2,679 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
On Partially Controlled Multi-Agent Systems
Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.Comment: See http://www.jair.org/ for any accompanying file
Evolutionary Robotics
info:eu-repo/semantics/publishedVersio
MultiโAgent Foraging: stateโofโtheโart and research challenges
International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic
Society-in-the-Loop: Programming the Algorithmic Social Contract
Recent rapid advances in Artificial Intelligence (AI) and Machine Learning
have raised many questions about the regulatory and governance mechanisms for
autonomous machines. Many commentators, scholars, and policy-makers now call
for ensuring that algorithms governing our lives are transparent, fair, and
accountable. Here, I propose a conceptual framework for the regulation of AI
and algorithmic systems. I argue that we need tools to program, debug and
maintain an algorithmic social contract, a pact between various human
stakeholders, mediated by machines. To achieve this, we can adapt the concept
of human-in-the-loop (HITL) from the fields of modeling and simulation, and
interactive machine learning. In particular, I propose an agenda I call
society-in-the-loop (SITL), which combines the HITL control paradigm with
mechanisms for negotiating the values of various stakeholders affected by AI
systems, and monitoring compliance with the agreement. In short, `SITL = HITL +
Social Contract.'Comment: (in press), Ethics of Information Technology, 201
ํ์ ๋ก๋ด์ ์ํ ์๋น์ค ๊ธฐ๋ฐ๊ณผ ๋ชจ๋ธ ๊ธฐ๋ฐ์ ์ํํธ์จ์ด ๊ฐ๋ฐ ๋ฐฉ๋ฒ๋ก
