Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Abstract

Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

Dynamic load balancing assisted optimized access control mechanism for Edge‐Fog‐Cloud network in Internet of Things environment

“…In Reference 19, an Edge‐Fog‐Cloud environment is discussed that details a distributed cloud for IoT computations. In addition to this, authors discuss an offloading approach detailing the simultaneous localization and mapping for indoor mobile robots with Edge‐Fog‐Cloud computing in Reference 20. To add a different orientation to the fog assisted cloud environment, a trust management approach is highlighted in Reference 21 enabling the block‐chain based fog computing platform.…”

Reliable distribution of computational load in robot teams

“…Lack of computing resources available to a single robot is a well known issue (Sarker et al 2019;Lan et al 2018). While robots become more and more computationally powerful their computation demands also grow in particular in the areas of image recognition, navigation, and data analysis.…”

Supporting the evolution of applications deployed on edge-based infrastructures using multi-layer feature models

“…The functionality of this application is decomposed in 21 different tasks (t): t1-t3, responsible for monitoring three parking places (P1, P2 and P4); providing video sharing in classrooms C4, C9 and C10 (t4-t6); notifying the presence of professors in offices O2, O5 and O7 (t7-t9); and collecting environmental conditions (t10) from the environmental station located at M2, which provides updated information about the weather. Apart from these functionalities, the augmented reality component enriches the real world according to the user's location, which is determined using SLAM (Simultaneous Location and Mapping) technology [31], provided by t11-t21. These kind of applications are specially attractive to apply edge computing, as the tasks are computational-intensive and delay-sensitive, and their execution on mobile devices is generally prohibitive in terms of battery cost when satisfying users' 2 Source code available at: http://caosd.lcc.uma.es/research/rsc/modules-evo.zip expectations in terms of latency [5,31].…”

“…Apart from these functionalities, the augmented reality component enriches the real world according to the user's location, which is determined using SLAM (Simultaneous Location and Mapping) technology [31], provided by t11-t21. These kind of applications are specially attractive to apply edge computing, as the tasks are computational-intensive and delay-sensitive, and their execution on mobile devices is generally prohibitive in terms of battery cost when satisfying users' 2 Source code available at: http://caosd.lcc.uma.es/research/rsc/modules-evo.zip expectations in terms of latency [5,31]. Concretely, 11 of these 21 tasks correspond to activities responsible for enriching the reality, of which 3 must be executed in the user node-the tasks responsible for camera calibration, capture frames and overlay context (t11, t13 and t17).…”