Networked Robot Control Data Optimization for Energy and Task Quality
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Solution Overview
Problem
As robots become increasingly complex and networked, existing systems struggle to efficiently generate optimized control data sets for networked robots to perform complex tasks effectively, especially in terms of energy efficiency and task execution quality.
Innovation Solution
A system comprising networked robots, an optimizer, and a database that allows for the creation, evaluation, and optimization of control data sets through local and distributed computing, enabling robots to determine and refine control data sets for new tasks based on energy consumption and execution parameters, with the optimizer utilizing distributed computing capacities for enhanced optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robots become increasingly complex to perform complex tasks, then task capability is improved, but control data complexity and energy consumption increase
Solution Approach 1:
The system segments the complex task into multiple sub-tasks and distributes them across multiple robots. Each robot receives simplified control data for its specific sub-task, reducing individual control complexity while maintaining overall system capability. The optimization unit processes control data for each robot separately, further simplifying the control burden.
Solution Approach 2:
The optimization unit dynamically adjusts control parameters (such as speed, acceleration, force) based on task requirements and robot capabilities. By optimizing these parameters, the system reduces energy consumption and control data complexity while maintaining task performance. The control data is transformed into optimized parameter sets that balance capability and complexity.
2Use of energy by moving object
If control data sets are optimized for energy efficiency, then energy consumption is reduced, but task execution quality may be compromised
Solution Approach 1:
The optimization unit uses feedback from task execution results to continuously refine control data sets. Energy-efficient control strategies are tested and evaluated, with successful optimizations retained and unsuccessful ones discarded. This feedback loop ensures that energy efficiency improvements do not compromise task execution quality, as any degradation would be detected and corrected.
Solution Approach 2:
The system optimizes control parameters within acceptable performance boundaries. Rather than maximizing energy efficiency at any cost, the optimization unit adjusts parameters (speed, acceleration, force) to achieve energy savings while maintaining quality thresholds. Multiple parameter combinations are evaluated to find the optimal balance between energy consumption and task execution quality.
3Loss of time
If local units determine control data sets for new tasks, then response time is improved, but optimization quality is insufficient compared to centralized processing
Solution Approach 1:
The system segments the optimization process into local and centralized components. Local units handle immediate, time-critical adjustments and preliminary optimization, providing fast response. The optimization unit performs comprehensive, high-quality optimization on a subset of control data, achieving high precision where needed. This segmentation allows both fast response and high optimization quality to coexist.
Solution Approach 2:
Rather than having every local unit perform full optimization (which would be too slow), the system applies partial optimization locally and excessive (comprehensive) optimization centrally for critical tasks. The optimization unit processes control data with high computational effort for tasks requiring maximum optimization quality, while local units handle routine optimizations with reduced computational overhead.
Data Source
Figure 1
AI summary
The invention relates to a system for generating sets of control data for networked robots, comprising a plurality of robots (Ri), wherein i = 1, 2, 3, ..., n and n ≥ 2, an optimizer (OE) and a database (DB), which are networked with one another by a data network, each robot (Ri) comprising at least: a control unit (SEi) for controlling and/or regulating the robot (Ri); a storage unit (SPEi) for storing the sets of control data SDi(Ak) which allow in each case the control of the robot (Ri) in accordance with a predetermined task (Ak), wherein k = 0, 1, 2, ..., m; a unit (EEi) for specifying a new task (Am+1) for the robot (Ri), wherein Am+1 ≠ Ak; a unit (EHi) for determining a set of control data SDi(Am+1) for the execution of the task (Am+1) by the robot (Ri), an evaluation unit (BEi) for evaluating the set of control data SDi(Am+1) determined by the unit (EHi) with regard to at least one parameter (P1) with the characteristic number KP1(SDi(Am+1)), and a communication unit (KEi) for communicating with the optimizer (OE) and/or the database (DB) and/or other robots (Rj≠i), wherein, upon request by a robot (Ri), the optimizer (OE) is designed and configured to determine an optimized set of control data SDi,P2(Am+1) at least with regard to one predetermined parameter (P2), wherein the request by the robot (Ri) occurs when the characteristic number KP1(SDi(Am+1)) does not meet a predetermined condition. The database (DB) stores the set of control data SDi,P2(Am+1) optimized by the optimizer (OE) and provides the same to the robot (Ri) for execution of the task (Am+1).