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ํ์ํ.๊ฐ๊น์ด ๋ฏธ๋์๋ ๋ค์ํ ๋ก๋ด์ด ๋ค์ํ ๋ถ์ผ์์ ํ๋์ ์๋ฌด๋ฅผ ํ๋ ฅํ์ฌ ์ํํ๋ ๋ชจ์ต์ ํํ ๋ณผ ์ ์๊ฒ ๋ ๊ฒ์ด๋ค. ๊ทธ๋ฌ๋ ์ค์ ๋ก ์ด๋ฌํ ๋ชจ์ต์ด ์คํ๋๊ธฐ์๋ ๋ ๊ฐ์ง์ ์ด๋ ค์์ด ์๋ค. ๋จผ์ ๋ก๋ด์ ์ด์ฉํ๊ธฐ ์ํ ์ํํธ์จ์ด๋ฅผ ๋ช
์ธํ๋ ๊ธฐ์กด ์ฐ๊ตฌ๋ค์ ๋๋ถ๋ถ ๊ฐ๋ฐ์๊ฐ ๋ก๋ด์ ํ๋์จ์ด์ ์ํํธ์จ์ด์ ๋ํ ์ง์์ ์๊ณ ์๋ ๊ฒ์ ๊ฐ์ ํ๊ณ ์๋ค. ๊ทธ๋์ ๋ก๋ด์ด๋ ์ปดํจํฐ์ ๋ํ ์ง์์ด ์๋ ์ฌ์ฉ์๋ค์ด ์ฌ๋ฌ ๋์ ๋ก๋ด์ด ํ๋ ฅํ๋ ์๋๋ฆฌ์ค๋ฅผ ์์ฑํ๊ธฐ๋ ์ฝ์ง ์๋ค. ๋ํ, ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ ๋ ๋ก๋ด์ ํ๋์จ์ด์ ํน์ฑ๊ณผ ๊ด๋ จ์ด ๊น์ด์, ๋ค์ํ ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ๋ ๊ฒ๋ ๊ฐ๋จํ์ง ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์์ ์์ค์ ๋ฏธ์
๋ช
์ธ์ ๋ก๋ด์ ํ์ ํ๋ก๊ทธ๋๋ฐ์ผ๋ก ๋๋์ด ์๋ก์ด ์ํํธ์จ์ด ๊ฐ๋ฐ ํ๋ ์์ํฌ๋ฅผ ์ ์ํ๋ค. ๋ํ, ๋ณธ ํ๋ ์์ํฌ๋ ํฌ๊ธฐ๊ฐ ์์ ๋ก๋ด๋ถํฐ ๊ณ์ฐ ๋ฅ๋ ฅ์ด ์ถฉ๋ถํ ๋ก๋ด๋ค์ด ์๋ก ๊ตฐ์ง์ ์ด๋ฃจ์ด ๋ฏธ์
์ ์ํํ ์ ์๋๋ก ์ง์ํ๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ๋ก๋ด์ ํ๋์จ์ด๋ ์ํํธ์จ์ด์ ๋ํ ์ง์์ด ๋ถ์กฑํ ์ฌ์ฉ์๋ ๋ก๋ด์ ๋์์ ์์ ์์ค์์ ๋ช
์ธํ ์ ์๋ ์คํฌ๋ฆฝํธ ์ธ์ด๋ฅผ ์ ์ํ๋ค. ์ ์ํ๋ ์ธ์ด๋ ๊ธฐ์กด์ ์คํฌ๋ฆฝํธ ์ธ์ด์์๋ ์ง์ํ์ง ์๋ ๋ค ๊ฐ์ง์ ๊ธฐ๋ฅ์ธ ํ์ ๊ตฌ์ฑ, ๊ฐ ํ์ ์๋น์ค ๊ธฐ๋ฐ ํ๋ก๊ทธ๋๋ฐ, ๋์ ์ผ๋ก ๋ชจ๋ ๋ณ๊ฒฝ, ๋ค์ค ์์
(๋ฉํฐ ํ์คํน)์ ์ง์ํ๋ค. ์ฐ์ ๋ก๋ด์ ํ์ผ๋ก ๊ทธ๋ฃน ์ง์ ์ ์๊ณ , ๋ก๋ด์ด ์ํํ ์ ์๋ ๊ธฐ๋ฅ์ ์๋น์ค ๋จ์๋ก ์ถ์ํํ์ฌ ์๋ก์ด ๋ณตํฉ ์๋น์ค๋ฅผ ๋ช
์ธํ ์ ์๋ค. ๋ํ ๋ก๋ด์ ๋ฉํฐ ํ์คํน์ ์ํด 'ํ๋' ์ด๋ผ๋ ๊ฐ๋
์ ๋์
ํ์๊ณ , ๋ณตํฉ ์๋น์ค ๋ด์์ ์ด๋ฒคํธ๋ฅผ ๋ฐ์์์ผ์ ๋์ ์ผ๋ก ๋ชจ๋๊ฐ ๋ณํํ ์ ์๋๋ก ํ์๋ค. ๋์๊ฐ ์ฌ๋ฌ ๋ก๋ด์ ํ๋ ฅ์ด ๋์ฑ ๊ฒฌ๊ณ ํ๊ณ , ์ ์ฐํ๊ณ , ํ์ฅ์ฑ์ ๋์ด๊ธฐ ์ํด, ๊ตฐ์ง ๋ก๋ด์ ์ด์ฉํ ๋ ๋ก๋ด์ด ์๋ฌด๋ฅผ ์ํํ๋ ๋์ค์ ๋ฌธ์ ๊ฐ ์๊ธธ ์ ์์ผ๋ฉฐ, ์ํฉ์ ๋ฐ๋ผ ๋ก๋ด์ ๋์ ์ผ๋ก ๋ค๋ฅธ ํ์๋ฅผ ์ํํ ์ ์๋ค๊ณ ๊ฐ์ ํ๋ค. ์ด๋ฅผ ์ํด ๋์ ์ผ๋ก๋ ํ์ ๊ตฌ์ฑํ ์ ์๊ณ , ์ฌ๋ฌ ๋์ ๋ก๋ด์ด ํ๋์ ์๋น์ค๋ฅผ ์ํํ๋ ๊ทธ๋ฃน ์๋น์ค๋ฅผ ์ง์ํ๊ณ , ์ผ๋ ๋ค ํต์ ๊ณผ ๊ฐ์ ์๋ก์ด ๊ธฐ๋ฅ์ ์คํฌ๋ฆฝํธ ์ธ์ด์ ๋ฐ์ํ์๋ค. ๋ฐ๋ผ์ ํ์ฅ๋ ์์ ์์ค์ ์คํฌ๋ฆฝํธ ์ธ์ด๋ ๋น์ ๋ฌธ๊ฐ๋ ๋ค์ํ ์ ํ์ ํ๋ ฅ ์๋ฌด๋ฅผ ์ฝ๊ฒ ๋ช
์ธํ ์ ์๋ค.
