AI Platform for Mobile Charging of Robotic Devices
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Solution Overview
Problem
Mobile robotic devices and vehicles face challenges in maintaining operational power due to limited battery life, especially in time-sensitive or harsh conditions, requiring efficient and intelligent recharging solutions to prevent inoperability.
Innovation Solution
An artificial intelligence platform coordinates recharging through a network of stationary and mobile charging pods, utilizing various charging methods like inductive, infrared, and radio frequency charging, and integrates with data providers to determine optimal recharging locations and times based on device usage, weather, and geographic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile systems use traditional recharging stations requiring manual identification and travel, then devices can be recharged, but devices experience loss of time and reduced productivity due to manual planning and stationary charging requirements
Solution Approach 1:
The mobile charging system enables charging pods to autonomously navigate to mobile systems requiring power, eliminating the need for manual device identification and travel planning. The system self-manages charging scheduling, pod routing, and battery replacement operations, significantly reducing time loss while maintaining reliability.
Solution Approach 2:
The system transitions from static charging stations to dynamic mobile charging pods that can move and position themselves optimally near mobile systems. This dynamic approach allows flexible charging locations and real-time adaptation to device positions, improving both reliability and time efficiency.
2Duration of action of moving object
If mobile systems carry larger batteries to extend operation duration, then devices can operate longer between charges, but device weight increases affecting mobility and energy efficiency
Solution Approach 1:
The system extracts the heavy battery component from the mobile system by using separate, replaceable charging pods that contain power sources. Mobile systems can quickly swap depleted batteries for charged ones without carrying excess weight, extending operational duration while maintaining lightweight design.
Solution Approach 2:
The system implements battery replacement where depleted batteries are discarded from active service and recovered for recharging in charging pods. This allows mobile systems to maintain light weight by only carrying necessary battery capacity while extending operational duration through rapid battery swaps.
3Reliability
If mobile systems require manual charging schedule planning, then charging can be coordinated, but device complexity and ease of operation deteriorate due to manual intervention requirements
Solution Approach 1:
The system continuously monitors battery charge levels of mobile systems and automatically triggers charging operations when thresholds are reached. This feedback mechanism eliminates manual planning while ensuring reliable charging coordination, and the system communicates charging status and scheduling information automatically.
Solution Approach 2:
The charging system autonomously manages all aspects of charging coordination including monitoring battery levels, selecting appropriate charging pods, scheduling charging operations, and executing battery replacements. This self-service approach maintains reliable charging coordination while dramatically improving ease of operation by eliminating manual intervention.
4Adaptability or versatility
If charging pods are deployed to provide mobile charging services, then devices can be recharged in remote locations, but system complexity increases due to coordination of multiple moving charging units
Solution Approach 1:
The charging pods are designed as universal, multi-functional units capable of autonomous navigation, wireless power transmission, battery replacement operations, and communication with multiple mobile systems. This standardization reduces overall system complexity despite the presence of multiple mobile charging units across diverse locations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures continuous operation of mobile systems by intelligently scheduling and executing recharging processes, even when devices are in remote or challenging environments, thereby preventing failure and ensuring timely completion of tasks.
Implementation Method 1
The charging pod(s) may provide charging to the mobile system(s) through one or more of inductive charging
Implementation Method 2
distant charging using infrared
Implementation Method 3
distant charging using radio frequency
Data Source
AI summary
Example methods, apparatus, systems, and machine-readable mediums for an artificial intelligence platform for mobile charging of rechargeable vehicles and robotic devices are disclosed. An example method may include determining that a mobile vehicle is operating within a region and determining that the mobile vehicle requires charging of a battery for the mobile device while operating within the region. The method may further comprise identifying a charging station available within the region for charging of the battery at a time and a location within the region and navigating at least one of the mobile vehicle or the charging station to the location at the time for charging the battery of the mobile vehicle.