A novel whale optimization algorithm of path planning strategy for mobile robots

Dai, Yaonan; Yu, Jiuyang; Zhang, Cong; Zhan, Bowen; Zheng, Xing

doi:10.1007/s10489-022-04030-0

Cited by 25 publications

(16 citation statements)

References 36 publications

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“…Dai et al. [ 12 ] have proposed a method that uses a novel whale optimization algorithm incorporating potential field factors to enhance mobile robots’ dynamic obstacle avoidance ability. Miao et al.…”

Section: Introductionmentioning

confidence: 99%

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Chen

Liang

2023

Sensors

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Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot’s driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.

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Section: Introductionmentioning

confidence: 99%

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Chen

Liang

2023

Sensors

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“…Arti cial Bee Colony (ABC) algorithm [30], Whale optimization algorithm [31] and many other existing algorithms has been proposed as well as implemented in the wide range of applications of AMRs [32].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Thakur¹,

Das²,

Kumari

et al. 2023

Preprint

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In this paper, a unique Machine Learning (ML) model namely, Adaptive Block Coordinate Descent Logistic Regression (ABCDLR), is proposed for segregating the movement of an Autonomous Mobile Robot (AMR) by framing it as three class problem, i.e., no, left, and right turn. The velocities of the left and right wheels, as well as the distance of the obstacle from AMR, are collected in real time by two Infrared (IR) and one Ultrasonic (US) sensors, respectively. The performance of the proposed algorithm is compared with three other state-of-the-art ML algorithms, such as, K-Nearest Neighbour (KNN), Naïve Baiyes, and Gradient Boosting, for obstacle avoidance by AMR; considering the accuracy, sensitivity, specificity, precision values for three different speed conditions, i.e., low, medium, and high. Various Logistic Regression (LR) model parameters, such as, pseudo R-squared (R2), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), LL-null, and Log-Likelihood Ratio (LLR) are considered to investigate the performance of the proposed ABCDLR model. Furthermore, the proposed model has been applied for path planning in three different types of dense environments, and its performance is compared with four other competitive path planning approaches, such as, A*, Fuzzy Logic Controller(FLC), Vector Field Histogram(VFH) and ASGDLR.

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“…Simulation and real machine experiments in MATLAB verified the effectiveness of the improvement and improved the distance by about 20.63% compared to other algorithms that have been improved. Yaonan Dai [21] proposed a new whale optimization algorithm (NWOA), which designs virtual obstacles and introduces adaptive techniques to solve the problems of slow convergence and easy to fall into local optimization in robot path planning by the original algorithm and other two improved algorithms for experiments, and the results showed that the path planning time and the average lengths of the path of the NWOA are as short as possible. Yucen Cai [22] used the secondary optimization of the harmonic search algorithm to improve the quality and global search ability of the population and improve the search accuracy, and introduced a dynamic balancing strategy and a population reconstruction mechanism to regulate the global search ability and local search ability of the algorithm to avoid getting into local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm

2023

Sensors

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An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved.

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A novel whale optimization algorithm of path planning strategy for mobile robots

Cited by 25 publications

References 36 publications

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm

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