๋ก๋ด์ ํ์๋ฅผ ํ๋ก๊ทธ๋๋ฐํ๊ธฐ ์ํด ๋ค์ํ ์ํํธ์จ์ด ๊ฐ๋ฐ ํ๋ ์์ํฌ๊ฐ ์ฐ๊ตฌ๋๊ณ ์๋ค. ํนํ ์ฌ์ฌ์ฉ์ฑ๊ณผ ํ์ฅ์ฑ์ ์ค์ ์ผ๋ก ๋ ์ฐ๊ตฌ๋ค์ด ์ต๊ทผ ๋ง์ด ์ฌ์ฉ๋๊ณ ์์ง๋ง, ๋๋ถ๋ถ์ ์ด๋ค ์ฐ๊ตฌ๋ ๋ฆฌ๋
์ค ์ด์์ฒด์ ์ ๊ฐ์ด ๋ง์ ํ๋์จ์ด ์์์ ํ์๋ก ํ๋ ์ด์์ฒด์ ๋ฅผ ๊ฐ์ ํ๊ณ ์๋ค. ๋ํ, ํ๋ก๊ทธ๋จ์ ๋ถ์ ๋ฐ ์ฑ๋ฅ ์์ธก ๋ฑ์ ๊ณ ๋ คํ์ง ์๊ธฐ ๋๋ฌธ์, ์์ ์ ์ฝ์ด ์ฌํ ํฌ๊ธฐ๊ฐ ์์ ๋ก๋ด์ ์ํํธ์จ์ด๋ฅผ ๊ฐ๋ฐํ๊ธฐ์๋ ์ด๋ ต๋ค. ๊ทธ๋์ ๋ณธ ์ฐ๊ตฌ์์๋ ์๋ฒ ๋๋ ์ํํธ์จ์ด๋ฅผ ์ค๊ณํ ๋ ์ฐ์ด๋ ์ ํ์ ์ธ ๋ชจ๋ธ์ ์ด์ฉํ๋ค. ์ด ๋ชจ๋ธ์ ์ ์ ๋ถ์๊ณผ ์ฑ๋ฅ ์์ธก์ด ๊ฐ๋ฅํ์ง๋ง, ๋ก๋ด์ ํ์๋ฅผ ํํํ๊ธฐ์๋ ์ ์ฝ์ด ์๋ค. ๊ทธ๋์ ๋ณธ ๋
ผ๋ฌธ์์ ์ธ๋ถ์ ์ด๋ฒคํธ์ ์ํด ์ํ ์ค๊ฐ์ ํ์๋ฅผ ๋ณ๊ฒฝํ๋ ๋ก๋ด์ ์ํด ์ ํ ์ํ ๋จธ์ ๋ชจ๋ธ๊ณผ ๋ฐ์ดํฐ ํ๋ก์ฐ ๋ชจ๋ธ์ด ๊ฒฐํฉํ์ฌ ๋์ ํ์๋ฅผ ๋ช
์ธํ ์ ์๋ ํ์ฅ๋ ๋ชจ๋ธ์ ์ ์ฉํ๋ค. ๊ทธ๋ฆฌ๊ณ ๋ฅ๋ฌ๋๊ณผ ๊ฐ์ด ๊ณ์ฐ๋์ ๋ง์ด ํ์๋ก ํ๋ ์์ฉ์ ๋ถ์ํ๊ธฐ ์ํด, ๋ฃจํ ๊ตฌ์กฐ๋ฅผ ๋ช
์์ ์ผ๋ก ํํํ ์ ์๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ๋ง์ง๋ง์ผ๋ก ์ฌ๋ฌ ๋ก๋ด์ ํ์
์ด์ฉ์ ์ํด ๋ก๋ด ์ฌ์ด์ ๊ณต์ ๋๋ ์ ๋ณด๋ฅผ ๋ํ๋ด๊ธฐ ์ํด ๋ ๊ฐ์ง ๋ชจ๋ธ์ ์ฌ์ฉํ๋ค. ๋จผ์ ์ค์์์ ๊ณต์ ์ ๋ณด๋ฅผ ๊ด๋ฆฌํ๊ธฐ ์ํด ๋ผ์ด๋ธ๋ฌ๋ฆฌ ํ์คํฌ๋ผ๋ ํน๋ณํ ํ์คํฌ๋ฅผ ํตํด ๊ณต์ ์ ๋ณด๋ฅผ ๋ํ๋ธ๋ค. ๋ํ, ๋ก๋ด์ด ์์ ์ ์ ๋ณด๋ฅผ ๊ฐ๊น์ด ๋ก๋ด๋ค๊ณผ ๊ณต์ ํ๊ธฐ ์ํด ๋ฉํฐ์บ์คํ
์ ์ํ ์๋ก์ด ํฌํธ๋ฅผ ์ถ๊ฐํ๋ค. ์ด๋ ๊ฒ ํ์ฅ๋ ์ ํ์ ์ธ ๋ชจ๋ธ์ ์ค์ ๋ก๋ด ์ฝ๋๋ก ์๋ ์์ฑ๋์ด, ์ํํธ์จ์ด ์ค๊ณ ์์ฐ์ฑ ๋ฐ ๊ฐ๋ฐ ํจ์จ์ฑ์ ์ด์ ์ ๊ฐ์ง๋ค.
๋น์ ๋ฌธ๊ฐ๊ฐ ๋ช
