Battery-Aware Mission Assignment for Autonomous Mobile Robots
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Solution Overview
Problem
Existing autonomous mobile robots in material handling facilities face inefficiencies due to battery state of charge limitations, leading to incomplete missions and disrupted productivity, especially in unstructured environments with unpredictable obstacles.
Innovation Solution
A robot mission coordinator system that optimizes mission assignments based on battery state of charge, robot location, mission priority, and environmental factors to maximize robot utilization and productivity by assigning missions that align with the robot's battery capacity and navigating obstacles effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots are assigned missions without considering battery state of charge, then mission assignment simplicity is maintained, but robot utilization efficiency deteriorates due to incomplete missions and frequent recharging interruptions
Solution Approach 1:
The system performs preliminary assessment of robot battery state of charge before mission assignment. The coordinator system evaluates current battery levels and predicts whether the robot can complete the assigned mission without recharging, preventing mission interruptions before they occur.
Solution Approach 2:
The mission assignment system dynamically adjusts assignments based on real-time battery state of charge data. As battery levels change, the system reevaluates and reassigns missions to optimize robot utilization, transitioning from static to dynamic assignment based on energy availability.
2Adaptability or versatility
If robots operate in unstructured environments with unpredictable obstacles, then environmental adaptability is improved, but mission completion reliability deteriorates due to unexpected battery depletion from extended navigation requirements
Solution Approach 1:
The system performs preliminary navigation path analysis to estimate energy requirements before assigning missions in unstructured environments. By predicting the energy needed to navigate potential obstacles and reach the destination, the system ensures the robot has sufficient battery charge to complete the mission despite environmental uncertainties.
Solution Approach 2:
The system continuously monitors battery state of charge during mission execution and provides feedback to the coordinator. When obstacles extend navigation time or energy consumption, the system detects battery level changes and can intervene to prevent mission failure by redirecting the robot to recharge.
3Reliability
If battery state of charge is monitored and considered in mission assignment, then mission completion reliability is improved, but system complexity increases due to additional monitoring and coordination requirements
Solution Approach 1:
The coordinator system performs multiple functions: it manages mission assignments, monitors battery state of charge, predicts energy requirements, and dynamically reassigns missions. By consolidating these diverse functions into a single multi-functional coordinator, the system reduces overall complexity compared to having separate systems for each function.
4Productivity
If robots are assigned longer missions to maximize utilization, then productivity is improved, but the risk of battery depletion increases, causing mission failures in unstructured environments
Solution Approach 1:
The system performs preliminary energy requirement analysis for each potential mission assignment. Before assigning a long-duration mission, the coordinator evaluates whether the robot's current battery state of charge is sufficient to complete the entire mission, preventing assignments that would result in battery depletion and mission failure.
Solution Approach 2:
The system dynamically adjusts mission assignment parameters based on battery state of charge. When battery levels are high, longer missions are assigned to maximize productivity. When battery levels are lower, the system assigns shorter missions or directs robots to recharge, optimizing the balance between productivity and reliability based on energy parameters.
Data Source
AI summary
Disclosed are various embodiments for optimizing robot utilization in autonomous mobile robots by accounting for the battery charge state when assigning missions to be performed by the autonomous mobile robots in a material handling facility. In particular, mission data associated with one or more missions and robot data including a battery charge state associated with one or more robots can be analyzed to determine a robot mission assignment that accounts for battery state charge in order to maximize robot utilization and productivity of material handling tasks.