Bio-Inspired Reactive Approaches for Automated Guided Vehicle Path Planning: A Review

From naval operations to ocean science missions, the importance of autonomous vehicles is increasing with the advances in underwater robotics technology. Due to the dynamic and intermittent underwater environment and physical limitations of underwater unmanned vehicle (UUV), feasible and optimal path planning is crucial for autonomous underwater operations. According to different mission, the path planning method of UUV is divided into two categories: the point to point path planning and the complete coverage path planning. The objective of this thesis is to develop and demonstrate an efficient underwater path planning method that is adapted to complicated ocean environment. In this thesis, existing path planning method for the fields of ocean science and robotics are first reviewed, and then local dynamic obstacle avoidance method is proposed to avoid dynamic obstacles. Based on this again, the path planning of UUV in local dynamic environment can be efficiently implemented by adopting rolling window path planning method and local dynamic obstacle avoidance method. This method with the guide point strategy combines global path planning with local dynamic path planning, so that not only the requirements of real-time on-line path planning for UUV are met, the global optimality is also considered. A navigation route for UUV is planned in advance by using priori environmental information based on ant colony algorithm, so it provides the reference information for the selection of guide point. In order to solved the problem of area coverage search, a complete coverage path planning method is proposed by combining ant colony algorithm with biologically inspires neural network. In order to demonstrate underwater path planning method, all of the above ideas and methods developed were tested in simulation experiments.

This paper conducts the hybridization of Swarm intelligence and Evolutionary Algorithm for Continuous and Discrete optimization. Optimization is the process of selecting the best element by following some rules and criteria from some set of available alternatives. Function optimization means finding the best available value of some given objective function in a defined domain. In this work we have proposed an innovative approach, by hybridizing Genetic Algorithm (GA) and Swarm Intelligence Algorithm (SIA). In this paper work we have implemented one evolutionary programming based algorithm - Improved First Evolutionary Programming (IFEP) and one swarm intelligence algorithm - Ant Colony Optimization (ACO). We have also used Travelling Salesman Problem (TSP) as a discrete problem. We have implemented both GA and ACO also to solve the Travelling Salesman Problem. We have compared the result produced by IFEP and ACO for Continuous Optimization. From the comparative study we have found that ACO is the better among the two. We also have compared the result produced by GA and ACO for Discrete Optimization and from the comparative study we have found that ACO often works better. We have conducted some experiments to optimize the parameters of ACO and GA and the amount of exploration and exploitation needed for ACO to produce the best result. using the best found parameter we have implemented a hybrid of Genetic Algorithm and Swarm Intelligence Algorithm and tested it with different strategies. Then we have conducted a comparative study between the hybrid and two other conventional Genetic and Swarm Intelligence Algorithms to observe the performance of our proposed hybrid algorithm. In some cases we have observed better performance from our proposed hybrid algorithm.

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

Automated guided vehicles (AGVs) are extensively used in many applications such as intelligent transportation, logistics, and industrial factories. In this paper, we address the path planning problem for an AGV system (i.e. a team of identical AGVs) with logic and time constraints using Petri nets. We propose a method to model an AGV system and its static environment by timed Petri nets. Combining the structural characteristics of Petri nets and integer linear programming technique, a path planning method is developed to ensure that all task regions are visited by AGVs in time and forbidden regions are always avoided. Finally, simulation studies are presented to show the effectiveness of the proposed path planning methodology.

Path planning techniques for mobile robots: Review and prospect

Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

Automated Guided Vehicle (AGV) path planning is the core technology of warehouse AGV. Reasonable path planning is helpful to maximize the benefits of warehouse space and time. Scholars at home and abroad have already made extensive and in-depth research on warehouse AGV path planning, and have achieved fruitful research results. In this paper, the models and environmental modeling methods of warehouse AGV path planning are summarized. It turned out that the cell method is intuitive and easy to model, the geometric method is safe, but difficult to update, and the artificial potential field method is easy to solve, but easy to fall into local optimum. The optimization methods of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm in AGV path planning are emphatically summarized. It is found that genetic algorithm is suitable for complex and highly nonlinear path planning problems, ant colony algorithm is suitable for discrete path planning problems, and particle swarm algorithm is suitable for real number path planning problems. The research summary of this paper provides reference value for the research of intelligent optimization algorithm of AGV path planning and new ideas for broadening the application field of AGV path planning.KeywordsWarehouseAGVPath planningIntelligent optimization algorithm

