Mobile Robot Cloud Offloading Under Battery and Network Constraints
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Solution Overview
Problem
Mobile robots, particularly payload-limited and battery-powered ones like robot vacuum cleaners and UAVs, face limitations in computational capacity, hindering their ability to perform tasks effectively and efficiently due to limited on-board computing resources.
Innovation Solution
A method for dynamically offloading computational tasks between a mobile robot's local processing system and a remote cloud system, utilizing robotics middleware to facilitate communication and resource allocation based on profile data, enabling computation tasks to be performed locally or remotely depending on network conditions, battery life, and cost considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computationally expensive techniques are implemented on mobile robots, then task performance accuracy and robustness are improved, but on-board computing resources are exceeded and battery life is reduced
Solution Approach 1:
The patent extracts computationally intensive tasks from the mobile robot's on-board processing system and relocates them to a remote cloud-based processing system. The robot controller identifies computation tasks suitable for offloading and transmits input data to the cloud system, which processes the tasks and returns output data to the robot, thereby preserving battery life while maintaining task performance accuracy.
Solution Approach 2:
The patent introduces a cloud-based processing system as an intermediary between the mobile robot and the computation tasks. This intermediary system handles the computationally expensive operations, acting as a mediator that relieves the robot's on-board resources while ensuring accurate task execution through coordinated data exchange between the robot controller and cloud system.
2Productivity
If more on-board computing resources are added to mobile robots, then computational capacity is improved, but payload weight and power consumption increase
Solution Approach 1:
The patent extracts the heavy computational workload from the mobile robot's on-board system and places it on a remote cloud system. This extraction eliminates the need for the robot to carry additional computing hardware, thereby maintaining high computational capacity for task execution while avoiding the weight penalty of onboard processing resources.
3Reliability
If computationally expensive techniques are used, then task execution robustness is improved, but real-time response capability deteriorates due to processing time
Solution Approach 1:
The patent creates a cloud-based copy of the computation processing capability that can handle computationally expensive techniques. By replicating the processing function in the cloud rather than relying solely on limited onboard resources, the system achieves robust task execution with improved time response, as the cloud system can process multiple tasks concurrently and return results efficiently.
Data Source
AI summary
Methods for operating a mobile robot to dynamically offload computation tasks to a cloud system are described. The methods advantageously enable a mobile robot to switch between local execution by the mobile robot or remote execution by the cloud system at any time, depending on timing requirements, energy requirements, or any other requirements. Thus, the methods enable the mobile robots to be robust to varying network conditions, while at the same time taking advantage of off-board computing resources when possible. In at least some embodiments, a middleware such as Robot Operating System is leveraged for communication between mobile robots and with the cloud system.