Robot Control Parameter Interpolation for Sensor-Responsive Motion
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Solution Overview
Problem
Traditional open-loop real-time robot control systems struggle to integrate sensor feedback and are incompatible with sophisticated control logic due to tight timing constraints and non-deterministic sensor inputs, limiting their ability to perform complex tasks with precision and reliability.
Innovation Solution
A real-time bridge system that generates interpolated control parameters allows robots to incorporate both real-time and non-real-time sensor information, enabling more natural and fluid reactions by translating non-real-time commands into real-time control commands, thereby overcoming the limitations of traditional open-loop control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If open-loop real-time control is used, then precise pre-planned movements are achieved, but sensor feedback integration becomes difficult
Solution Approach 1:
The control system is segmented into distinct layers: a real-time control layer that handles precise motor control and a non-real-time layer that processes sensor feedback and sophisticated control logic. This segmentation allows each layer to operate independently with its own timing requirements, resolving the contradiction between precise pre-planned movements and sensor feedback integration.
Solution Approach 2:
A real-time bridge acts as an intermediary between the non-real-time control logic and the real-time controller. The bridge translates non-real-time commands into real-time executable instructions, enabling sensor feedback to influence robot behavior without compromising the deterministic timing of motor control operations.
2Reliability
If tight timing constraints are enforced, then real-time control is maintained, but sophisticated control logic cannot be computed
Solution Approach 1:
The control architecture segments computation into real-time and non-real-time portions. Sophisticated control logic such as reinforcement learning and neural network inference runs in the non-real-time layer without timing constraints, while only essential real-time control parameters are enforced by the real-time controller, maintaining both reliability and computational flexibility.
Solution Approach 2:
Complex control decisions are computed in advance in the non-real-time layer before execution. This preliminary computation allows sophisticated algorithms to generate control commands that are then translated and executed by the real-time controller, ensuring that complex logic does not interfere with real-time performance.
3Adaptability or versatility
If non-real-time commands are used, then sophisticated control logic is enabled, but real-time responsiveness is reduced
Solution Approach 1:
The real-time bridge serves as a mediator that translates non-real-time commands into real-time executable instructions. This translation layer enables sophisticated control logic to be expressed in flexible non-real-time terms while ensuring that the actual motor control operations occur with the required real-time responsiveness.
Solution Approach 2:
The system changes the timing parameters of command execution based on the control layer. Non-real-time commands specify desired behavior with flexible timing, while the real-time bridge converts these into time-critical parameters that the real-time controller can execute with precise timing, achieving both flexibility and responsiveness.
4Reliability
If deterministic control is maintained, then real-time reliability is ensured, but sensor integration becomes difficult
Solution Approach 1:
The control system segments deterministic and non-deterministic operations into separate layers. The real-time control layer maintains strict determinism for motor control, while the non-real-time layer handles non-deterministic sensor inputs and processing, allowing both deterministic reliability and sensor feedback capability to coexist.
Solution Approach 2:
The real-time bridge acts as an intermediary that filters and translates non-deterministic sensor-derived commands into deterministic real-time control parameters. This mediation preserves the deterministic nature of motor control while enabling the system to respond to non-deterministic sensor feedback.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing interpolated robot control parameters. One of the methods includes receiving, by a real-time bridge from a control agent for a robot, a non-real-time command for the robot, wherein the non-real-time command specifies a trajectory to be attained by a component of the robot and a target value for a control parameter, wherein the control parameter controls how a real-time controller will cause the robot to react to one or more external stimuli encountered during a control cycle of the real-time controller. The real-time bridge provides the one or more real-time commands translated from the non-real-time command and interpolated control parameter information to the real-time controller, thereby causing the robot to effectuate the trajectory of the non-real-time command according to the interpolated control parameter information.