Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot

Adamu, Muminu O.; Oguntunde, Pelumi E.

doi:10.1007/s11277-019-06180-w

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…to tackle the problems of mobile robot path planning [4] including A* algorithms [5], D* algorithms [6], fuzzy logic (FL) [7], genetic algorithms (GAs) [8], particle swarm optimization (PSO) [9], ant colony algorithms (ACOs) [10], artificial potential fields [11], probabilistic road map (PRM), bug algorithms, and others [4]. Each of these algorithms has advantages and limitations in different environments.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

IJIES

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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.

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Section: Introductionmentioning

confidence: 99%

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

IJIES

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“…Trajectory planning is one of the most important techniques of unmanned aerial vehicle research and it finds a feasible optimal trajectory between the initial control point and target control point [2]. The trajectory planning usually can be divided into the global trajectory planning and local…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning for UAV Based on Improved ACO Algorithm

et al. 2020

IEEE Access

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Trajectory planning is an important subject in the field of unmanned aerial vehicles (UAVs). However, existing methods do not solve some problems well, such as slow convergence speed and low searching efficiency of related algorithms and collisions between UAVs and the obstacles. Therefore, a method is proposed to solve a trajectory planning problem for multi-UAV in a static environment, which includes three main phases: the initial trajectory generation, the trajectory correction, and the smooth trajectory planning. First, the improved algorithm called MACO which introduces the metropolis criterion into the node screening mechanism of the ant colony optimization (ACO) algorithm is presented to generate the initial trajectory that can effectively avoid falling into the local optimal solution and stagnation. Then, considering size constraint of UAVs, this paper gives three trajectory correction schemes to solve the collision avoidance problem and further to optimize the initial trajectory. Last, the discontinuity originated from the sharp turn which occurred in the trajectory planning is solved by the inscribed circle (IC) smooth method, and the produced trajectory has shown better performance in reducing fuel consumption and improving the trajectory safety. Experimental results demonstrate that the proposed method has the high feasibility and effectiveness from aspects of the optimal solution, the collision avoidance, and the smooth trajectory in the trajectory planning problem for UAVs.

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“…To obtain better optimization results, a PSO optimization technique is proposed in [12] to converge at the global minimum, and a custom algorithm is used to generate the coordinates of the search space.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

et al. 2020

Journal of Control Science and Engineering

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The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively.

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Fast and Optimal Path Planning Algorithm (FAOPPA) for a Mobile Robot

Cited by 18 publications

References 10 publications

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

Trajectory Planning for UAV Based on Improved ACO Algorithm

An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot

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