Dynamic Task Scheduling for Autonomous Robot Battery Longevity

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Solution Overview

Problem

Robotic assistants face battery degradation due to frequent charging and discharging, leading to reduced operational lifespan, as they often transition between wake and suspend modes, causing power consumption and latency issues.

Innovation Solution

A system that uses historical data and input to predict user tasks, allowing the robot to schedule tasks and charging dynamically, minimizing transitions between modes and maintaining battery levels between 30% and 70% capacity to extend battery life.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the robot frequently transitions between wake and suspend modes to perform tasks, then task availability and responsiveness are improved, but battery degradation accelerates and operational lifespan reduces

Engineering Contradiction:

Improvetask availabilityVSAvoidbattery lifespan

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The robot performs charging actions in advance during predicted idle periods before tasks are needed, ensuring battery is replenished without requiring frequent wake-suspend transitions. The system predicts future task requirements and schedules charging beforehand during low-activity periods, reducing the need for reactive mode switching that degrades the battery.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the robot's operational state based on real-time conditions and predictions. Instead of rigid wake-suspend transitions, the robot continuously monitors task predictions, battery state, and environmental factors to determine optimal operational modes, reducing unnecessary transitions while maintaining task availability.

Inventive Principle:

Principle #15Dynamics

2Speed

If the robot maintains wake mode to perform tasks quickly, then task execution speed is improved, but power consumption increases

Engineering Contradiction:

Improvetask execution speedVSAvoidpower consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The robot anticipates upcoming tasks using historical data and predictions, transitioning to wake mode in advance during predicted idle periods. This allows the robot to be ready for tasks without remaining continuously awake, reducing overall power consumption while maintaining quick response capability when tasks are actually needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors task execution patterns, battery state, and environmental conditions to dynamically adjust wake-suspend timing. Feedback from actual task performance refines future predictions, optimizing the balance between quick task execution and power consumption over time.

Inventive Principle:

Principle #23Feedback

3Use of energy by moving object

If the robot enters suspend mode to conserve battery, then power consumption is reduced, but task response latency increases

Engineering Contradiction:

Improvepower consumptionVSAvoidtask response latency

Core Design Contradiction:

Use of energy by moving objectVSLoss of time

Solution Approach 1:

The robot transitions to wake mode in advance of predicted tasks during idle periods, eliminating the need for last-minute wake transitions that cause latency. By preparing beforehand based on task predictions, the robot maintains responsiveness without sacrificing battery conservation during actual idle time.

Inventive Principle:

Principle #10Preliminary action

4Quantity of substance

If the robot charges frequently to maintain battery levels, then battery capacity is maintained, but charge/discharge cycles increase causing degradation

Engineering Contradiction:

Improvebattery capacityVSAvoidbattery lifespan

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The robot autonomously monitors its own battery state, task predictions, and environmental conditions to determine optimal charging timing. The system serves its own battery management needs by intelligently scheduling charging during predicted idle periods, avoiding both overcharging and excessive charge cycles while maintaining adequate capacity.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11400823B1Dynamic scheduling of tasks and charging of an autonomous mobile device

Publication Date: 2022.08.02 AMAZON TECH INC

AI summary

A robot operates using electrical power. The robot is able to autonomously return to a dock to recharge its batteries. The robot may perform a variety of tasks that consume electrical power, such as performing video calls, presenting audio or video content, acting as a sentry, and so forth. Usage of the robot is analyzed to predict tasks that a user is likely to request. This information is used along with information about previously scheduled tasks and system tasks to create a list of tasks to be performed and when to perform those tasks. The tasks to be performed are calculated to maximize availability of the robot to the user while minimizing charge/discharge cycles, minimizing the extent of the charge/discharge, and minimizing the use of rapid charging. As a result, the lifespan of the batteries may be increased.