We study the data collection problem in an Internet of Things (IoT) network where an unmanned aerial vehicle (UAV) is utilized to aggregate data from a set of IoT devices. We formulate the scheduling and path planning problems for the UAV. The goal of the scheduling problem is to find the sequence of nodes that the UAV will visit to complete the data collection task in the shortest possible time, ensuring that it does not run out of energy during its mission. We express this problem as a mixed-integer nonlinear problem and propose an efficient algorithm to solve the aforementioned NP-hard problem in polynomial time. Path planning problem aims to find a collision-free path for the UAV. While the state-of-the-art schemes have focused on solving the path planning problem in static environments, we study the problem in a dynamic environment with moving obstacles. We develop an algorithm that works on both static and dynamic environments. Our method combines deep reinforcement learning (RL) with graph-based global path planning algorithms to find a collision-free path for the UAV. One important advantage of our RL-based method over the existing studies is its map independency, which allows us to transform the agent's learning from one environment to another. Via simulation studies, we show that our method is significantly effective in improving the safety of the path planning algorithms in dynamic environments.

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

Automated guided vehicle (AGV) is widely used in intelligent warehouse systems. The ant colony optimization is a classical swarm intelligence optimization algorithm. It can achieve good path planning results for AGV. But the use of uniformly distributed initialization pheromone concentration often leads the algorithm to fall into a local optimum solution. And under congestion, the common strategy of the ant colony optimization is to wait or re-plan the path, resulting in reduced efficiency of the AGV. This paper proposes a static path planning method considering the congestion factor. The congestion factor is introduced into the ant colony algorithm. Firstly, the congestion degree is analyzed according to the traffic flow. And the congestion factor is added to the transfer rule so that the AGV can choose autonomously whether to avoid congested road sections when planning the path. Then, nonuniform distribution of initialization pheromone concentration is used instead of uniform distribution of initialization pheromone concentration, avoiding blind search in the early stage of the algorithm. Finally, simulation experiments are conducted on the AGV in a 20×20 map. And the results prove that the method can effectively avoid congested road sections, increase the flexibility of the algorithm and improve the convergence speed of the algorithm.

In this article, a new path planning algorithm is proposed. The algorithm is developed on the basis of the algorithm for finding the best value using multi-objective evolutionary particle swarm optimization, known as the MOEPSO. The proposed algorithm is used for the path planning of autonomous mobile robots in both static and dynamic environments. The paths must follow the determined criteria, namely, the shortest path, the smoothest path, and the safest path. In addition, the algorithm considers the degree of mutation, crossover, and selection to improve the efficiency of each particle. Furthermore, a weight adjustment method is proposed for the movement of particles in each iteration to increase the chance of finding the best fit solution. In addition, a method to manage feasible waypoints within the radius of obstacles or blocked by obstacles is proposed using a simple random method. The main contribution of this article is the development of a new path planning algorithm for autonomous mobile robots. This algorithm can build the shortest, smoothest, and safest paths for robots. It also offers an evolutionary operator to prevent falling into a local optimum. The proposed algorithm uses path finding simulation in a static environment and dynamic environment in conjunction with comparing performance to path planning algorithms in previous studies. In the static environment (4 obstacles), the shortest path obtained from the proposed algorithm is 14.3222 m. In the static environment (5 obstacles), the shortest path obtained from the proposed algorithm is 14.5989 m. In the static environment (6 obstacles), the shortest path obtained from the proposed algorithm is 14.4743 m. In the dynamic environment the shortest path is 12.2381 m. The results show that the proposed algorithm can determine the paths from the starting point to the destination with the shortest distances that require the shortest processing time.

