- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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New approach to enhancing the performance of cloud-based vision system of mobile robots
摘要： Mobile robots require real-time performance, high computation power, and a shared computing environment. Although cloud computing offers computation power, it may adversely affect real-time performance owing to network lag. The main objective of this study is to allow a mobile robot vision system to reliably achieve real-time constraints using cloud computing. A human cloud mobile robot architecture is proposed as well as a data flow mechanism organized on both the mobile robot and the cloud server sides. Two algorithms are proposed: (i) A real-time image clustering algorithm, applied on the mobile robot side, and (ii) A modified growing neural gas algorithm, applied on the cloud server side. The experimental results demonstrate that there is a 25% to 45% enhancement in the total response time, depending on the communication bandwidth and image resolution. Moreover, better performance in terms of data size, path planning time, and accuracy is demonstrated over other state-of-the-art techniques.
关键词： Computation offloading,Computer vision,3D point cloud,Mobile robot,Stereo vision,Real-time networking,Cloud computing,Cloud robotics
更新于2025-09-23 15:23:52
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Learning-based Computation Offloading for IoT Devices with Energy Harvesting
摘要： Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model and the computation latency model. Further, we present a deep RL based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL based offloading scheme reduces the energy consumption, computation latency and task drop rate and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.
关键词： Mobile edge computing,energy harvesting,reinforcement learning,computation offloading,Internet of Things
更新于2025-09-23 15:22:29
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User Preference Aware Task Coordination and Proactive Bandwidth Allocation in a FiWi Based Human-Agent-Robot Teamwork Ecosystem
摘要： Cooperative human-agent-robot teamwork (HART) provides enormous opportunities for present-day human users to orchestrate their real-time tasks in a coordinated fashion. However, given human users’ different preferences for real-time HART task execution, e.g., lower delay and monetary cost, the selection of proper task coordination services has emerged as an important research problem by taking dynamically changing cloud agent/robot resources, network bandwidth utilization as well as delay-sensitive and delay-tolerant HART task properties into account. To cope with these challenges, in this paper we explore the synergy between caching, computation, and communications for achieving cost-effective HART task execution. To exploit the locality of different HART-centric tasks and local/non-local cloud agent/robot resources for different HART-centric task execution, we consider integrated fiber-wireless (FiWi) enhanced networks with computation task offloading as well as fiber backhaul sharing and WiFi offloading capabilities. More precisely, to minimize task execution delay and monetary cost, we propose a user preference aware HART task coordination framework that selects the appropriate dedicated or non-dedicated robot and cloud agent for given caching and computing HART task execution requirements. Further, to cope with varying bandwidth resources, we propose a proactive bandwidth allocation policy for the execution of both delay-sensitive and delay-tolerant HART tasks execution across FiWi enhanced network infrastructures. We evaluate the performance of our proposed preference aware task offloading scheme and compare it to various baseline schemes in terms of different key performance indicators, including the task execution time and monetary cost saving ratio, communication to computation ratio, and offloading gain overhead ratio. Our findings indicate that the proposed delay cost saving (DCS) policy exhibits a 27% higher task execution time saving ratio and a 48% lower monetary cost saving ratio than the proposed monetary cost saving (MCS) policy in a typical scenario.
关键词： human-agent-robot teamwork (HART),Tactile Internet,fiber-wireless (FiWi) enhanced networks,Caching,computation offloading
更新于2025-09-19 17:15:36