Hybrid Robot Control for Compliance and Trajectory Correction
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Solution Overview
Problem
Traditional robotic systems struggle with inaccurate position and force control due to environmental changes and compliance issues, leading to task failures and potential damage, with current remedies being environment-specific and ineffective in all scenarios.
Innovation Solution
A robotic system with hybrid force and position control, utilizing sensors to measure actual forces and positions, a controller to determine corrections, and a common reference frame for dynamic pose correction, enabling adaptive control to modify trajectories in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional position control is used, then the robot follows predefined trajectories, but position accuracy deteriorates due to compliance in mechanical linkages and non-rigid connections
Solution Approach 1:
The system dynamically changes control parameters by switching between position control and force control modes based on task requirements and environmental conditions. The hybrid controller adjusts the weighting between position and force control outputs in real-time to compensate for compliance effects, thereby maintaining position accuracy despite non-rigid connections.
Solution Approach 2:
The system uses force sensors to measure actual forces at the end effector and position sensors to monitor actual positions. This feedback is fed into the hybrid controller which compares actual states with desired states and generates corrective control outputs. The feedback loop continuously compensates for position errors caused by compliance in mechanical linkages and non-rigid connections.
2Adaptability or versatility
If traditional force control is used, then the robot can adapt to environmental changes, but position accuracy deteriorates due to compliance and non-rigid connections
Solution Approach 1:
The hybrid controller dynamically adjusts the control strategy by varying the hybrid weighting value over time based on task phase and environmental conditions. During phases requiring precise positioning, the controller increases position control weighting; during phases requiring force adaptation, it increases force control weighting. This dynamic switching enables the system to maintain position accuracy while adapting to environmental changes.
Solution Approach 2:
The hybrid control system integrates both position control and force control capabilities into a single unified controller. This multi-functional approach allows the robot to perform both precision positioning tasks and adaptive force interaction tasks using the same control architecture, eliminating the need for separate control systems and making the robot versatile across different task types.
3Measurement precision
If additional position sensors and force sensors are added, then measurement capability improves, but system complexity increases
Solution Approach 1:
The system combines force sensors and position sensors into an integrated sensing system that feeds both measurements to a unified hybrid controller. By merging the control of position and force into a single hybrid control framework, the system reduces the complexity that would otherwise arise from managing separate position control and force control systems. The hybrid controller processes both sensor inputs together to generate coordinated control outputs.
Solution Approach 2:
The hybrid control system automatically determines the appropriate control strategy and weighting factors based on real-time sensor feedback and task requirements, without requiring manual intervention or complex external configuration. The controller self-adjusts the hybrid weighting value and control parameters based on the measured states and desired outcomes, reducing the operational complexity of the enhanced sensor system.
4Measurement precision
If hybrid control with variable weighting is implemented, then control accuracy improves, but computational complexity increases
Solution Approach 1:
The hybrid controller dynamically changes the weighting parameter (hybrid weighting value) between position control and force control based on task phase and environmental conditions. This parameter adjustment enables the controller to optimize control accuracy for different operational scenarios. The weighting value varies continuously or discretely depending on the control strategy, allowing the system to achieve high accuracy across diverse tasks while using a relatively simple linear combination approach.
Data Source
AI summary
An object can be moved via a robotic system with a combination of force and position control. The control system can include the object to be moved, the robotic system that moves the object, at least one force sensor, at least one position sensor, and a controller. A position control output, a force control output, and a hybrid weighting value can each be determined by the controller based on sensor data and then combined to determine an amount of position control and/or force control to be applied to move the object and/or modify an object in motion's trajectory.