UAV-Robot Relationship for Coordination of Robots on a Collision Free Path

Upadhyay, Ashish; Shrimali, Kushashwa Ravi; Shukla, Anupam

doi:10.1016/j.procs.2018.07.052

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…On the other hand, 2D path planning needs less effort with the probabilistic roadmaps (PRM) algorithms [5,6] from 3D path planning [7]. In some research, after the swarm UAVs collect the data, the data is immediately loaded into the cloud [8,9,10].…”

Section: Related Workmentioning

confidence: 99%

Q-LEARNING BASED OBSTACLE AVOIDANCE DATA HARVESTING MODEL USING UAV and UGV

Akin

Şahin

2023

EJT

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The Internet of Things (IoT) has revolutionized our lives by providing convenience in various aspects of our lives. However, for the IoT environment to function optimally, it is crucial to regularly collect data from IoT devices. This is because timely data collection enables more accurate evaluations and insights. Additionally, energy conservation is another crucial aspect to consider when collecting data, as it can have a significant impact on the sustainability of the IoT ecosystem. To achieve this, Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are increasingly being used to collect data. In this study, we delve into the problem of how UAVs and UGVs can effectively and efficiently collect data from IoT devices in an environment with obstacles. To address this challenge, we propose a Q-learning-based Obstacle Avoidance Data Harvesting (QOA-DH) method, which utilizes the principles of reinforcement learning to make decisions on data collection. Additionally, we conduct a comparison of the performance of UAVs and UGVs, considering the different restrictions and assumptions that are unique to each type of vehicle. This research aims to improve the overall efficiency and effectiveness of data collection in IoT environments and pave the way for sustainable IoT solutions.

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Section: Related Workmentioning

confidence: 99%

Q-LEARNING BASED OBSTACLE AVOIDANCE DATA HARVESTING MODEL USING UAV and UGV

Akin

Şahin

2023

EJT

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“…The problem of cooperative timing of multiple UAVs was discussed by the authors in reference 53 . The authors in reference 52 compared the A*(star) algorithm and probabilistic road map algorithms on the basis of time taken. In case of multiple UAVs, the delay between the arrival of UAVs need to be optimized.…”

Section: Need Of Path Planningmentioning

confidence: 99%

Path planning in unmanned aerial vehicles: An optimistic overview

Shahid

Abrar

Ajmal

et al. 2022

Int J Communication

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Summary With the development in the technology, a rapid increase in the use of unmanned aerial vehicle (UAV) is observed. UAVs are executing tasks that were previously performed by humans or manned aircraft, thus reducing the man workload and saving time. Path planning of UAVs is one of the most researched topics these days. Researchers are investigating more about UAVs and path planning to make it more feasible and economical. The major issue faced while planning a feasible path is to detect and avoid the obstacles encountered during a mission. This paper presents a detailed analysis of path planning in UAVs. Important aspects of path planning, that is, environment, dimensions, obstacles, and type and number of UAVs used in path planning, are also being discussed. Obstacle scenarios which UAV may come across during a mission and constraints which UAV has to follow for successful mission are briefly mentioned. Optimization techniques used to optimize the UAV path are also presented. In addition, future challenges and issues are also discussed.

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“…Although it is not oriented to UAVs, it presents an algorithm based on PRM to establish optimal paths in radioactive environments in order to minimize the exposure of people in hostile environments of this type. Similarly, Upadhyay et al [20] used a PRM in 2D and then algorithm A* to find an optimal set of paths for a system formed by multiple robots, which are coordinated with a UAV.…”

Section: D Path Planningmentioning

confidence: 99%

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

Madridano

Al-Kaff

Martín

et al. 2020

Sensors

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The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.

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UAV-Robot Relationship for Coordination of Robots on a Collision Free Path

Cited by 7 publications

References 4 publications

Q-LEARNING BASED OBSTACLE AVOIDANCE DATA HARVESTING MODEL USING UAV and UGV

Q-LEARNING BASED OBSTACLE AVOIDANCE DATA HARVESTING MODEL USING UAV and UGV

Path planning in unmanned aerial vehicles: An optimistic overview

3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

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