Path planning based on firefly algorithm and Bezier curve

Li, Baolei; Liu, Lanjuan; Zhang, Qinhu; Lv, Danjv; Zhang, Yufeng; Zhang, Junhua; Shi, Xinling

doi:10.1109/icinfa.2014.6932730

Cited by 7 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the hybrid algorithm provides a smooth path with the shortest distance compared to those of the genetic algorithm (GA) [17], the particle swarm with inertia weight (PSO-w) algorithm [17], and the firefly algorithm (FA) [17] in two different maps. In the first map, when comparing the hybrid algorithm with the GA algorithm, the hybrid algorithm provided an enhancement on the path length equals to 1.5% compared to the GA algorithm, while the proposed hybrid algorithm enhancement on the path length equals 0.5% compared to the PSO-w algorithm and 2.3% compared to the FA algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…The Cartesian distance between any two fireflies at ( 𝑥 𝑖 , 𝑦 𝑖 ) and ( 𝑥 𝑗 , 𝑦 𝑗 ), respectively, is expressed as shown in Eq. ( 17) [17,18]:…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

“…13 Table 6 The result of the comparison between the algorithms. Finally, the hybrid FAMCPSO was compared with the GA, the PSO-W, and the FA that was developed in [17], using an environment with a workspace of [16×16] m, where the starting and ending positions are at (5,5) and (15,15), respectively.…”

Section: Algorithm Types Best Path Lengthmentioning

confidence: 99%

“…The authors demonstrated how to find the shortest feasible (collision-free) path using an approach based on the firefly algorithm (FA) [17,18]. They also compared their approach to two well-known swarm optimization algorithms: the genetic algorithm (GA) and the particle swarm algorithm with inertia weight (PSO-w).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

IJIES

View full text Add to dashboard Cite

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The Cartesian distance between any two fireflies at ( 𝑥 𝑖 , 𝑦 𝑖 ) and ( 𝑥 𝑗 , 𝑦 𝑗 ), respectively, is expressed as shown in Eq. ( 17) [17,18]:…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

Section: Algorithm Types Best Path Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…In addition to the above, the proposed method incorporates the concepts of personal best and global best into FF. Except for the firefly movement, which has been updated to integrate the notions of personal and global best [33]- [35], all the methods in FF have remained the same. Consequently, the altered position vector of the FF method may be written as follows in Eq.…”

Section: B Hybrid Firefly Algorithm With Modify Chaotic Particle Swar...mentioning

confidence: 99%

Dynamic Obstacle Avoidance Algorithm for Autonomous Mobile Robots

2023

IJCCCE

View full text Add to dashboard Cite

A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO). The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b. Index Terms— Dynamic environment, Collision avoidance, obstacle avoidance, Path planning, PSO, FA, GA.

show abstract