Collaborative Robot Predictive Control for Human Task Synchronization
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Solution Overview
Problem
Current collaborative robot systems are inefficient due to their inability to predict human operator activities accurately, leading to downtime as robots wait for human operators to complete tasks, especially in environments where tasks require both human intelligence and mechanical operations.
Innovation Solution
A predictive control method using detection devices like cameras or sensors to track the human operator's position and movement patterns, processing data to predict which work sector the operator will move to and when, allowing the robot to prepare for synchronization and minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot waits for the human operator to complete tasks before acting, then the robot can accurately synchronize with the operator, but the robot experiences downtime and loses working efficiency
Solution Approach 1:
The system performs preliminary actions by predicting the operator's future movements and preparing the robot in advance. The prediction module forecasts which work sector the operator will move to next, allowing the robot to position itself proactively rather than reactively, eliminating idle waiting time while maintaining synchronization accuracy
Solution Approach 2:
The system continuously monitors the operator's current position and residence time in work sectors, using this feedback to update predictions dynamically. This closed-loop feedback mechanism ensures the robot adapts to actual operator behavior patterns, maintaining high synchronization accuracy while optimizing productivity through informed predictive actions
2Reliability
If the robot remains stationary waiting for the operator, then the robot avoids premature actions that could desynchronize collaboration, but overall system productivity decreases
Solution Approach 1:
Instead of remaining stationary, the robot performs preliminary positioning actions based on predicted operator movements. The system calculates probable future sectors and prepares the robot in advance, transforming idle waiting time into productive preparation time while maintaining collaboration synchronization
Solution Approach 2:
The system transitions from a static waiting strategy to a dynamic predictive strategy. The robot's behavior becomes adaptive and flexible, continuously adjusting its actions based on real-time operator behavior patterns and predicted movements, thereby eliminating rigid idle time while preserving synchronization
3Productivity
If the robot interrupts its task to take pieces from the operator, then the robot can maintain continuous workflow, but the robot must frequently stop and start which reduces overall efficiency
Solution Approach 1:
The system predicts when the operator will release pieces and proactively positions the robot to receive them at the optimal moment. This preliminary positioning eliminates the need for frequent task interruptions, as the robot is already in place to seamlessly integrate piece transfer into its continuous workflow
Solution Approach 2:
The predictive control enables continuous useful action by eliminating idle waiting and minimizing interruptions. The robot maintains continuous workflow by anticipating piece transfer timing and positioning itself in advance, ensuring uninterrupted productive action while reducing task interruption overhead
Data Source
AI summary
This disclosure relates to a method of controlling a collaborative robot, or “cobot”. According to the disclosed method the “cobot” is controlled so as to make it ready to perform a task in collaboration with the human operator only when the latter is about to move into a work sector to carry out the task collaborating with the robot. The control method of the present disclosure can be implemented by means of a control system comprising detection devices, such as for example one or more cameras or a mat equipped with sensors, which detect the position of the hands or of the entire body of the operator in the space of work, a memory in which to store identification data of the sectors of work engaged by the human operator, of the times of permanence in them and of the successive sectors of work in which the human operator moves, as well as a control microprocessor unit which processes this data stored in the memory according to the method of this disclosure to predict in which work sector the operator will move his hands and when that will happen, and which controls a robot based on this prediction information. The method of this disclosure can be implemented by means of software executed by a microprocessor unit.