140 research outputs found

    Routing and video streaming in drone networks

    Get PDF
    PhDDrones can be used for several civil applications including search and rescue, coverage, and aerial imaging. Newer applications like construction and delivery of goods are also emerging. Performing tasks as a team of drones is often beneficial but requires coordination through communication. In this thesis, the communication requirements of video streaming drone applications based on existing works are studied. The existing communication technologies are then analyzed to understand if the communication requirements posed by these drone applications can be met by the available technologies. The shortcomings of existing technologies with respect to drone applications are identified and potential requirements for future technologies are suggested. The existing communication and routing protocols including ad-hoc on-demand distance vector (AODV), location-aided routing (LAR), and greedy perimeter stateless routing (GPSR) protocols are studied to identify their limitations in context to the drone networks. An application scenario where a team of drones covers multiple areas of interest is considered, where the drones follow known trajectories and transmit continuous streams of sensed traffic (images or video) to a ground station. A route switching (RS) algorithm is proposed that utilizes both the location and the trajectory information of the drones to schedule and update routes to overcome route discovery and route error overhead. Simulation results show that the RS scheme outperforms LAR and AODV by achieving higher network performance in terms of throughput and delay. Video streaming drone applications such as search and rescue, surveillance, and disaster management, benefit from multicast wireless video streaming to transmit identical data to multiple users. Video multicast streaming using IEEE 802.11 poses challenges of reliability, performance, and fairness under tight delay bounds. Because of the mobility of the video sources and the high data-rate of the videos, the transmission rate should be adapted based on receivers' link conditions. Rate-adaptive video multicast streaming in IEEE 802.11 requires wireless link estimation as well as frequent feedback from multiple receivers. A contribution to this thesis is an application-layer rate-adaptive video multicast streaming framework using an 802.11 ad-hoc network that is applicable when both the sender and the receiver nodes are mobile. The receiver nodes of a multicast group are assigned with roles dynamically based on their link conditions. An application layer video multicast gateway (ALVM-GW) adapts the transmission rate and the video encoding rate based on the received feedback. Role switching between multiple receiver nodes (designated nodes) cater for mobility and rate adaptation addresses the challenges of performance and fairness. The reliability challenge is addressed through re-transmission of lost packets while delays under given bounds are achieved through video encoding rate adaptation. Emulation and experimental results show that the proposed approach outperforms legacy multicast in terms of packet loss and video quality

    Adoption of vehicular ad hoc networking protocols by networked robots

    Get PDF
    This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

    ํ˜‘์—… ๋กœ๋ด‡์„ ์œ„ํ•œ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜๊ณผ ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก 

