Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method

Yan, Fei; Liu, Yi-Sha; Xiao, Jizhong

doi:10.1007/s11633-013-0750-9

Cited by 138 publications

(83 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The advantage of the A * and D * algorithms lie in the ability to judge or evaluate the best point-to-point path so they can provide a flyable trajectory for UAVs [9]. The RRT and PRM are based on probabilistic reasoning, and hence, robust but they require the discretization of the operating space which affects the smoothness of the planned trajectory [10], [11]. In [12], the approach based on the Voronoi diagram has been developed with a good obstacle avoidance capability.…”

Section: Introductionmentioning

confidence: 99%

Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Hoang

Phung

Dinh

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (UAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of UAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angleencoded Particle Swarm Optimization (θ-PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of-Things (IoT) for multi-directional transmission and reception of data between the UAVs, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.

show abstract

“…To improve the quality of the solution, many variants of these sampling based algorithms have been proposed [12][13][14][15][16]. For aerial vehicles path planning problem, some adapted versions [17][18][19][20] are applied. Few methods based on Voronoi diagram have been used to deal with the problem of 3D path planning [21].…”

Section: Introductionmentioning

confidence: 99%

Filtered Medial Surface Based Approach for 3D Collision-Free Path Planning Problem

Benzaid

Marie

Mansouri

et al. 2018

Journal of Robotics

View full text Add to dashboard Cite

This paper introduces an original 3D path planning approach for Unmanned Aerial Vehicle (UAV) applications. More specifically, the core idea is to generate a smooth and collision-free path with respect to the vehicle dimension. Given a 3D grid representation of the environment, the Generalized Voronoi Graph (GVG) is first approximated using a filtered medial surface (FMS) algorithm on the corresponding navigable space. Based on an efficient pruning criterion, the produced FMS excludes GVG portions corresponding to narrow passages unfitting safe UAV navigation constraints, and thus it defines a set of guaranteed safe trajectories within the environment. Given a set of starting and destination coordinates, an adapted A-star algorithm is then applied to compute the shortest path on the FMS. Finally, an optimization process ensures the smoothness of the final path by fitting a set of 3D Bézier curves to the initial path. For a comparative study, the A-star algorithm is applied directly on the input environment representation and relevant comparative criteria are defined to assert the proposed approach using simulation results.

show abstract

Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method

Cited by 138 publications

References 23 publications

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

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

Angle-Encoded Swarm Optimization for UAV Formation Path Planning

Filtered Medial Surface Based Approach for 3D Collision-Free Path Planning Problem

Contact Info

Product

Resources

About