Robot Fleet Scheduling Using Actuator Wear-State Vectors
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Solution Overview
Problem
Existing schedulers for dynamic mechanical systems, such as robots, fail to account for varying wear on actuators due to different tasks, leading to suboptimal resource allocation and premature maintenance.
Innovation Solution
A workload scheduler that uses a vector packing algorithm to assign tasks to robots based on their current wear-state vectors and duty cycles, optimizing actuator usage to distribute wear evenly across the fleet, thereby minimizing maintenance and extending the lifespan of mechanical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing schedulers assign tasks to robots without considering actuator wear, then task allocation is simple and quick, but actuator wear becomes uneven leading to premature maintenance
Solution Approach 1:
The scheduler performs preliminary assessment of actuator wear states before task assignment. By evaluating current wear levels on each actuator and predicting future wear based on task requirements, the system proactively balances wear distribution before it leads to premature failure, rather than reacting after damage occurs.
Solution Approach 2:
The scheduling algorithm changes from simple task-robot matching to a multi-parameter optimization problem that includes actuator wear states, duty cycle requirements, and lifespan predictions. This transforms the scheduling decision into a wear-balancing optimization that extends overall system reliability.
2Reliability
If the scheduler optimizes task allocation based on wear-state vectors and duty cycles, then actuator wear is distributed evenly, but computational complexity increases
Solution Approach 1:
The system applies partial optimization by focusing computational effort on the most critical actuators and tasks. Rather than exhaustively optimizing all possible task-robot-actuator combinations, the scheduler identifies key wear-risk scenarios and optimizes those specifically, achieving sufficient wear balancing with reduced computational overhead.
Solution Approach 2:
The scheduling system uses the robots' own operational data (actual duty cycles, observed wear patterns) to self-adjust future task allocations. By learning from historical performance and automatically adapting assignments, the system reduces the need for complex external optimization computations while maintaining reliable wear distribution.
3Productivity
If maintenance is performed based on actual wear rather than scheduled intervals, then maintenance timing is optimized, but tracking wear states requires additional monitoring infrastructure
Solution Approach 1:
The actuator monitoring system serves multiple functions: it tracks real-time duty cycles for task planning, measures wear accumulation for maintenance scheduling, and provides performance data for predictive analytics. This multi-functional approach eliminates the need for separate monitoring infrastructure, as the same sensors and data collection mechanisms support all three objectives.
Solution Approach 2:
The system implements continuous feedback loops where actuators report operational status and wear metrics, the scheduler processes this information to adjust task assignments, and maintenance schedules are dynamically updated based on actual wear thresholds. This closed-loop feedback enables precision maintenance timing without requiring complex external monitoring systems.
Data Source
AI summary
Provided is a process, including: obtaining, with a computer system, a set of tasks to be performed by a fleet of robots; obtaining, with the computer system, for each task in the set of tasks, a respective plurality of duty cycles, each corresponding to an amount of usage of a respective actuator of a robot among the fleet of robots upon performing the respective task; accessing, with the computer system, for each robot in the fleet of robots, a current wear-state vector having dimensions corresponding to cumulative wear on actuators of the respective robots; and based on the current wear-state vectors and the duty cycles of the tasks, with the computer system, assigning the tasks to the robots in the fleet of robots.