Robotic Behavior Trees With Data Flow Graph Execution
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Solution Overview
Problem
Current robotics planning requires extensive manual programming, which is time-consuming, error-prone, and lacks efficient data flow control, particularly in high-frequency or high-bandwidth data scenarios.
Innovation Solution
Implementing a robotic system that utilizes data flow graphs to control data flow within a robotic behavior tree, allowing for asynchronous execution and type-checking between nodes, thereby enhancing user friendliness, reducing errors, and increasing flexibility in process execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to control robotic movements, then precise control of robotic components is achieved, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The patent uses behavior trees as a high-level abstraction that copies or represents complex robotic behaviors in a declarative format rather than requiring detailed procedural programming. This allows precise control to be specified through intuitive node definitions rather than lengthy code sequences, reducing programming time while maintaining control precision.
Solution Approach 2:
The patent introduces behavior trees as an intermediary layer between high-level task specifications and low-level robotic control. This mediator automatically translates behavioral descriptions into executable control commands, eliminating the need for manual programming while preserving precise control through the structured node evaluation mechanism.
2Ease of operation
If shared data space is used for data exchange between behavior tree nodes, then data flow is enabled, but errors occur when reading or writing high frequency or high bandwidth data
Solution Approach 1:
The patent segments the data exchange mechanism into separate data flow graph connections rather than using a monolithic shared data space. Each connection between nodes is independently managed with its own data buffer and synchronization protocol, allowing high-frequency data to be handled without interfering with other data streams and preventing race conditions.
Solution Approach 2:
The patent introduces data flow graphs as an intermediary mechanism between behavior tree nodes for data exchange. This mediator provides structured data buffers and synchronization protocols that manage high-frequency data flows reliably, preventing the errors that occur with direct shared memory access while maintaining ease of data flow operations.
3Productivity
If behavior trees control flow of operations at nodes, then robotic process execution is structured, but control of data flow between nodes is not achieved
Solution Approach 1:
The patent merges behavior trees for control flow with data flow graphs for data flow into a unified execution framework. The behavior tree nodes and data flow graph nodes are integrated such that control flow decisions and data flow operations work together seamlessly, allowing structured process execution while enabling explicit data flow control between nodes through the combined architecture.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning and executing robotic processes. One of the methods includes receiving a definition of a robotic behavior tree, receiving a definition of a data flow graph, and executing a robotic process using the definition of the robotic behavior tree and the data flow graph.