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ํ•˜์ˆœํšŒ.๊ฐ€๊นŒ์šด ๋ฏธ๋ž˜์—๋Š” ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์ด ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•˜๋‚˜์˜ ์ž„๋ฌด๋ฅผ ํ˜‘๋ ฅํ•˜์—ฌ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ชจ์Šต์€ ํ”ํžˆ ๋ณผ ์ˆ˜ ์žˆ๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ๋กœ ์ด๋Ÿฌํ•œ ๋ชจ์Šต์ด ์‹คํ˜„๋˜๊ธฐ์—๋Š” ๋‘ ๊ฐ€์ง€์˜ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๋จผ์ € ๋กœ๋ด‡์„ ์šด์šฉํ•˜๊ธฐ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋ช…์„ธํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋Œ€๋ถ€๋ถ„ ๊ฐœ๋ฐœ์ž๊ฐ€ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์™€ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์„ ์•Œ๊ณ  ์žˆ๋Š” ๊ฒƒ์„ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋กœ๋ด‡์ด๋‚˜ ์ปดํ“จํ„ฐ์— ๋Œ€ํ•œ ์ง€์‹์ด ์—†๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ˜‘๋ ฅํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ž‘์„ฑํ•˜๊ธฐ๋Š” ์‰ฝ์ง€ ์•Š๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•  ๋•Œ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์˜ ํŠน์„ฑ๊ณผ ๊ด€๋ จ์ด ๊นŠ์–ด์„œ, ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ๋„ ๊ฐ„๋‹จํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒ์œ„ ์ˆ˜์ค€์˜ ๋ฏธ์…˜ ๋ช…์„ธ์™€ ๋กœ๋ด‡์˜ ํ–‰์œ„ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ƒˆ๋กœ์šด ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ๋ณธ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡๋ถ€ํ„ฐ ๊ณ„์‚ฐ ๋Šฅ๋ ฅ์ด ์ถฉ๋ถ„ํ•œ ๋กœ๋ด‡๋“ค์ด ์„œ๋กœ ๊ตฐ์ง‘์„ ์ด๋ฃจ์–ด ๋ฏธ์…˜์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด๋‚˜ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์ด ๋ถ€์กฑํ•œ ์‚ฌ์šฉ์ž๋„ ๋กœ๋ด‡์˜ ๋™์ž‘์„ ์ƒ์œ„ ์ˆ˜์ค€์—์„œ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์–ธ์–ด๋Š” ๊ธฐ์กด์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์—์„œ๋Š” ์ง€์›ํ•˜์ง€ ์•Š๋Š” ๋„ค ๊ฐ€์ง€์˜ ๊ธฐ๋Šฅ์ธ ํŒ€์˜ ๊ตฌ์„ฑ, ๊ฐ ํŒ€์˜ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ, ๋™์ ์œผ๋กœ ๋ชจ๋“œ ๋ณ€๊ฒฝ, ๋‹ค์ค‘ ์ž‘์—…(๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น)์„ ์ง€์›ํ•œ๋‹ค. ์šฐ์„  ๋กœ๋ด‡์€ ํŒ€์œผ๋กœ ๊ทธ๋ฃน ์ง€์„ ์ˆ˜ ์žˆ๊ณ , ๋กœ๋ด‡์ด ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋Šฅ์„ ์„œ๋น„์Šค ๋‹จ์œ„๋กœ ์ถ”์ƒํ™”ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ณตํ•ฉ ์„œ๋น„์Šค๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋กœ๋ด‡์˜ ๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น์„ ์œ„ํ•ด 'ํ”Œ๋žœ' ์ด๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•˜์˜€๊ณ , ๋ณตํ•ฉ ์„œ๋น„์Šค ๋‚ด์—์„œ ์ด๋ฒคํŠธ๋ฅผ ๋ฐœ์ƒ์‹œ์ผœ์„œ ๋™์ ์œผ๋กœ ๋ชจ๋“œ๊ฐ€ ๋ณ€ํ™˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘๋ ฅ์ด ๋”์šฑ ๊ฒฌ๊ณ ํ•˜๊ณ , ์œ ์—ฐํ•˜๊ณ , ํ™•์žฅ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด, ๊ตฐ์ง‘ ๋กœ๋ด‡์„ ์šด์šฉํ•  ๋•Œ ๋กœ๋ด‡์ด ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋„์ค‘์— ๋ฌธ์ œ๊ฐ€ ์ƒ๊ธธ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋กœ๋ด‡์„ ๋™์ ์œผ๋กœ ๋‹ค๋ฅธ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋™์ ์œผ๋กœ๋„ ํŒ€์„ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๊ณ , ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ•˜๋‚˜์˜ ์„œ๋น„์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ทธ๋ฃน ์„œ๋น„์Šค๋ฅผ ์ง€์›ํ•˜๊ณ , ์ผ๋Œ€ ๋‹ค ํ†ต์‹ ๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์— ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํ™•์žฅ๋œ ์ƒ์œ„ ์ˆ˜์ค€์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ๋น„์ „๋ฌธ๊ฐ€๋„ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ํ˜‘๋ ฅ ์ž„๋ฌด๋ฅผ ์‰ฝ๊ฒŒ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ”„๋กœ๊ทธ๋ž˜๋ฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์žฌ์‚ฌ์šฉ์„ฑ๊ณผ ํ™•์žฅ์„ฑ์„ ์ค‘์ ์œผ๋กœ ๋‘” ์—ฐ๊ตฌ๋“ค์ด ์ตœ๊ทผ ๋งŽ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์ด๋“ค ์—ฐ๊ตฌ๋Š” ๋ฆฌ๋ˆ…์Šค ์šด์˜์ฒด์ œ์™€ ๊ฐ™์ด ๋งŽ์€ ํ•˜๋“œ์›จ์–ด ์ž์›์„ ํ•„์š”๋กœ ํ•˜๋Š” ์šด์˜์ฒด์ œ๋ฅผ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ํ”„๋กœ๊ทธ๋žจ์˜ ๋ถ„์„ ๋ฐ ์„ฑ๋Šฅ ์˜ˆ์ธก ๋“ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์—, ์ž์› ์ œ์•ฝ์ด ์‹ฌํ•œ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๊ธฐ์—๋Š” ์–ด๋ ต๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„๋ฒ ๋””๋“œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์„ค๊ณ„ํ•  ๋•Œ ์“ฐ์ด๋Š” ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์„ ์ด์šฉํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ์ •์  ๋ถ„์„๊ณผ ์„ฑ๋Šฅ ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜์ง€๋งŒ, ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ‘œํ˜„ํ•˜๊ธฐ์—๋Š” ์ œ์•ฝ์ด ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์™ธ๋ถ€์˜ ์ด๋ฒคํŠธ์— ์˜ํ•ด ์ˆ˜ํ–‰ ์ค‘๊ฐ„์— ํ–‰์œ„๋ฅผ ๋ณ€๊ฒฝํ•˜๋Š” ๋กœ๋ด‡์„ ์œ„ํ•ด ์œ ํ•œ ์ƒํƒœ ๋จธ์‹  ๋ชจ๋ธ๊ณผ ๋ฐ์ดํ„ฐ ํ”Œ๋กœ์šฐ ๋ชจ๋ธ์ด ๊ฒฐํ•ฉํ•˜์—ฌ ๋™์  ํ–‰์œ„๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ํ™•์žฅ๋œ ๋ชจ๋ธ์„ ์ ์šฉํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋”ฅ๋Ÿฌ๋‹๊ณผ ๊ฐ™์ด ๊ณ„์‚ฐ๋Ÿ‰์„ ๋งŽ์ด ํ•„์š”๋กœ ํ•˜๋Š” ์‘์šฉ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ๋ฃจํ”„ ๊ตฌ์กฐ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘์—… ์šด์šฉ์„ ์œ„ํ•ด ๋กœ๋ด‡ ์‚ฌ์ด์— ๊ณต์œ ๋˜๋Š” ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋จผ์ € ์ค‘์•™์—์„œ ๊ณต์œ  ์ •๋ณด๋ฅผ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ํƒœ์Šคํฌ๋ผ๋Š” ํŠน๋ณ„ํ•œ ํƒœ์Šคํฌ๋ฅผ ํ†ตํ•ด ๊ณต์œ  ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์ด ์ž์‹ ์˜ ์ •๋ณด๋ฅผ ๊ฐ€๊นŒ์šด ๋กœ๋ด‡๋“ค๊ณผ ๊ณต์œ ํ•˜๊ธฐ ์œ„ํ•ด ๋ฉ€ํ‹ฐ์บ์ŠคํŒ…์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํฌํŠธ๋ฅผ ์ถ”๊ฐ€ํ•œ๋‹ค. ์ด๋ ‡๊ฒŒ ํ™•์žฅ๋œ ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์€ ์‹ค์ œ ๋กœ๋ด‡ ์ฝ”๋“œ๋กœ ์ž๋™ ์ƒ์„ฑ๋˜์–ด, ์†Œํ”„ํŠธ์›จ์–ด ์„ค๊ณ„ ์ƒ์‚ฐ์„ฑ ๋ฐ ๊ฐœ๋ฐœ ํšจ์œจ์„ฑ์— ์ด์ ์„ ๊ฐ€์ง„๋‹ค. ๋น„์ „๋ฌธ๊ฐ€๊ฐ€ ๋ช…์„ธํ•œ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ์ •ํ˜•์ ์ธ ํƒœ์Šคํฌ ๋ชจ๋ธ๋กœ ๋ณ€ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด ์ค‘๊ฐ„ ๋‹จ๊ณ„์ธ ์ „๋žต ๋‹จ๊ณ„๋ฅผ ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์—ฌ๋Ÿฌ ๋Œ€์˜ ์‹ค์ œ ๋กœ๋ด‡์„ ์ด์šฉํ•œ ํ˜‘์—…ํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•ด ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.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 ์š”์•ฝ 158Docto

