Reactive task execution of a mobile robot

Abstract This thesis presents a novel control architecture, Samba, for reactive task execution. Reactive task execution implies goal-oriented and reactive properties from a robot and the ability to execute several tasks at the same time, also in a dynamic environment. These requirements are fullfilled in Samba by the rrepresentation of goals, intermediate results, and robots actions. The key idea in Samba is to produce continously reactions for all the important objects in the environment. These reactions are represented as action maps, which are a novel representation for robot actions. An action map specifies for each possible action how preferable the action is from the perspective of the producer of the map. the preferences are shown by assigning a weight to each action. Tasks are executed by modifying and combining action maps. The tasks can be either reasoned by a higher layer or triggered by sensor data. Action maps, and the methods for modifying and combining them, enable executing tasks inparallel and considering the dynamics of the environment. further, as the action maps are produced continously from sensor data, the robot actions are based on the current state of the environment. Markers describe goals and intermediate results. They facilitate managing the complexity of the system. Markers describing intermediate results decompose the system vertically, into producers and consumers of data. Markers describing goals decompose the control system horizontally, into a Samba layer and a higher layer of reasoning tasks. Tasks flow via markers from the higher layer to the Samba layer. Markers are tested on a real robot equipment with stereo gaze platform. Further, the samba architecture is applied to playing soccer. Experiments were carried out in the 1997 and 1998 RoboCup competitions. These experiments show that the Samba architecture is a potential alternative for controlling a mobile robot in a dynamic environment

Interoperable GPU kernels as latency improver for MEC

Abstract Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile UE and a MEC server instead of video codecs and this way avoiding video decoding. We demonstrate the ability to address latency bottlenecks on edge computing workloads that transact graphics. We select SPIR-V compatible GPU kernels as the intermediate representation. Our approach requires know-how in GPU architecture and GPU domain-specific languages (DSLs), but compared to video-based edge graphics, it decreases UE device delay by sevenfold. Further, we find that due to low cold-start times on both UEs and MEC servers, application migration can happen in milliseconds. We imply that graphics-based location-aware applications, such as MR, can benefit from this kind of approach

Century of manual remote control, automation, autonomy, and self-organization

Abstract We present a multidisciplinary historical review of manual remote control, automation, autonomy, and self-organization roughly covering the last century. Some conceptual analysis is given using hierarchical classifications. We show the relationships between control theory, computer science, and communication theory. We observe that the three disciplines have progressed at least partially independently, but we can see also some convergence towards similar system models, often using different terminology. We expect that multidisciplinary studies will turn out to be useful for avoiding overlapping work and for making faster progress. Furthermore, a unified terminology would facilitate communication between disciplines. This review provides a starting point for building such terminology

Energy efficient opportunistic edge computing for the Internet of Things

Abstract Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution

Research and education towards smart and sustainable world

Abstract We propose a vision for directing research and education in the field of information and communications technology (ICT). Our Smart and Sustainable World vision targets prosperity for the people and the planet through better awareness and control of both human-made and natural environments. The needs of society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm, especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems, and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach

Accelerating research to business with Hilla Runway model

Abstract The key to win on global markets is not based only on excellence of products and services, but increasingly more on successful business models and value networks, where partnering, smart specialisation and joint solution deliveries are the key elements for sustainable success. There also has to be solid processes and tools for facilitating the transfer from research to business — the creation and evolution of business. Hilla Runway provides a model and a toolset that supports business development and commercialisation

Unleashing GPUs for Network Function Virtualization:an open architecture based on Vulkan and Kubernetes

Abstract General-purpose computing on graphics processing units (GPGPU) is a promising way to speed up computationally intensive network functions, such as performing real-time traffic classification based on machine learning. Recent studies have focused on integrated graphics units and various performance optimizations to address bottlenecks such as latency. However, these approaches tend to produce architecture-specific binaries and lack the orchestration of functions. A complementary effort would be a GPGPU architecture based on standard and open components, which allows the creation of interoperable and orchestrable network functions. This study describes and evaluates such open architecture based on the cross-platform Vulkan API, in which we execute hand-written SPIR-V code as a network function. We also demonstrate a multi-node orchestration approach for our proposed architecture using Kubernetes. We validate our architecture by executing SPIR-V code performing traffic classification with random forest inference. We test this application both on discrete and integrated graphics cards and on x86 and ARM. We find that in all cases the GPUs are faster than the baseline Cython code