Modelling and Characterization of a Maze-Solving Mobile Robot Using Wall Follower Algorithm

Rosario, Jay Robert B. del; Sanidad, Jefferson G.; Lim, Allimzon M.; Uy, Pierre Stanley L.; Bacar, Allan Jeffrey C.; Cai, Mark Anthony D.; Dubouzet, Alec Zandrae A.

doi:10.4028/www.scientific.net/amm.446-447.1245

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…We select both resource intensive applications better suited for cloud resources, and more lightweight services that edge devices can accommodate. These include S1: face recognition (identify human faces using FaceNet [118]), S2: tree recognition (identify trees using a CNN from TensorFlow's Model Zoo [24,28]), S3: drone detection (detect other drones using an SVM classifier trained for the orange tag all our drones have [5]), S4: obstacle avoidance (detect obstacles in the drone's vicinity and adjusts course to avoid them, using the obstacle detection framework in ardrone-autonomy [5]), S5: people deduplication (disambiguate between faces using FaceNet [118]), S6: maze (navigate through a walled maze using the Wall Follower algorithm [22,51]), S7: weather analytics (weather prediction based on temperature and humidity levels in sensor data), S8: soil analytics (estimation of soil hydration from images and humidity sensor), S9: text recognition (image to text conversion of signs), and finally S10: simultaneous localization and mapping (SLAM, using image and sensor data) [4]. We evaluate one service at a time to eliminate interference, however, the platform supports multi-tenancy.…”

Section: Methodsmentioning

confidence: 99%

A Hardware-Software Stack for Serverless Edge Swarms

Patterson¹,

Pigorovsky²,

Dempsey³

et al. 2021

Preprint

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Swarms of autonomous devices are increasing in ubiquity and size, making the need for rethinking their hardwaresoftware system stack critical.We present HiveMind, the first swarm coordination platform that enables programmable execution of complex task workflows between cloud and edge resources in a performant and scalable manner. HiveMind is a software-hardware platform that includes a domain-specific language to simplify programmability of cloud-edge applications, a program synthesis tool to automatically explore task placement strategies, a centralized controller that leverages serverless computing to elastically scale cloud resources, and a reconfigurable hardware acceleration fabric for network and remote memory accesses.We design and build the full end-to-end HiveMind system on two real edge swarms comprised of drones and robotic cars. We quantify the opportunities and challenges serverless introduces to edge applications, as well as the trade-offs between centralized and distributed coordination. We show that Hive-Mind achieves significantly better performance predictability and battery efficiency compared to existing centralized and decentralized platforms, while also incurring lower network traffic. Using both real systems and a validated simulator we show that HiveMind can scale to thousands of edge devices without sacrificing performance or efficiency, demonstrating that centralized platforms can be both scalable and performant.

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Section: Methodsmentioning

confidence: 99%

A Hardware-Software Stack for Serverless Edge Swarms

Patterson¹,

Pigorovsky²,

Dempsey³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…𝑣 𝑅 = 𝑟 𝜔 𝑅 (6) 𝑣 𝐿 = 𝑟 𝜔 𝐿 (7) Here, r is the radius of the wheel and ω is the angular velocity of the respective motor. The motion and angular motion of the robot from independent wheel movements are shown in equations 8 and 9.…”

Section: S Go Straightmentioning

confidence: 99%

“…Wall tracing algorithms can be used for this process. Despite this being a simple concept, solutions are impossible in complex environments [7]. Wall tracing algorithms have been used along with other algorithms to find solutions [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimized Graph Search Algorithms for Exploration with Mobile Robot

Güllü

Kuscu

2021

EMITTER Int'l J. of Engin. Technol.

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Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.

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“…Wall following algorithm is a simple but highly effective method used for exploring and mapping of indoor environments [16]. Details of the wall following algorithm is discussed in a related study [17].…”

Section: Wall Following Algorithmmentioning

confidence: 99%

A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

Oral

Turgut

Arıkan

2020

Hittite J. Sci. Eng.

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R ecently, quadrotors gained popularity due to their high maneuverability, cost and vertical takeoff/landing capabilities. Nevertheless, they also have disadvantages such as they have a limited flight time and small payload capabilities. In addition to these, a major part of the energy of a quadrotor is spent against gravity for hovering. Still, they are one of the most adopted air vehicles for commercial and research purposes. Most of the time UAV's (Unmanned Aerial Vehicles) are used in outdoor applications such as surveillance, search/rescue and patrolling. Recently, UAV's started to find uses in indoor environments. Material handling in manufacturing and inspection in harsh environments are to name a few[1] of these applications. One of the most promising applications is to utilize them in urban relief and disaster operations where a UAV moves autonomously avoiding obstacles in GPS-denied buildings to help human operators for rescue operations.In order to navigate in an indoor environment, a map of the environment is needed. However, in most of the cases either the map is unknown or some partial information about the environment is available. Indoor mapping process can be performed by using seve-Article History:

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Modelling and Characterization of a Maze-Solving Mobile Robot Using Wall Follower Algorithm

Cited by 10 publications

References 3 publications

A Hardware-Software Stack for Serverless Edge Swarms

A Hardware-Software Stack for Serverless Edge Swarms

Optimized Graph Search Algorithms for Exploration with Mobile Robot

A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

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