3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms

Aguilar, Wilbert G.; Morales, Stephanie

doi:10.3390/electronics5040070

Cited by 69 publications

(23 citation statements)

References 25 publications

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“…Figure 2d shows an example of an L segment defined by the [t 1 , t 2 ] points, where the direction of the line is given by the flight path of the UAV. Therefore, − → u ( Figure 5) is a unit vector that points in the direction of the desired orientation, and with d defined as the distance between t 1 and t 2 according to Equation (21). Therefore, the L segments will be described, in general, as:…”

Section: Definition Of Straight-line Segmentmentioning

confidence: 99%

“…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%

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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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Section: Definition Of Straight-line Segmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Georgios Mastorakis et al [28] fetch data from the depth image and detect falls by measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. Aguilar et al [29] presented a 3D path planning system that uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Alazrai R. [30] built a view-invariant descriptor for human activity representation by 3D skeleton joint positions to detect falls and other activities.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Fall Detection Based on the Human Torso Motion Model

Yao

Min

2017

Applied Sciences

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In our proposed method, a new feature named torso angle is firstly proposed and imported in the human torso motion model (HTMM) for fall detection. By tracking the changing rate of torso angle and centroid height, our fall detection model has a strong capability of differentiating falls from other fall-like activities, such as controlled lying down, quickly crouching and bending, which may be big obstacles for other existing computer vision-based approaches to discriminate.

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“…Roadmap path planning methods are gaining popular-Information Technology and Control 2019/2/48 180 ity in addressing mobile robot path planning problems [20]. Notable among these methods include probabilistic roadmap (PRM) [33], voronoi diagram (VD) [4,5] and rapidly exploring random tree (RRT) path planning methods [1,7,9,13,14,24,25,34,37,39]. Consideration is given to RRT path planning in this paper.…”

Section: Introductionmentioning

confidence: 99%

Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

Ayawli

Mei

Shen

et al. 2019

ITC

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This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments

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3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms

Cited by 69 publications

References 25 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

A New Approach to Fall Detection Based on the Human Torso Motion Model

Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

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