Trajectory Planning for UAVs in 3D Environments Using a Moving Band in Potential Sigmoid Fields

Rivera, Diego; Prieto, Flavio; Ramírez, Ricardo

doi:10.1109/sbr-lars.2012.26

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…This topic has been studied in [18], [19] and [20]. The reactive behavior is designed to find a safe way, even if new obstacles have appeared in the environment as the result of an updated Octomap.…”

Section: B Navigation Systemmentioning

confidence: 99%

A flexible hardware-in-the-loop architecture for UAVs

Lepej

Santamaria-Navarro²,

Solà³

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-As robotic technology matures, fully autonomous robots become a realistic possibility, but demand very complex solutions to be rapidly engineered. In order to be able to quickly set up a working autonomous system, and to reduce the gap between simulated and real experiments, we propose a modular, upgradeable and flexible hardware-in-theloop (HIL) architecture, which hybridizes the simulated and real settings. We take as use case the autonomous exploration of dense forests with UAVs, with the aim of creating useful maps for forest inspection, cataloging, or to compute other metrics such as total wood volume. As the first step in the development of the full system, in this paper we implement a fraction of this architecture, comprising assisted localization, and automatic methods for mapping, planning and motion execution. Specifically we are able to simulate the use of a 3D LIDAR endowed below an actual UAV autonomously navigating among simulated obstacles, thus the platform safety is not compromised. The full system is modular and takes profit of pieces either publicly available or easily programmed. We highlight the flexibility of the proposed HIL architecture to rapidly configure different experimental setups with a UAV in challenging terrain. Moreover, it can be extended to other robotic fields without further design. The HIL system uses the multi-platform ROS capabilities and only needs a motion capture system as external extra hardware, which is becoming standard equipment in all research labs dealing with mobile robots.

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Section: B Navigation Systemmentioning

confidence: 99%

A flexible hardware-in-the-loop architecture for UAVs

Lepej

Santamaria-Navarro²,

Solà³

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…Some representative techniques used in path planning methods and based on continuous and discrete environment sampling include: RRT (rapidly-exploring random tree) [15][16][17][18]; PRM (probabilistic road maps) [19][20][21][22][23]; Voronoi diagrams [24][25][26]; and artificial potential [27][28][29][30]. Nevertheless, it is important to note that RRT and PRM make random explorations (continuous sampling) of the defined environment.…”

Section: Introductionmentioning

confidence: 99%

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

et al. 2019

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A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.

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“…The set of control points that define the collision-free space is calculated using specific path planning methods based on continuous and discrete environment sampling. Some examples of these techniques are: the rapidly-exploring random tree (RRT) [20][21][22][23]; probabilistic road maps (PRM) [24][25][26][27][28]; heuristic planners (genetic algorithms-GA) [29,30]; swarm intelligence [31][32][33][34]; fuzzy logic [35,36]); Voronoi diagrams [37][38][39]; artificial potential [40][41][42][43]; and recursive rewarding modified adaptive cell decomposition (RR-MACD) [44].…”

Section: Introductionmentioning

confidence: 99%

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

et al. 2019

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Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.

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Trajectory Planning for UAVs in 3D Environments Using a Moving Band in Potential Sigmoid Fields

Cited by 6 publications

References 13 publications

A flexible hardware-in-the-loop architecture for UAVs

A flexible hardware-in-the-loop architecture for UAVs

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

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