The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path that connects a start state and a goal state while meeting the required constraints. Computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches for addressing complex real-world problems for which mathematical or traditional modelling does not perform well. It has been applied in the field of UAVs since it can yield effective, accurate and rapid solutions. This article provides an overview of studies on UAV path planning based on CI methods published in major journals and conference proceedings. We survey relevant studies with respect to different CI algorithms utilized in UAV path planning, the types of time domain in UAV path planning, namely, offline and online, and the types of environment models, namely, 2D and 3D. It is observed that CI methods outperform traditional methods on online and 3D problems. The analysis is useful for identifying key results from UAV path planning research and is leveraged in this article to highlight trends and open issues.

Survey on computational-intelligence-based UAV path planning

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle UAV technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a "Post Disaster" is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.

A Disaster Management Specific Mobility Model for Flying Ad-hoc Network

Unmanned Systems has been taking place of manned systems in several fields like aviation. Unmanned Aerial Vehicle (UAV), one of the most popular and effective unmanned systems, is gradually becoming the vital element of aviation because of its high success rate in both military and civilian missions. Basic problem of UAV is finding the best path in tough environment. Coverage zones of radars and complex environment are the main obstacles in this problem. A UAV intends to travel all control points in an optimal way to be more productive while avoiding radars. In this paper, we used Genetic Algorithm (GA), which is Evolutionary algorithm, to find the optimal flyable path for the UAVs in a 3D environment. Each generation is anticipated to be better than its previous generation in GA. For the purpose of reaching an optimal path, solving the Travelling Salesman Problem (TSP) is one of the major phases in the proposed method. In order to show the visual of solution in better quality, we preferred MATLAB as the implementation environment. Additionally, there is a shared library and mathematical calculations are easier in MATLAB. The complexity of our problem can be increased by adding extra constraints caused by the dynamic environment as the future works. Experimental results show that GA can be opted for optimal path planning for the UAVs.

Optimal path planning for UAVs using Genetic Algorithm

In this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post-disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human-swarm interfaces. All these aspects have been analyzed with respect to the state-of-the-art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real-time intervention.

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

Unmanned Aerial Vehicles (UAVS) are aircrafts without human pilots onboard. They are attracting increasing attention in recent years because of their important contribution and cost effective application in many tasks. How to find a safe and collision-free path in the environment with obstacles is always an important issue in Unmanned Aerial Vehicles (UAVS). In order to solve the problem of path planning for UAV in dynamic environment, this paper proposes a path planning method with an improved artificial potential field to overcome the local minimum problem. According to detects of the artificial potential field, this paper improves the APF through changing the repulsive potential function. The performance of simulation shows that the improved APF method can help the UAV avoid collisions with obstacles effectively and find the optimal path relatively from the start to the goal by choosing a proper m.

Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function

The planning of path for Unmanned Aerial Vehicle (UAV) is always considered to be a vital task. Path planning for UAV for avoiding the obstacle in its path can be accomplished by finding the solution for an optimization problem. Genetic Algorithm which is a global optimization tool can be of great use to solve the optimization problem for path planning of UAV. Artificial Neural Network (ANN) works well for function fitting quickly and can be used to approximate almost any function. The Genetic Algorithms are good at converging to the globally optimum solution generation by generation. Each generation is expected to be better than its previous generation. Neural Networks work faster than Genetic Algorithms for finding the solution to a given problem but may get converged to local optimum instead of global optimum. In this paper a new method for path planning for UAV to avoid obstacle coming in its path based on the combination of Genetic Algorithms and Artificial Neural Networks has been proposed in which the output generated from the Genetic Algorithms is used to train the network of Artificial Neural Networks. The model for path planning is based on 3D digital map.

S Aditya Gautam

Papers

Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D