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

Samaniego, Franklin; Sanchis, Javier; García-Nieto, Sergio; Simarro, Raúl

doi:10.3390/electronics8030306

Cited by 27 publications

(27 citation statements)

References 57 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, new cost-efficient flying paths are emerging, such as spiral coverage path. [121] In the case of computation time, a new approach was proposed [122], by combining different methodologies for mapping of the 3D environment. In pursuit of the optimal flight plan, Fu et al [116] proposed three cost functions, of which path security cost is used to determine the feasibility of the survey and the length cost and smoothness cost of the path are referred to as the energy consumption of UAS flight mission.…”

Section: Flight Missionmentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

View full text Add to dashboard Cite

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

show abstract

Section: Flight Missionmentioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In this section we analyze five scenarios in 3D space, similar to the methodology proposed in [44]. Recursive rewarding modified adaptive cell decomposition (RR-MACD) splits the 3D environment like a discrete mesh of collision-free voxels.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The RR-MACD methodology gives two sets of results based on the defined constraints. The results presented in [44] are shown in summary form in Table 1, where the first column shows the scenario number. The second column shows RR-MACD with four constraints and the RR-MACD with 10 constraints in the third column shows the conditions to solve the path planning problem.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…3D path planning results. The number of free-collision voxels within a defined environment is indicated as S f ree , while the number of discrete 3D nodes built by [44] is denoted by ρ x (F) (3D final path).…”

Section: Experiments and Resultsmentioning

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%

See 2 more Smart Citations

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

et al. 2019

Self Cite

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

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Mannan

Obaidat

Mahmood

et al. 2022

Int J Communication

View full text Add to dashboard Cite

The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.

show abstract

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

Cited by 27 publications

References 57 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

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

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Contact Info

Product

Resources

About