Mobile Robot Navigation Algorithm Based on Ant Colony Algorithm with A* Heuristic Method

Li, Shiguo; Su, Weihua; Huang, Ruqiang; Zhang, Shiyue

doi:10.1109/icras49812.2020.9135055

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…In this section, collision avoidance planning algorithms are divided into two categories: traditional algorithms and intelligence algorithms. The traditional collision avoidance planning algorithms mainly include a rapidly exploring random tree (RRT) [5], a dynamic window approach [6], an artificial potential field method [7], a bug algorithm [8], and intelligent collision avoidance algorithms, mainly include ant colony optimization algorithms [9], genetic algorithms (GA), and reinforcement learning algorithms [10]. The advantages and disadvantages of commonly used path planning algorithms are shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning

Gao,

Han,

Wang

et al. 2023

JMSE

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A UUV can perform tasks such as underwater surveillance, reconnaissance, surveillance, and tracking by being equipped with sensors and different task modules. Due to the complex underwater environment, the UUV must have good collision avoidance planning algorithms to avoid various underwater obstacles when performing tasks. The existing path planning algorithms take a long time to plan and have poor adaptability to the environment. Some collision-avoidance planning algorithms do not take into account the kinematic limitations of the UUV, thus placing high demands on the performance and control algorithms of UUV. This article proposes a PPO−DWA collision avoidance planning algorithm for the UUV under static unknown obstacles, which is based on the proximal policy optimization (PPO) algorithm and the dynamic window approach (DWA). This algorithm acquires the obstacle information from forward-looking sonar as input and outputs the corresponding continuous actions. The PPO−DWA collision avoidance planning algorithm consists of the PPO algorithm and the modified DWA. The PPO collision avoidance planning algorithm is only responsible for outputting the continuous angular velocity, aiming to reduce the difficulty of training neural networks. The modified DWA acquires obstacle information and the optimal angular velocity from the PPO algorithm as input, and outputs of the linear velocity. The collision avoidance actions output by this algorithm meet the kinematic constraints of UUV, and the algorithm execution time is relatively short. The experimental data demonstrates that the PPO−DWA algorithm can effectively plan smooth collision-free paths in complex obstacle environments, and the execution time of the algorithm is acceptable.

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Section: Related Workmentioning

confidence: 99%

Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning

Gao,

Han,

Wang

et al. 2023

JMSE

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“…There are many types of traditional collision avoidance planning algorithms, such as the Dynamic Window method [13], Rapidly-Exploring Random Tree [14], and the artificial potential field method [15], but these lack the ability to learn and have poor adaptability to the environment. Heuristic search algorithms commonly used to solve collision avoidance planning problems include the genetic algorithm [16], Particle Swarm Optimization Algorithm [17], and Ant Colony Algorithm [18]. It is difficult to achieve realtime planning for collision avoidance planning based on heuristic search algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

Wang,

Gao,

Wang

et al. 2023

JMSE

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Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning.

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“…There is an abundance of literature on this subject to cover in the current study; notwithstanding, it is necessary to mention merged approaches. Some developments may be cited, for instance, in [33], an ant colony algorithm was proposed using an evaluation function of the A* algorithm to accelerate the algorithm convergence rate. It was designed for the path planning.…”

Section: Introductionmentioning

confidence: 99%

Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree

et al. 2022

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This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.

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Mobile Robot Navigation Algorithm Based on Ant Colony Algorithm with A* Heuristic Method

Cited by 4 publications

References 19 publications

Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning

Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning

Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree

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