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Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms

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Abstract

Three-dimensional path planning for autonomous robots is a prevalent problem in mobile robotics. This paper presents three novel versions of a hybrid method designed to assist in planning such paths for these robots. In this paper, an improvement on Rapidly exploring Random Tree (RRT) algorithm, namely Adapted-RRT, is presented that uses three well-known metaheuristic algorithms, namely Grey Wolf Optimization (GWO), Incremental Grey Wolf Optimization (I-GWO), and Expanded Grey Wolf Optimization (Ex-GWO)). RRT variants, using these algorithms, are named Adapted-RRTGWO, Adapted-RRTI-GWO, and Adapted-RRTEx-GWO. The most significant shortcoming of the methods in the original sampling-based algorithm is their inability in finding the optimal paths. On the other hand, the metaheuristic-based algorithms are disadvantaged as they demand a predetermined knowledge of intermediate stations. This study is novel in that it uses the advantages of sampling and metaheuristic methods while eliminating their shortcomings. In these methods, two important operations (length and direction of each movement) are defined that play an important role in selecting the next stations and generating an optimal path. They try to find solutions close to the optima without collision, while providing comparatively efficient execution time and space complexities. The proposed methods have been simulated employing four different maps for three unmanned aerial vehicles, with diverse sets of starting and ending points. The results have been compared among a total of 11 algorithms. The comparison of results shows that the proposed path planning methods generally outperform various algorithms, namely BPIB-RRT*, tGSRT, GWO, I-GWO, Ex-GWO, PSO, Improved BA, and WOA. The simulation results are analysed in terms of optimal path costs, execution time, and convergence rate.

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Acknowledgements

The open-source code of the proposed path planning method is presented at the https://github.com/aliyevroyal/3DPathPlanningForUAVs.

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Authors and Affiliations

  1. Software Engineering Department, Faculty of Engineering, Istinye University, Istanbul, Turkey

    Farzad Kiani

  2. Computer Engineering Department, Faculty of Engineering and Architecture, Beykent University, Istanbul, Turkey

    Amir Seyyedabbasi

  3. Computer Engineering Department, Faculty of Engineering, Istanbul Aydin University, Istanbul, Turkey

    Royal Aliyev, Murat Ugur Gulle & Hasan Basyildiz

  4. Computer Engineering Department, Faculty of Engineering, Istanbul Ayvansaray University, Istanbul, Turkey

    M. Ahmed Shah

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Appendix: Terminologies

Appendix: Terminologies

Term

Explanation

Path Planning

It is a computational problem to find a sequence of valid configurations that moves the mobile robot from the source to the destination

Optimal Path Planning

It is measured based on various factors such as path length, collision-free space, execution time, and the total number of turns

Sampling-based Path Planning

These methods need some pre-known information of the whole workspace, that is, a mathematical representation to describe the workspace

Mathematical-based Path Planning

These algorithms model the environment (kinematic constraints) as well as the parameters of the system (dynamic). Afterward, they map the bounds of these two parameters based on the cost function bound

Nature-based Path Planning

These methods attempt to find an almost optimal path by eliminating the process of creating complex environment models based on stochastic approaches

Node-based Path Planning

These algorithms are informed search methods and find an optimal path based on certain decomposition

Unknown Environments

The mobile robot does not have any information about the environment. In this case, the movements are not deterministic and it is according to local information. The results for all actions are unknown to the agent

Known Environments

The mobile robot has enough information about the environment. the results for all actions are known to the agent

Fully or Partially Known Environments

The whole or part of the environment refers to certain situations

Unstructured Environments

The mobile robot cannot rely on complete knowledge about its environment. The environment fills with uncertain elements

Trajectory

The path between two points that followed by an object

Metaheuristics Algorithms

These algorithms try to efficiently explore the search space in order to find near-optimal solutions by exploiting without falling to local trap optima

Local Optima Trap

Situation mistakenly considered the best solution. However, there is a better solution in the global area than the found solution

Autonomous Mobile Robots

They gain required information about their environment. They can operate autonomously without human intervention

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Kiani, F., Seyyedabbasi, A., Aliyev, R. et al. Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms. Neural Comput & Applic 33, 15569–15599 (2021). https://doi.org/10.1007/s00521-021-06179-0

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