A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

Abstract

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.Note to Practitioners-Cloud computing for robotics is very promising for several reasons, like robot's energy saving, larger storage capacity, stable electric power, better resource utilization and the difficulty of upgrading the robots' embedded hardware. The presented application extracts 3D point clouds from the stereo image pairs of a camera situated on the robot. Using these 3D points, a shared control is implemented to help the remote teleop-eration of a robot. That is, the commands sent by a joystick are attenuated when a possible collision is detected (by checking the future commanded trajectory against the 3D points). All of these computationally heavy tasks (difficult to perform by a mobile robot) are done in the cloud. The offloading models proposed in this paper are generic enough to be used in other applications. Obtained results show that further improvement in communica-tion technologies will suppose a significant performance boost for offloading computation.

Cloud Robotics: Current Status and Open Issues

“…[40] ✓ Ref. [59] ✓ A. SLAM SLAM is a fundamental topic since 1990s when DurrantWhyte first clearly defined SLAM [35] as exploring unknown environment problems. Extended Kalman filter (EKF) [36], the most famous SLAM method, has been applied to common uses.…”

“…As mentioned above, the four major time consumption (pre-processing time for sending and receiving data, time consumed for sending and receiving data, time consumed in cloud for processing and lastly network delay) should be less than the onboard time consumption. Taking SLAM as an example, applications such as stereo image processing [59], a typical task with great computation, may delegate the majority of tasks (navigation assistance, etc.) to the cloud, which leads to less bandwidth consumption and latencies compared with other computational modes.…”

Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud

“…One example scenario is cloud offloading of computationally intensive components. AIOLOS proxy policies support dynamic trade-offs between parameters such as on-board processing and network communication, as for example presented in [17], where the authors compare various robot-cloud configurations for the components involved in robot navigation. Another scenario is a big/little artificial neural network deployment [18], [19], where the big neural network on the cloud is only executed if the confidence of the small neural network is considered too low.…”

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

“…Besides, other applications were proposed as well. For instance, knowledge change among small batch assembly robots [30], robot navigation assistance [31], and so on. The aforementioned research took advantage of a wide range of online resources.…”