A smart path planner for wheeled mobile robots using adaptive particle swarm optimization

Mohanty, Prases K.; Dewang, Harshal S.

doi:10.1007/s40430-021-02827-7

Cited by 15 publications

(15 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3). Specifically, if the robot reaches the target, this means the distance (dRG) is equal to or near to zero, in which case the robot must be stopped [14].…”

Section: Target-pursuing Behaviormentioning

confidence: 99%

“…( 4). Particularly, if the robot detects an obstacle, it tries to avoid it and then continues to reach the target [14].…”

Section: Obstacle-pursuing Behaviormentioning

confidence: 99%

“…In another work in Ref. [14], the authors proposed an intelligent Adaptive Particle Swarm Optimization (APSO) algorithm for robot path planning in uncertain environments. The drawback of this method is that it needs to be modified to work in dynamic or multi-robot systems and to avoid the early convergence in local minima.…”

Section: Introductionmentioning

confidence: 99%

“…From previous studies, it was seen that many optimization methods produce low-quality solutions or suffer from an early convergence in the primary stages. These issues are observed in PSO [14], Ant colony [7], GWO [15]. etc., therefore there is a need to study and improve these methods to overcome their limitations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Abed¹,

Lutfy²,

Al-Doori³

2022

JESA

View full text Add to dashboard Cite

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.

show abstract

“…(3). Specifically, if the robot reaches the target, this means the distance (dRG) is equal to or near to zero, in which case the robot must be stopped [14].…”

Section: Target-pursuing Behaviormentioning

confidence: 99%

“…( 4). Particularly, if the robot detects an obstacle, it tries to avoid it and then continues to reach the target [14].…”

Section: Obstacle-pursuing Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Abed¹,

Lutfy²,

Al-Doori³

2022

JESA

View full text Add to dashboard Cite

show abstract

“…The motion planning problem from a robotics perspective has the goal of determining the set of inputs for an open-loop control that drives the robot from an initial configuration to a goal configuration in a feasible and collision-free space while the kinematic and dynamic constraints are satisfied. Motion planning has been investigated from different perspectives [14] using techniques such as potential field, neuronal networks [29] or genetic algorithms [30], to name a few. In particular, we are interested in motion planning for automated vehicles where the problem has been addressed from formal approaches like control theory [27] to a machine learning problem.…”

Section: Introductionmentioning

confidence: 99%

Custom Distribution for Sampling-Based Motion Planning

Flores-Aquino¹,

Vasquez-Gomez²,

Gutiérrez-Frías³

2021

Preprint

View full text Add to dashboard Cite

Sampling-based algorithms are widely used in robotics because they are very useful in high dimensional spaces. However, the rate of success and quality of the solutions are determined by an adequate selection of their parameters such as the distance between states, the local planner, and the sampling method. For robots with large configuration spaces or dynamic restrictions selecting these parameters is a challenging task. This paper proposes a method for improving the results for a set of the most popular sampling-based algorithms, the Rapidly-exploring Random Trees (RRTs) by adjusting the sampling method. The idea is to replace the sampling function, traditionally a Uniform Probability Density Function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries of a similar task. With few samples, our method builds the custom distribution allowing a higher success rate and sparser trees in randomly new queries. We test our method in several common tasks of autonomous driving such as parking maneuvers or obstacle clearance and also in complex scenarios outperforming the base orig-

show abstract