Modeling, Control, State Estimation and Path Planning Methods for Autonomous Multirotor Aerial Robots

2018 ◽  
Vol 7 (3) ◽  
pp. 180-250 ◽  
Author(s):  
Christos Papachristos ◽  
Tung Dang ◽  
Shehryar Khattak ◽  
Frank Mascarich ◽  
Nikhil Khedekar ◽  
...  
Keyword(s):  
Path Planning ◽  
State Estimation ◽  
Aerial Robots ◽  
Planning Methods ◽  
Control State

Modeling, Control, State Estimation and Path Planning Methods for Autonomous Multirotor Aerial Robots

2018 ◽  
Author(s):  
Christos Papachristos ◽  
Tung Dang ◽  
Shehryar Khattak ◽  
Frank Mascarich ◽  
Nikhil Khedekar ◽  
...  
Keyword(s):  
Path Planning ◽  
State Estimation ◽  
Aerial Robots ◽  
Planning Methods ◽  
Control State

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

scholarly journals Multi-UAV Path Planning for Autonomous Missions in Mixed GNSS Coverage Scenarios

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4188 ◽  
Author(s):  
Flavia Causa ◽  
Giancarmine Fasano ◽  
Michele Grassi
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Variable Number ◽  
Global Navigation Satellite Systems ◽  
Routing Problem ◽  
Satellite Systems ◽  
Cooperative Strategies ◽  
Planning Methods ◽  
Global Navigation Satellite ◽  
Multi Uav

This paper presents an algorithm for multi-UAV path planning in scenarios with heterogeneous Global Navigation Satellite Systems (GNSS) coverage. In these environments, cooperative strategies can be effectively exploited when flying in GNSS-challenging conditions, e.g., natural/urban canyons, while the different UAVs can fly as independent systems in the absence of navigation issues (i.e., open sky conditions). These different flight environments are taken into account at path planning level, obtaining a distributed multi-UAV system that autonomously reconfigures itself based on mission needs. Path planning, formulated as a vehicle routing problem, aims at defining smooth and flyable polynomial trajectories, whose time of flight is estimated to guarantee coexistence of different UAVs at the same challenging area. The algorithm is tested in a simulation environment directly derived from a real-world 3D scenario, for variable number of UAVs and waypoints. Its solution and computational cost are compared with optimal planning methods. Results show that the computational burden is almost unaffected by the number of UAVs, and it is compatible with near real time implementation even for a relatively large number of waypoints. The provided solution takes full advantage from the available flight resources, reducing mission time for a given set of waypoints and for increasing UAV number.

scholarly journals Path Planning Methods in an Environment with Obstacles (A Review)

2018 ◽  
pp. 15-58 ◽  
Author(s):  
W. Liu
Keyword(s):  
Path Planning ◽  
Incomplete Information ◽  
Cell Decomposition ◽  
Reference Points ◽  
Mobile Robot Navigation ◽  
Problem Solution ◽  
Local Path Planning ◽  
Starting Point ◽  
Planning Methods ◽  
Local Path

Planning the path is the most important task in the mobile robot navigation. This task involves basically three aspects. First, the planned path must run from a given starting point to a given endpoint. Secondly, it should ensure robot’s collision-free movement. Thirdly, among all the possible paths that meet the first two requirements it must be, in a certain sense, optimal.Methods of path planning can be classified according to different characteristics. In the context of using intelligent technologies, they can be divided into traditional methods and heuristic ones. By the nature of the environment, it is possible to divide planning methods into planning methods in a static environment and in a dynamic one (it should be noted, however, that a static environment is rare). Methods can also be divided according to the completeness of information about the environment, namely methods with complete information (in this case the issue is a global path planning) and methods with incomplete information (usually, this refers to the situational awareness in the immediate vicinity of the robot, in this case it is a local path planning). Note that incomplete information about the environment can be a consequence of the changing environment, i.e. in a dynamic environment, there is, usually, a local path planning.Literature offers a great deal of methods for path planning where various heuristic techniques are used, which, as a rule, result from the denotative meaning of the problem being solved. This review discusses the main approaches to the problem solution. Here we can distinguish five classes of basic methods: graph-based methods, methods based on cell decomposition, use of potential fields, optimization methods, фтв methods based on intelligent technologies.Many methods of path planning, as a result, give a chain of reference points (waypoints) connecting the beginning and end of the path. This should be seen as an intermediate result. The problem to route the reference points along the constructed chain arises. It is called the task of smoothing the path, and the review addresses this problem as well.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

Mobile Anchor-Assisted Localization Using Invasive Weed Optimization Algorithm

2020 ◽  
pp. 415-436
Author(s):  
Vaishali Raghavendra Kulkarni ◽  
Veena Desai ◽  
Akash Sikarwar ◽  
Raghavendra V. Kulkarni
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Sensor Nodes ◽  
Circular Path ◽  
Cultural Algorithm ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Mobile Anchor ◽  
Planning Methods ◽  
Planning Algorithm

Sensor localization in wireless sensor networks has been addressed using mobile anchor (MA) and a metaheuristic algorithm. The path of a MA plays an important role in localizing maximum number of sensor nodes. The random and circle path planning methods have been presented. Each method has been evaluated for number of localized nodes, accuracy, and computing time in localization. The localization has been performed using trilateration method and two metaheuristic stochastic algorithms, namely invasive weed optimization (IWO) and cultural algorithm (CA). Experimental results indicate that the IWO-based localization outperforms the trilateration method and the CA-based localization in terms of accuracy but with higher computing time. However, the computing speed of trilateration localization is faster than the IWO- and CA-based localization. In the path-planning algorithms, the results show that the circular path planning algorithm localizes more nodes than the random path.

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