An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle

Che, Gaofeng; Liu, Lijun; Yu, Zhen

doi:10.1007/s12652-019-01531-8

Cited by 104 publications

(38 citation statements)

References 11 publications

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“…graph-based algorithms; 2. algorithms for avoiding obstacles; 3. algorithms using intelligent methods. Analysis of the literature has shown that recently, many different methods and algorithms have been proposed for route planning [4][5][6][7][8][9][10][11][12][13][14]. The article [6] presents a solution for planning the shortest route for moving a robot in a maze based on the Voronoi graph and has the following form in fig.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

“…In [10], an algorithm for optimizing ant colonies based on optimizing a swarm of particles is used to find the optimal route. Due to various limitations such as limited battery power and limited visibility, the ant colony algorithm uses an improved pheromone update rule and a heuristic function based on the particle swarm optimization algorithm.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

“…Step 2. Based on the heuristic information and the weight of the pheromone value, the next search point for ants is determined by formula (10) in article [10].…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

“…Step 3. Then the local pheromone footprint is updated in accordance with the formula (11) [10] Step 4. Determine if all the ants have completed the construction of the trail.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

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Approach to Building a Global Mobile Agent Way Based on Q-Learning

Martovytskyi

Ivaniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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Today, the problem of navigation of autonomous mobile systems in a space where disturbances are possible is urgent. The task of finding a route for a mobile robot is a complex and non-trivial task. At the moment, there are many algorithms that allow you to solve such problems in accordance with the specified criteria for building a route. Most of these algorithms are modifications of "basic" path planning methods that are optimized for specific conditions. The subject of research in the article is the process of building a global path for a mobile agent. The purpose of the work is to create an algorithm for planning the route of autonomous mobile systems in space using the Q-learning algorithm. The following tasks are solved in the article: development of an approach to training and support of a reinforcement learning algorithm for building a global path of a mobile agent; testing the agent's ability to find a path in environments that are not in the training set. The following methods are used: graph theory, queuing theory, Markov decisionmaking process theory and mathematical programming methods. The research is based on scientific articles and other materials from foreign conferences and archives in the field of machine learning, deep learning and deep reinforcement learning. The following results were obtained: an approach was formulated to construct the global path of a mobile agent based on the accumulated data in the process of interaction with the external environment. The environment rewards these actions and the agent continues to carry them out. This approach will allow this method to be applied to a wide range of situations and devices. Conclusions: This approach allows accumulating the knowledge of the outside world for further decision-making when planning a route where the robot can acquire the skill of self-learning, studying and training like a human, and finding the path from the initial state to the target state in an unknown environment. In the modern world, the use of robots and autonomous systems is spreading, designed to replace or facilitate human labor, make it safer and speed it up. Adaptive autonomous path finding algorithms are very important in many robotics applications. Thus, navigation tasks with limited information are relevant today, since this is the main task that the agent solves, and one of the tasks that are part of the robot during operation.

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Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

“…Step 2. Based on the heuristic information and the weight of the pheromone value, the next search point for ants is determined by formula (10) in article [10].…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

“…Step 3. Then the local pheromone footprint is updated in accordance with the formula (11) [10] Step 4. Determine if all the ants have completed the construction of the trail.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

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Approach to Building a Global Mobile Agent Way Based on Q-Learning

Martovytskyi

Ivaniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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“…Lateral positioning is a part of a more general function which allows a car to be driven by itself. Authors in Che et al (2019) have recently proposed an interesting work focuced on path planning by using improved Particle Swarm Optimization. In that context, our research is to find a trade-off between processing time and the efficiency of road marking detection and lane estimation in complex and challenging environments.…”

Section: Introductionmentioning

confidence: 99%

Bayesian curved lane estimation for autonomous driving

Fakhfakh

Chaâri

Fakhfakh

2020

J Ambient Intell Human Comput

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Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also doesn't meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection. Keywords Autonomous driving • embedded camera • road marking • multi-hyperbola • Bayesian framework

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Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Zhao

Zhang

Liu

et al. 2023

Asian Journal of Control

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In the real‐world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability‐based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high‐stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability‐based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability‐based path planning framework can effectively plan the high‐quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.

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An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle

Cited by 104 publications

References 11 publications

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Approach to Building a Global Mobile Agent Way Based on Q-Learning

Bayesian curved lane estimation for autonomous driving

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

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