintains operational reliability by handling routine decisions and error recovery independently, freeing human operators from constant oversight while preserving system reliability.
Solution Approach 2:
The architecture implements continuous feedback loops where sensor data flows to the intelligence kernel, which processes information and generates control commands. The kernel uses feedback from hardware abstractions to autonomously adjust robot behavior, maintain safety constraints, and recover from errors. This automated feedback mechanism reduces the need for human oversight while maintaining reliable operation through real-time monitoring and adjustment.
3Productivity
If multiple operators are assigned per robot for navigation and obstacle avoidance, then productivity of complex tasks is improved, but device complexity increases due to multiple control interfaces
Solution Approach 1:
Multiple operator functions are merged into a single unified control interface that manages collaborative human-robot teamwork. The intelligence kernel consolidates navigation, obstacle avoidance, and task execution controls into one interface where operators can issue high-level commands while the kernel handles low-level coordination. This merging approach enables multiple operators to work together on complex tasks without requiring separate control systems for each function, maintaining productivity while reducing overall control complexity.
4Ease of manufacture
If robot intelligence is implemented as a collection of programmed behaviors, then ease of manufacture is improved, but adaptability decreases because behaviors cannot be easily ported across different platforms
Solution Approach 1:
Hardware abstractions serve as an intermediary layer between platform-specific hardware and platform-independent behavior implementations. The abstractions provide standardized interfaces that shield behavior code from hardware variations, allowing behaviors developed for one platform to be ported to other platforms through the same abstraction interface. This intermediary mechanism maintains ease of behavior implementation while enabling cross-platform adaptability by decoupling behavior logic from hardware specifics.
Data Source
AI summary
Methods and systems for controlling a plurality of robots through a single user interface include at least one robot display window for each of the plurality of robots with the at least one robot display window illustrating one or more conditions of a respective one of the plurality of robots. The user interface further includes at least one robot control window for each of the plurality of robots with the at least one robot control window configured to receive one or more commands for sending to the respective one of the plurality of robots. The user interface further includes a multi-robot common window comprised of information received from each of the plurality of robots.