Edge SLAM Offloading for Low-Latency Mobile Robot Navigation
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Solution Overview
Problem
Mobile robots face challenges in offloading computationally expensive simultaneous localization and mapping tasks due to processing power, energy, and memory constraints, and intermittent cloud connectivity, which affects latency-sensitive applications.
Innovation Solution
A dynamic offloading framework that partitions SLAM tasks between mobile robots and external resources, prioritizing tasks based on computation, communication load, and energy usage to minimize execution time and meet latency requirements, using an analysis module to determine optimal offloading to edge network servers and cloud infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computationally expensive SLAM tasks are performed locally on mobile robots, then processing autonomy is improved, but energy consumption and processing power requirements increase beyond robot capabilities
Solution Approach 1:
The patent introduces an edge server as an intermediary between mobile robots and the cloud. The edge server performs SLAM computation offloading, receiving sensor data from robots and returning processed results. This mediator approach allows robots to maintain high processing autonomy without bearing the full computational burden, thereby reducing their energy consumption while preserving autonomy benefits.
Solution Approach 2:
The patent transitions the computational architecture from a single-dimension local processing model to a multi-dimensional distributed model involving robot-local processors, edge servers, and cloud infrastructure. This dimensional expansion allows flexible task distribution based on real-time conditions, enabling robots to offload computationally intensive SLAM tasks to edge servers, thus reducing local energy consumption while maintaining autonomy.
2Power
If SLAM tasks are offloaded to remote cloud servers, then processing power is improved, but latency increases for latency-sensitive applications
Solution Approach 1:
The patent segments the cloud computing infrastructure into edge servers positioned geographically closer to robots and remote cloud servers. This segmentation creates a hierarchical architecture where latency-sensitive SLAM tasks are processed at the edge, reducing communication latency, while less time-sensitive tasks can be handled by remote clouds. The segmentation thus resolves the latency issue while preserving access to powerful cloud resources.
Solution Approach 2:
The patent implements local quality by deploying edge servers in proximity to mobile robots, creating localized computing resources with different characteristics from remote clouds. These local edge servers provide low-latency processing for time-sensitive SLAM operations, while maintaining the option to use remote cloud servers for non-critical computations. This local quality approach ensures that processing power needs are met without incurring high latency penalties.
3Ease of operation
If cloud connectivity is assumed continuous, then offloading simplicity is improved, but reliability decreases under intermittent connectivity conditions
Solution Approach 1:
The patent implements beforehand cushioning by designing the offloading system to anticipate and prepare for intermittent connectivity conditions. The architecture includes multiple offloading destinations (edge servers and cloud servers) and implements intelligent task routing that can adapt when connections are unavailable. This preparatory design cushions the system against connectivity disruptions, maintaining reliability without significantly complicating the offloading operation.
Solution Approach 2:
The patent introduces dynamics into the offloading system by implementing adaptive task routing that responds to real-time connectivity conditions. The system dynamically selects between edge servers and cloud servers based on current network availability, and can adjust task distribution strategies when connectivity is intermittent. This dynamic behavior maintains reliability under varying conditions while keeping the offloading mechanism relatively simple through automated adaptation.
4Power
If computation load is shared among peer robots in a cluster, then processing power is improved, but energy consumption and memory requirements increase beyond individual robot capabilities
Solution Approach 1:
The patent extracts the memory-intensive SLAM computation tasks from the mobile robot cluster and relocates them to external edge servers and cloud infrastructure. This extraction allows robots to maintain lightweight memory configurations while still benefiting from distributed processing power. The computationally intensive and memory-demanding SLAM operations are performed externally, with only essential sensor data and results being exchanged with the robot cluster.
Data Source
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AI summary
A system and method for offloading scalable robotic tasks in a mobile robotics framework. The system comprises a cluster of mobile robots and they are connected with a back-end cluster infrastructure. It receives scalable robotic tasks at a mobile robot of the cluster. The scalable robotics tasks include building a map of an unknown environment by using the mobile robot, navigating the environment using the map and localizing the mobile robot on the map. Therefore, the system estimate the map of an unknown environment and at the same time it localizes the mobile robot on the map. Further, the system analyzes the scalable robotics tasks based on computation, communication load and energy usage of each scalable robotic task. And finally the system priorities the scalable robotic tasks to minimize the execution time of the tasks and partitioning the SLAM with computation offloading in edge network and mobile cloud server setup.