Cooperative Robot Location Generation for Energy-Efficient Motion
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Solution Overview
Problem
Current systems for cooperative robot operations in industrial settings face inefficiencies in energy consumption and motion time, particularly in production lines where multiple robots are used, as they often require complex powertrains and operate for long hours, leading to high energy usage and prolonged task cycles.
Innovation Solution
A method for automatic and efficient location generation in cooperative motion between master and slave robots, which involves simulating slave operations to determine optimal locations along the master trajectory, generating candidate operations, and calculating an efficiency factor to identify and retain only the most efficient operations, thereby optimizing energy consumption and motion time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots with complex powertrains are used in cooperative operations, then the operational capability and task completion ability are improved, but the energy consumption increases significantly
Solution Approach 1:
The system performs preliminary simulation of slave operations to obtain duration and trajectory time data before actual execution. This preliminary action allows optimization of candidate operations to minimize energy consumption while ensuring task completion capability is maintained.
Solution Approach 2:
The system generates multiple candidate operations with different parameters and evaluates them using an efficiency factor that considers energy consumption. By changing operational parameters and selecting the most efficient candidate, the system reduces energy usage while maintaining productivity.
2Reliability
If multiple robots operate for long hours to complete complex tasks, then the task completion thoroughness is improved, but the task cycle time is prolonged
Solution Approach 1:
The system simulates slave operations and generates candidate operations in advance, evaluating their efficiency factors before actual execution. This preliminary evaluation identifies the most time-efficient operations that still ensure reliable task completion.
Solution Approach 2:
The system uses efficiency factor calculations as feedback to select optimal candidate operations. This feedback mechanism ensures that operations are chosen based on their ability to minimize task cycle time while maintaining completion reliability.
3Adaptability or versatility
If complex cooperative operations are executed without optimization, then the operational flexibility is maintained, but the efficiency factor is reduced
Solution Approach 1:
The system dynamically generates and evaluates multiple candidate operations based on simulated slave operation data. This dynamic approach allows the system to adapt to different operational scenarios while selecting the most efficient candidate, thereby maintaining flexibility without sacrificing productivity.
Solution Approach 2:
The system changes operational parameters by generating candidate operations with varying characteristics and selects the optimal one based on efficiency factor calculations. This parameter optimization maintains operational flexibility while improving overall efficiency.
Data Source
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AI summary
Methods for automatic and efficient location generation for cooperative motion. A method includes receiving a cooperative operation (200) comprising a master operation (205) and the slave operation (210), simulating the slave operation (210) to obtain a slave duration (154) between consecutive slave locations (240) and a trajectory time (156) to perform the slave operation (210), populating a plurality of potential locations (300) along the master trajectory (215), generating a plurality of candidate operations (400) in a population (168), for each of the plurality of candidate operations (400) in the population (168), simulating a candidate operation (400) with the slave operation (210) to calculate an efficiency factor (166) to perform the candidate operation (400) and removing the candidate operation (400) from the population (168) when the efficiency factor (166) is not better compared to other candidate operations (400) in the population (168) and returning the candidate operation (400) remaining in the population (168).