Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment

Elmi, Zahra; Efe, Mehmet Önder

doi:10.1080/0952813x.2020.1764631

Cited by 16 publications

(12 citation statements)

References 23 publications

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“…The predicted potential field, as well as quadratic terms of tracking, inputs, changes in inputs, and slack variables, are all included in the objective function, with weighting matrices Q , R, S, and P, respectively. The predicted states are obtained by (8). The tracking output is calculated by (9).…”

Section: Mpc Frameworkmentioning

confidence: 99%

“…Heuristic approaches are presented to overcome the limitations of conventional methods. Probabilistic Roadmap [4], Simulated Annealing [5], Ant Colony Optimization [6], Particle Swarm Optimization [7], and Grasshopper optimization [8] are a few examples of heuristic methods. However, these algorithms have problems in static and dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Vehicle Path Planning Using MPC and APF

Elmi¹,

Elmi²

2022

Motion Planning

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Autonomous vehicles have been at the forefront of academic and industrial research in recent decades. This study’s aim is to reduce traffic congestion, improve safety, and accidents. Path planning algorithms are one of the main elements in autonomous vehicles that make critical decisions. Motion planning methods are required when transporting passengers from one point to another. These methods have incorporated several methods such as generating the best trajectory while considering the constraints of vehicle dynamics and obstacles, searching a path to follow, and avoiding obstacles that guarantee comfort, safety, and efficiency. We suggested an effective path planning algorithm based on Model Predictive Controller that determines the maneuvers mode such as lane-keeping and lane-changing automatically. We utilized two different artificial potential field functions for the road boundary, obstacles, and lane center to ensure safety. On the four scenarios, we examined the proposed path planning controller. The obtained results show that when a path planning controller is used, the vehicle avoids colliding with obstacles and follows the rules of the road by adjusting the vehicle’s dynamics. An autonomous vehicle’s safety is ensured by the path planning controller.

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Section: Mpc Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Path Planning Using MPC and APF

Elmi¹,

Elmi²

2022

Motion Planning

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“…Compared with the PSO, grasshopper optimization displays distinct advantages with time and smoothness. On the other hand, the same authors [3] presented safe and smooth pathfinding for a mobile robot that can traverse from a starting point to a destination point without colliding obstacles. Four circles (different radii on the range sensor) estimate obstacle direction.…”

Section: Figure 1 Optimization Algorithms Typesmentioning

confidence: 99%

“…Some of these problems are increasingly complex, which becomes difficult to solve and optimize using conventional mathematical methods. Path planning is one of the NP-Hard problems, which requires a high computational cost to be solved using classical methods [1]- [3]. Metaheuristic methods have been proposed to overcome the complexity of classical methods.…”

Section: Introductionmentioning

confidence: 99%

Grasshopper optimization algorithm based path planning for autonomous mobile robot

Shareef

Al-Darraji

2022

Bulletin EEI

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Autonomous mobile robots have become very popular and essential in our life, especially in industry. One of the crucial activities of the robot is planning the path from a start point to a target point, avoiding obstacles in the environment. Recently, path planning received more attention, and many methodologies have been proposed. Path planning studies have shown the effectiveness of swarm intelligence in complex and known or unknown environments. This paper presents a global path planning method based on grasshopper optimization algorithm (GOA) in a known static environment. This algorithm is improved using the bias factor to increase the efficiency and improve the resulting path. The resulting path from this algorithm is further enhanced using an improved version multinomial logistic regression algorithm (MLR). The algorithms were evaluated using three different large environments of varying complexities. The GOA algorithm has been compared with the ant colony optimization algorithm (ACO) using the same environments. The experiments have shown the superiority of our algorithm in terms of time convergence and cost.

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“…Emli and Efe 47 have presented online route outlining of robot using grasshopper algorithm. A collision-free path is obtained through implementation of the algorithm in unknown workspaces.…”

Section: Introductionmentioning

confidence: 99%

Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

Kumar

Parhi

Muni

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length.

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Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment

Cited by 16 publications

References 23 publications

Autonomous Vehicle Path Planning Using MPC and APF

Autonomous Vehicle Path Planning Using MPC and APF

Grasshopper optimization algorithm based path planning for autonomous mobile robot

Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

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