A Brief Review of the Intelligent Algorithm for Traveling Salesman Problem in UAV Route Planning

Xu, Yunpeng; Che, Chang

doi:10.1109/iceiec.2019.8784651

Cited by 30 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the work of Xu and Che [ 22 ], a brief review of algorithms to solve the traveling salesman problem is presented. In Liu et al [ 23 ], the routing algorithm considers the age of the collected information as a priority.…”

Section: Work Descriptionmentioning

confidence: 99%

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Goulart

Pinto

Boava

et al. 2022

Sensors

View full text Add to dashboard Cite

The goal of this work is to present a systematic literature mapping (SLM) identifying algorithms for the search for data, determining the best path and types of communication between the local server and the drone, as well as possible simulators to validate proposed solutions. The concept, here considered as IoT Off-Grid, is characterized by being an environment without commercial electrical infrastructure and without communication connected to the internet. IoT equipment generates data to be stored on a local server. It collects these data through a drone that searches each local server for later integration with the commercial internet environment. As a result, we have algorithms to determine the best path based on the TSP—travelling salesman problem. Different types of communication between the drone and the server contain the data, predominantly WiFi 802.11. As a simulator, OMNeT++ stands out.

show abstract

Section: Work Descriptionmentioning

confidence: 99%

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Goulart

Pinto

Boava

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…To further reduce the complexity of selecting the optimal path, the path planning problem can be converted to the TSP [ 63 ]. The optimization methods for solving the TSP include metaheuristic algorithms and fuzzy neural networks.…”

Section: Related Workmentioning

confidence: 99%

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Behjati

Noh

Alobaidy

et al. 2021

Sensors

View full text Add to dashboard Cite

Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

show abstract

“…tour) of visiting each node in a given graph and finally returning to the starting node such that the overall traversal cost is minimized. TSP and its variants are widely used in robotics for applications like path planning for UAVs [1], multi-robot path planning [2], task allocation for robotic manipulators [3], coverage planning [4].…”

Section: Introductionmentioning

confidence: 99%

“…of Mechanical and Industrial Engineering, Ryerson University. Xx Emails: {ishaan.mehta, s.saeedi}@ryerson.ca 1 Project website: https://sites.google.com/view/pa-net-btsp [8]- [10]. In practice, these evolutionary based methods suffer in performance and computation time with an increase in the scale of the problem [11].…”

Section: Introductionmentioning

confidence: 99%

“…We use PA-Net to generate BTSP tours for a robotic planning application. Our main contributions are: (1) The drawback of existing approaches [13] is costly training of separate networks for different preferences in objective space. Our method avoids this costly training through the use of a single network, while providing generalizability over different preferences in objective space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP

Mehta¹,

Saeedi²

2022

Preprint

View full text Add to dashboard Cite

Travelling salesperson problem (TSP) is a classic resource allocation problem used to find an optimal order of doing a set of tasks while minimizing (or maximizing) an associated objective function. It is widely used in robotics for applications such as planning, scheduling etc. In this work, we solve TSP for two objectives using reinforcement learning. Often in multi objective optimization problems, the associated objective functions can be conflicting in nature. In such cases, the optimality is defined in terms of Pareto optimality. A set of these Pareto Optimal solutions in the objective space form a Pareto front (or frontier). Each solution has its own trade off. In this work, we present PA-Net, a network that generates good approximations of the Pareto front for the bi-objective travelling salesperson problem (BTSP). Firstly, BTSP is converted into a constrained optimization problem. We then train our network to solve this constrained problem using the Lagrangian relaxation and policy gradient. With PA-Net we are able to generate good quality Pareto fronts with fast inference times. Finally, we present the application of PA-Net to find optimal visiting order in a robotic navigation task/coverage planning 1 .

show abstract

A Brief Review of the Intelligent Algorithm for Traveling Salesman Problem in UAV Route Planning

Cited by 30 publications

References 27 publications

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

LoRa Communications as an Enabler for Internet of Drones towards Large-Scale Livestock Monitoring in Rural Farms

Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP

Contact Info

Product

Resources

About