Task scheduling is an important problem that is encountered in distributed systems, project management, and cloud computing. Task dependencies used to model the so-called directed acyclic graphs (DAGs) and for the scheduling optimization have been widely used. However, such methods do not work well in dynamic environments and complex constraints. Task scheduling using DAGs is proposed in this paper, along with the application of Artificial Intelligence (AI) algorithms exploring Artificial Neural Networks (ANN), Artificial Intelligence algorithms, which include Genetic Algorithms (GA) and a specific type (Reinforcement Learning (RL)) as novelty addition to the scheduling problem. The proposed method is developed to enhance inventory efficiency, bound make span, and intend to dynamically cope with changes in resource availability and task priorities. The unusual effect that paper accounts for in that race was the application of artificial intelligence to enhance the process of scheduling. The workflowSim simulator is used to evaluate the proposed method on both synthetic and real-world workflows. Experimental results highlight the method’s superior effectiveness when compared with alternative algorithms.

Planning the path is the most important task in the mobile robot navigation. This task involves basically three aspects. First, the planned path must run from a given starting point to a given endpoint. Secondly, it should ensure robot’s collision-free movement. Thirdly, among all the possible paths that meet the first two requirements it must be, in a certain sense, optimal.Methods of path planning can be classified according to different characteristics. In the context of using intelligent technologies, they can be divided into traditional methods and heuristic ones. By the nature of the environment, it is possible to divide planning methods into planning methods in a static environment and in a dynamic one (it should be noted, however, that a static environment is rare). Methods can also be divided according to the completeness of information about the environment, namely methods with complete information (in this case the issue is a global path planning) and methods with incomplete information (usually, this refers to the situational awareness in the immediate vicinity of the robot, in this case it is a local path planning). Note that incomplete information about the environment can be a consequence of the changing environment, i.e. in a dynamic environment, there is, usually, a local path planning.Literature offers a great deal of methods for path planning where various heuristic techniques are used, which, as a rule, result from the denotative meaning of the problem being solved. This review discusses the main approaches to the problem solution. Here we can distinguish five classes of basic methods: graph-based methods, methods based on cell decomposition, use of potential fields, optimization methods, фтв methods based on intelligent technologies.Many methods of path planning, as a result, give a chain of reference points (waypoints) connecting the beginning and end of the path. This should be seen as an intermediate result. The problem to route the reference points along the constructed chain arises. It is called the task of smoothing the path, and the review addresses this problem as well.

With the rapid advancement of robotics technology, path planning has attracted extensive research attention. Reinforcement learning, owing to its ability to acquire optimal policies through continuous interaction with the environment, offers a promising solution for path planning in environments with incomplete or unknown information. However, reinforcement learning‐based path planning methods often suffer from high training complexity and low utilization of effective samples. To address these issues, this paper proposes an improved deep reinforcement learning (DRL) algorithm. The proposed approach builds upon the deep deterministic policy gradient (DDPG) algorithm and incorporates a short‐term goal planning strategy based on local perceptual information, which decomposes the global navigation task into multiple short‐term subgoals, thereby reducing task complexity and enhancing learning efficiency. Furthermore, a reward function integrating the artificial potential field (APF) method is designed to improve obstacle avoidance capability. To tackle the low utilization of effective experiences in DDPG, a dual experience pool strategy is introduced to improve experience utilization efficiency and accelerate model training. The parameters for short‐term goal selection are optimized through multiple comparative experiments, and the proposed method is evaluated against several DRL‐based path planning approaches in a static environment. Experimental results demonstrate that the improved algorithm significantly accelerates convergence. Moreover, dynamic environment simulation experiments verify that the proposed algorithm can effectively avoid moving obstacles and achieve safe navigation to the target position.

The handling operations performed in ports require the use of equipment operating in a dynamic environment. Some tasks may not be fully carried out due to equipment failure or power breakdown that may occur particularly with the automated guided vehicles (AGV). The unavailability of equipment such as AGV has important consequences in terms of respecting the deadlines of different operations that a port should perform, such as the loading and unloading operations of ships. This situation can aggravate if there are also traffic problems in the port with some inaccessible network nodes. A part of the equipment will be blocked or the operations will take longer than expected if they don`t take the optimal path to connect the loading/unloading points and storage areas. These reasons confirm the usefulness of establishing a robust system able to resolve the problem of assigning containers in the static and dynamic environments. In a previous work, we developed a system for assigning containers in a static environment. In order to improve this method, we devote this paper to the study of the robustness of our system to the dynamic environment of the port. The numerical tests included in this paper show an adequate performance of our method for this particular dynamic environment.