Machine learning-based solutions for resource management in fog computing

In recent years, automatic mobile robots (AMRs) have been widely concerned in various fields, such as material handling, unmanned aerial vehicle (UAV) cruise, automatic factories and so forth. Compared with traditional robots, an AMR system brings several challenges such as adaptation to the environment, stable communication and robust control. For example, in a complex environment, AMR has to observe the surrounding environment in real time to avoid obstacles, and track the target in real time to change its trajectory until the task is completed, based on an efficient integration of the perception, communication and control. AMR systems require support from various technologies including real-time positioning and navigation, trajectory planning, target tracking, multi-agent systems, robot monitoring and so forth. In order to enable more enhanced applications of AMRs, the geometric adaptability, design characteristics, mobility, flexibility, obstacle avoidance and fault-tolerant control should be further discussed. This paper provides a comprehensive elaboration on the four aspects of AMR systems, namely AMR applications, perception technologies, robot communications and control mechanisms. Future research directions are also pointed out.

https://figshare.com/articles/preprint/A_Comprehensive_Review_on_Automatic_Mobile_Robots_Applications_Perception_Communication_and_Control/19619880/1/files/34851498.pdf

A Comprehensive Review on Automatic Mobile Robots: Applications, Perception, Communication and Control

As a specific kind of Cyber-Physical Systems (CPSs), autonomous robot clusters play an important role in various intelligent manufacturing fields. However, due to the increasing design complexity of robot clusters, it is becoming more and more challenging to guarantee the safety and efficiency for multi-robot cooperative navigation in dynamic and complex environments. Although Deep Reinforcement Learning (DRL) shows great potential in learning multi-robot cooperative navigation policies, existing DRL-based approaches suffer from scalability issues and rarely consider the transferability of trained policies to new tasks. To address these problems, this paper presents a novel DRL-based multi-robot cooperative navigation approach named HRMR-Navi that equips each robot with both a two-layered hierarchical graph network model and an attention-based communication model. In our approach, the hierarchical graph network model can efficiently figure out hierarchical relations among all agents that either cooperate for efficiency or avoid obstacles for safety to derive more advanced strategies, and the communication model can accurately form a global view of the environment for a specific robot, thus the multi-robot cooperation efficiency can be further strengthened. Meanwhile, we propose an improved Proximal Policy Optimization (PPO) algorithm based on the Maximum Entropy reinforcement learning, named MEPPO, to enhance the robot exploration ability. Comprehensive experimental results demonstrate that, compared with state-of-the-art approaches, HRMR-Navi can achieve more efficient cooperative navigation with less time cost, lower collision rate, higher scalability, and better knowledge transferability. 

Hierarchical Relational Graph Learning for Autonomous Multi-Robot Cooperative Navigation in Dynamic Environments

Heterogeneous teams of multiple mobile robots will be important for future scientific explorations of extraterrestrial surfaces or hazardous areas. Mission operation in such harsh, unknown environments poses diverse challenges. Robots need to cooperate autonomously due to the large network latency to the ground station while operators need to adapt the ongoing mission flexibly based on new discoveries obtained during execution. Furthermore, shared situational awareness between operators and roboticists is highly required to deal with execution failures promptly. To overcome these challenges, this paper proposes the high-level mission operation framework ROSMC. The concept of mission synchronization to robots enables continuous mission adaptations and future planning by operators while robots execute the mission autonomously. The ROS-based GUIs enable operators to intuitively create and monitor the mission for robots as well as to communicate with roboticists smoothly. The proposed framework was evaluated by a pilot study with a simulator and demonstrated at a Moon-analogue field on Mt. Etna in Sicily, Italy, involving 3 robots and around 70 researchers for 4 weeks. 

ROSMC: A High-Level Mission Operation Framework for Heterogeneous Robotic Teams

In recent years, automatic mobile robots (AMR) have been widely concerned in various fields, such as material handling, unmanned aerial vehicle (UAV) cruise, automatic factories and so on. Compared with traditional robots, an AMR bring system poses several challenges such as adaptation to the environment, stable communication and robust control. For example, when in a complex environment, it can observe the surrounding environment in real time to avoid obstacles, and track the target in real time to change its trajectory until the task is completed, which improves the flexibility and mobility of the robot. More importantly, AMR systems require supports from various technologies including real-time positioning and navigation, trajectory planning, target tracking, multi-agent systems, robot monitoring and so on. In the near future, its geometric adaptability, design characteristics, mobility, flexibility, obstacle avoidance and fault-tolerant control will be further improved. This article gives a comprehensive elaboration on the four aspects of AMR systems, namely, AMR applications, perception technologies, robot communications, and control mechanisms. Future research directions are also pointed out. 

IoT systems can be regarded as distributed systems that are composed of heterogeneous smart devices with different performances and functionalities. Many IoT applications with various resources and real-time requirements will run concurrently in an IoT system. In addition, non-functional properties such as power consumption and device lifetime are considered important. Since the IoT services can be added or removed anytime and the device status may change at runtime, the system configuration is unprecedentedly dynamic, which brings up a challenging scheduling problem of IoT services onto the devices. We assume that an IoT application is specified by a directed graph, called service graph, where nodes represent services while the directed edges represent the dependencies between them. To tackle this problem, we propose a novel adaptive approach that consists of two scheduling techniques, incremental and global. The incremental heuristic method is proposed to provide fast responsiveness to the dynamically changing configuration while satisfying the real-time constraints. During steady state operation, GA-based global rescheduling is periodically applied. We use the acceptance ratio of new services and energy consumption as two performance metrics of the proposed scheduling method. The viability of the proposed approach is verified by extensive simulations with random IoT service graphs.

Adaptive run-time scheduling of dependent services for service-oriented IoT systems

A key technical challenge to make multiple robots work together is software development how to specify the mission at the user level and how to program each robot separately. In this paper, we propose a novel software development framework in which a mission is specified with a novel scripting language and the individual robot behavior with an extended dataflow model at the task level. How to relate these two specifications and how to generate the robot code automatically are also addressed in the proposed framework. The viability of the proposed methodology is validated with a preliminary experiment.

Service-Oriented Robot Software Development Framework for Distributed Heterogeneous Platforms

In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. A key technical challenge to realize this vision is software challenge on how to specify the mission at the user level and how to program each robot separately. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming. For mission specification, a novel scripting language is proposed with the expression capability of dynamic mode change and multitasking. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. In the proposed framework, the actual robot software is automatically generated from the model. The viability of the proposed framework is demonstrated with two real-life experiments.

SeMo: Service-Oriented and Model-Based Software Framework for Cooperating Robots

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Adaptive run-time scheduling of dependent services for service-oriented IoT systems

Service-Oriented Robot Software Development Framework for Distributed Heterogeneous Platforms

SeMo: Service-Oriented and Model-Based Software Framework for Cooperating Robots