MVO-based path planning scheme with coordination of UAVs in 3-D environment

Jain, Gatij; Yadav, Gaurav; Prakash, D.B.; Shukla, Anupam; Tiwari, Ritu

doi:10.1016/j.jocs.2019.07.003

Cited by 43 publications

(30 citation statements)

References 22 publications

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“…Authors propose an efficient technique to find the flight path for UAV. 13 It starts with research on how to effectively avoid dynamic obstacles. Based on the original solution, it finds out the problems of accuracy and efficiency.…”

Section: Uav Path Planning With Obstacle Avoidancementioning

confidence: 99%

See 1 more Smart Citation

Aspects of unmanned aerial vehicles path planning: Overview and applications

Khan

Saad

et al. 2021

Int J Communication

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Aiming to the applications from security surveillance, military operational capabilities to the content and package delivery, unmanned aerial vehicles (UAVs) has a successfully created his space in the available technologies. The compact sized powerful flying robots are wirelessly controlled and are capable to complete tasks with and without direct human intervention. UAVs however still face serious challenges that limit the dream of complete autonomous unmanned flying machines. The key challenges include path planning and obstacle avoidance of these unmanned flying robots that are unavoidable while performing the application-specific functionalities both in indoor and outdoor environments. In this manuscript with a survey, we investigate the state-of-the-art UAV path planning algorithms and obstacle avoidance techniques. We have also summarized and compared the schemes in tabular form. In addition, current and future research directions and challenges are also discussed, showing the prospective research directions.

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Section: Uav Path Planning With Obstacle Avoidancementioning

confidence: 99%

“…Long flight time can cover large distances Constraints of major environmental factors not considered during the aerial operations Jain et al 13 Dynamic obstacles avoidance Complex calculation process. Constraints of major environmental factors not considered during the aerial operations.…”

Section: Future Applications Of Uavmentioning

confidence: 99%

Aspects of unmanned aerial vehicles path planning: Overview and applications

Khan

Saad

et al. 2021

Int J Communication

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“…turns, and the smoothing of the UAV's path is handled using Polynomial of degree five (Dewangan et al, 2019;Jain et al, 2019;Saxena et al, 2019).…”

Section: Describe the Coding Of Multi-uav Flight Pathmentioning

confidence: 99%

“…These algorithms have achieved good results in wind prediction, parameter identification and large-scale production scheduling (Chen & Pi, 2019;Chen et al, 2019b;Shao et al, 2020Shao et al, , 2019. At present, researchers apply grey wolf optimizer (GWO) (Dewangan et al, 2019), MVO (Jain et al, 2019) and SSA (Saxena et al, 2019) to optimize the path of multi-UAVs, where SSA is the most efficient. Wolpert et al pointed out that there is no one optimization algorithm suitable for solving all optimization problems, meaning that any one algorithm is somewhat different in solving a particular optimization problem (Wolpert & Macready, 1997).…”

Section: Introductionmentioning

confidence: 99%

Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

Yang

Bi³

et al. 2021

Preprint

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Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

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“…N OWADAYS, with the vast developments in technology, autonomous and mobile robots [1] are employed in many application fields such as autonomous driving [2], [3], navigation in complex environments [4]- [6], vehicle routing [7], buildings [8], industrial automation [9] and unmanned air vehicles [10], [11]. Depending on the application and the task to be solved, robots have to navigate in different types of environments without colliding with obstacles.…”

Section: Introductionmentioning

confidence: 99%

Voronoi Boundary Visibility for Efficient Path Planning

Al-Dahhan

Schmidt

2020

IEEE Access

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The subject of this paper is the computation of paths for mobile robots that navigate from a start position to a goal position in environments with static obstacles. Specifically, we focus on paths that are represented by straight lines. Such paths can for example directly be followed by omni-directional robots or can be used as an initial solution for path smoothing. In this context, the most common performance metrics are the path length, the obstacle clearance and the computation time. In this paper, we develop a new path planning algorithm that addresses all the stated performance metrics. Our method first determines all possible connections between the start position and goal position along the edges of the generalized Voronoi diagram (GVD) of a given obstacle map. The shortest connections are then refined using a balanced method for creating shortcuts along existing waypoints and introducing new waypoints in order to cut corners. As an important feature, our method reduces the number of required waypoints by iteratively adding new waypoints and then removing unnecessary waypoints along solution paths. Moreover, our method takes into account multiple start-goal connections, since the shortest start-goal connection along the edges of the GVD might not lead to the shortest solution path. A comprehensive computational evaluation for a large number of maps with different properties shows that the proposed method outperforms sampling-based algorithms such as Probabilistic Roadmaps (PRM) and exact methods such as Visibility Graphs (VG) by computing close-to-optimal solution paths with a specified minimum obstacle clearance in less time. INDEX TERMS Mobile robots, path planning, obstacles, Voronoi diagram, safety.

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MVO-based path planning scheme with coordination of UAVs in 3-D environment

Cited by 43 publications

References 22 publications

Aspects of unmanned aerial vehicles path planning: Overview and applications

Aspects of unmanned aerial vehicles path planning: Overview and applications

Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

Voronoi Boundary Visibility for Efficient Path Planning

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