    A Survey on 5G Usage Scenarios and Traffic Models

    Get PDF
    The fifth-generation mobile initiative, 5G, is a tremendous and collective effort to specify, standardize, design, manufacture, and deploy the next cellular network generation. 5G networks will support demanding services such as enhanced Mobile Broadband, Ultra-Reliable and Low Latency Communications and massive Machine-Type Communications, which will require data rates of tens of Gbps, latencies of few milliseconds and connection densities of millions of devices per square kilometer. This survey presents the most significant use cases expected for 5G including their corresponding scenarios and traffic models. First, the paper analyzes the characteristics and requirements for 5G communications, considering aspects such as traffic volume, network deployments, and main performance targets. Secondly, emphasizing the definition of performance evaluation criteria for 5G technologies, the paper reviews related proposals from principal standards development organizations and industry alliances. Finally, well-defined and significant 5G use cases are provided. As a result, these guidelines will help and ease the performance evaluation of current and future 5G innovations, as well as the dimensioning of 5G future deployments.This work is partially funded by the Spanish Ministry of Economy and Competitiveness (project TEC2016-76795-C6-4-R)H2020 research and innovation project 5G-CLARITY (Grant No. 871428)Andalusian Knowledge Agency (project A-TIC-241-UGR18)
    • โ€ฆ
    corecore