A Dijkstra Algorithm for Fixed-Wing UAV Motion Planning Based on Terrain Elevation

Medeiros, Felipe Leonardo Lôbo; Silva, José Demisio Simões da

doi:10.1007/978-3-642-16138-4_22

Cited by 24 publications

(14 citation statements)

References 7 publications

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“…A visibility graph has the property of allowing the planning of the shortest path between any two nodes, if a method for planning shortest paths in graphs is applied, such as the Dijkstra algorithm. 33 Figure 3 illustrates an example of a shortest path planned with a visibility graph.…”

Section: Convex Vertices and Path Planningmentioning

confidence: 99%

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

Véras

Medeiros²,

Guimarães³

2019

International Journal of Advanced Robotic Systems

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The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.

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Section: Convex Vertices and Path Planningmentioning

confidence: 99%

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

Véras

Medeiros²,

Guimarães³

2019

International Journal of Advanced Robotic Systems

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“…Graph‐based search algorithms give the advantages of having fast searching abilities and possible online implementation, but they generate nonsmooth trajectories and they are not suitable for large areas. These methods have found wide applications in the UAV domain: Dijkstra's algorithm has been recently implemented in a fixed‐wing UAV and as a first step of a planning algorithm for UAV with kinematic constraints in the presence of polygonal obstacles; the A* approach, with a post‐smoothing process (A*PS) that finds significantly shorter paths than A*, and the LazyTheta* method, a 3D implementation of the Theta* algorithm using the line of sight function and applying smooth search, have been tested and compared with UAVs; D*‐Lite, a more efficient variant of A* that replans only a local section of the path, together with a Probabilistic Roadmap planner for building the environment's configuration and a smoothing process, has produced encouraging results with a simulated UAV …”

Section: Autonomous Navigationmentioning

confidence: 99%

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

Recchiuto

Sgorbissa

2017

Journal of Field Robotics

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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

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“…The adoption of the junction-focused logic and source routing schemes resulted in an average packet delivery ratio (PDR) of 83% and an average delay of 0.425 s. This technique chooses the paths according to Dijkstra's algorithm. Dijkstra's algorithm is used to perform a bidirectional search on time-dependent road networks [10] and plan the motion of unmanned aerial vehicles (UAVs) based on terrain elevation [11]. Dijkstra's algorithm was also recently used for solving mathematical problems like L-concave function maximization [12].…”

Section: Related Workmentioning

confidence: 99%

AOLSR: hybrid ad hoc routing protocol based on a modified Dijkstra's algorithm

Natarajan

Rajendran

2014

J Wireless Com Network

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Mobile ad hoc networks (MANETs) are highly vulnerable to both link and node failures due to nodal mobility. The routing resilience against link and/or node failures needs to be enhanced to avoid the degradation of network performance. This can be achieved by multipath routing which uses multiple alternative paths to route the messages via multiple disjoint paths and result in increased bandwidth, fault-tolerance, and security. An optimized link state routing (OLSR) protocol is a proactive routing protocol. An advanced OLSR (AOLSR) protocol is proposed based on a modified Dijkstra's algorithm which enables routing in multiple paths of dense and sparse ne0074work topologies. The routing is based on the energy of nodes and links (implied from the lifetime) and the mobility of the nodes. It is a hybrid ad hoc routing protocol because it combines the proactive and reactive features. It is another form of source routing protocol which allows a sender of a data packet to partially or completely reveal the route the packets take through the network. Two cost functions are introduced to build link-disjoint or node-disjoint paths. Secondary functions, namely path recovery and loop discovery process are involved to manage the topology changes of the network. AOLSR protocol is analyzed and compared with the existing MANET routing protocols namely, dynamic source routing (DSR) and OLSR. Its performance is observed to be satisfactory in terms of average end-to-end delay, packet delivery ratio (PDR), average time in first-in-first-out (FIFO) queue, and throughput.

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A Dijkstra Algorithm for Fixed-Wing UAV Motion Planning Based on Terrain Elevation

Cited by 24 publications

References 7 publications

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

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

AOLSR: hybrid ad hoc routing protocol based on a modified Dijkstra's algorithm

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