์ธํ ์คํฌ๋ฆฝํธ ์ธ์ด๋ ์ ํ์ ์ธ ํ์คํฌ ๋ชจ๋ธ๋ก ๋ณํํ๊ธฐ ์ํด ์ค๊ฐ ๋จ๊ณ์ธ ์ ๋ต ๋จ๊ณ๋ฅผ ์ถ๊ฐํ์๋ค. ์ ์ํ๋ ๋ฐฉ๋ฒ๋ก ์ ํ๋น์ฑ์ ๊ฒ์ฆํ๊ธฐ ์ํด, ์๋ฎฌ๋ ์ด์
๊ณผ ์ฌ๋ฌ ๋์ ์ค์ ๋ก๋ด์ ์ด์ฉํ ํ์
ํ๋ ์๋๋ฆฌ์ค์ ๋ํด ์คํ์ ์งํํ์๋ค.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid.
For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily.
For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly.
By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Contribution 7
1.3 Dissertation Organization 9
Chapter 2. Background and Existing Research 11
2.1 Terminologies 11
2.2 Robot Software Development Frameworks 25
2.3 Parallel Embedded Software Development Framework 31
Chapter 3. Overview of the SeMo Framework 41
3.1 Motivational Examples 45
Chapter 4. Robot Behavior Programming 47
4.1 Related works 48
4.2 Model-based Task Graph Specification for Individual Robots 56
4.3 Model-based Task Graph Specification for Cooperating Robots 70
4.4 Automatic Code Generation 74
4.5 Experiments 78
Chapter 5. High-level Mission Specification 81
5.1 Service-oriented Mission Specification 82
5.2 Strategy Description 93
5.3 Automatic Task Graph Generation 96
5.4 Related works 99
5.5 Experiments 104
Chapter 6. Conclusion 114
6.1 Future Research 116
Bibliography 118
Appendices 133
์์ฝ 158Docto
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