Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan, Mohanan M.; Salgaonkar, Ambuja

doi:10.1002/eng2.12132

Cited by 17 publications

(15 citation statements)

References 33 publications

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“…Palmeiri et al [254] compared the performance of FA, PSO, and BCO in the coordination of the swarm robotics system in terms of energy consumption. FA also has better performance to globally cover all the nodes than the ACO algorithm, reducing the computation time by 7.2% and decreasing the coverage path length by 2.5% in the case of grid size 10 x 10 of dynamic sloped terrain [255]. Nevertheless, there is no significant improvement in the path length if it increases the robot density.…”

Section: ) Swarm Intelligencementioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: ) Swarm Intelligencementioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…For evaluating the models, characteristics are ranked between 1 and 5, based on 1-poor, 2-moderate, 3-good, 4-very good and 5-excellent. Ranks are given based on the literature [2,3,60,[63][64][65]…”

Section: Probabilistic Collision Checkermentioning

confidence: 99%

Probabilistic Approach to Robot Motion Planning in Dynamic Environments

Mohanan

Salgaonkar

2020

SN COMPUT. SCI.

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Four major approaches to robot motion planning in dynamic environments are discussed: probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon control (PCLRHC) and gross hidden Markov model (GHMM). A comparison of three mapping techniques, Kalman filter, expectation and maximization algorithm and Markov model, is presented. The PCS method is the probabilistic extension of inevitable collision state, which is found to be the safest motion planning method. The concept of open-loop and partially closed-loop receding horizon control (OLRHC and PCLRHC) is compared critically, and the algorithms are benchmarked. GHMM is the best suited method for environments with limited space and dynamic environment due to human interactions. GHMM parameters and structure are evaluated using an incremental "learn-and-predict" approach. For exploring GHMM, we simulated a cafeteria with eight tables to be served by a robot, considering three different arrangements of tables along with convex and concave obstacles, and obtained the path length and time taken for a Hamiltonian path. During the simulation, it was observed that for a given static or dynamic environment, the concavity of the obstacles is what makes the scenario a complex one.

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“…The performance of ACO and Firefly algorithm in different dynamic environments of a rubber plantation was compared by (Gangadharan et al, 2020). Simulations shows that FF outperformed ACO in terms of path length and time of execution.…”

Section: B Ant Colony Optimization (Aco)mentioning

confidence: 99%

Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

Yinka-Banjo

Agwogie²

2020

Nig. J. Technol. Dev.

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This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

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Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Cited by 17 publications

References 33 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Probabilistic Approach to Robot Motion Planning in Dynamic Environments

Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

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