Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning

Gul, Faiza; Mir, Imran; Alarabiat, Deemah; Alabool, Hamzeh; Abualigah, Laith; Mir, Suleman

doi:10.1016/j.jpdc.2022.06.014

Cited by 32 publications

(13 citation statements)

References 60 publications

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“…In the same a hybrid swarm optimization algorithm is used to avoid collision and find the shortest path. A hybrid solution to path planning problem is also proposed in [34] which combines the new bio-inspired grey wolf algorithm and particle swarm optimization. Another bio-inspired path planning algorithm is proposed in [35], inspired by plant growth and uses the plant growth route planning algorithm to solve the path planning problem in 3D dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Robot Motion Control Using Stepping Ahead Firefly Algorithm and Kinematic Equations

Chaudhary,

Prasad,

Prasad

et al. 2024

IEEE Access

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The use of robots to carry out activities in various fields has evolved over the years. Robots can move from one location to another according to a plan and have a good understanding of the environment to perform their assigned tasks. Robot movements are based on motion planning and control algorithms. This paper proposes a new hybrid motion planning and control algorithm called FAStep-Kinematic ( Stepping Ahead Firefly algorithm and Kinematic equations). This algorithm includes the Firefly Stepping Ahead algorithm to avoid obstacles, the equation of motion to control the movement of the robot, and a method to detect obstacles. The algorithm starts with the equation of motion and switches to the stepping ahead firefly algorithm when an obstacle is detected. The stepping ahead firefly algorithm then plans the robot's next step to avoid obstacles. The robot moves to this step. This procedure is repeated until the robot successfully avoids an obstacle and reaches its target. The obstacles used in this article are static and are known to robots. The hybrid algorithm will be used to navigate robots in a cluttered environment. The new algorithm's effectiveness will be seen with an application to tractor-trailer robotic systems. The results show that the robots are able to reach their destination safely and using a shorter route. Additionally, this research compared the new hybrid algorithm's performance with that of the ACO-Kinematic (Ant Colony Optimization and Kinematic equations) algorithm in terms of path length and convergence time. The analysis show that the hybrid algorithm is superior to the ACO-Kinematic algorithm. The proposed algorithm improves the path length by 0.37%, 9.22%, and 5.79% compared to the ACO-Kinematic algorithm in three different scenarios.

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Section: Related Workmentioning

confidence: 99%

Robot Motion Control Using Stepping Ahead Firefly Algorithm and Kinematic Equations

Chaudhary,

Prasad,

Prasad

et al. 2024

IEEE Access

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“…This network makes use of input, hidden, and output layers as well as nodes with feedforward connections. A novel method of route tracking for moving robots that mimic vehicles was presented [94]. Based on neural predictive control, this plan was implemented.…”

Section: Artificial Neural Network (Nn) Techniquementioning

confidence: 99%

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Borkar,

Aljrees,

Pandey

et al. 2023

Processes

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Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

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“…The cells are regarded as viable contenders for the posts. Equations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) are used in the suggested technique to update the X(iter+1) position. Using Equation 15, Utilize X1, X2, X3 and X4,…”

Section: A Integrated Stochastic Optimizermentioning

confidence: 99%

“…Teams of autonomous mobile robots can perform information-gathering activities including exploration, surveillance, and inspection with increased efficiency, dependability, and robustness, among other benefits [1]. These benefits are attained by utilising some kind of team coordination, which is frequently built assuming the ability to interact without boundaries [2][3][4][5]. However, dealing with communication-challenged circumstances is a common requirement for operations in the real world.…”

Section: Introductionmentioning

confidence: 99%

“…Optimization-based search is one of the most common artificial intelligence methods used to introduce functions such as human learning [2,3,5,19,20], UAV path planning [15,[21][22][23][24][25][26][27][28][29][30][31][32][33]. Because optimization methods produce good outcomes, they play a key role in tackling complex problems [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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A Centralized Strategy for Multi-Agent Exploration

Gul

Mir

et al. 2022

IEEE Access

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This paper introduces recently developed Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework "Coordinated Multi-Robot Exploration Aquila Optimizer" (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded to improve multirobot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stuck locally) or takes longer exploration time.

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Implementation of bio-inspired hybrid algorithm with mutation operator for robotic path planning

Cited by 32 publications

References 60 publications

Robot Motion Control Using Stepping Ahead Firefly Algorithm and Kinematic Equations

Robot Motion Control Using Stepping Ahead Firefly Algorithm and Kinematic Equations

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

A Centralized Strategy for Multi-Agent Exploration

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