Real-Time Multi-Agent Mobile Robot based on A Client-Server Model

In mobile robot research domain and especially in its path planning sub-domain, the optimization tools the most used are PSO (Particle Swarm Optimization) and GA (Genetic Algorithms). In fact, mobile robot path planning is a multi-objective optimization problem where at least these objectives are considered: the shortest and the safest path to the goal. Sometimes other objectives are added like travel duration. The subject of this paper is a comparison investigation concerning the efficiency of PSO and GA in mobile robot path planning optimization. We start with a comparison of evolutionary performances of PSO and GA. Then, we apply PSO and GA in the optimization of parameters of DVSF\(^{2}\) (Dynamic Variable Speed Force Field) path planning approach. Different environments of different types, statics and dynamics are considered in these simulations to decide which is better for mobile robot path planning optimization: PSO or GA.

The mobile robot has been one of the hottest fields in recent years, it has been widely applied in the most critical and efficient occasions in the industry, meanwhile, path planning becomes a frontier and interesting research topic for the mobile robot. In this paper, firstly mobile robot is introduced, some universal path planning algorithms of the mobile robot are reviewed, then a fuzzy logic and filter smoothing based on the data from the laser scan sensor is proposed, the path planning and autonomous obstacle avoidance based on this algorithm is emphasized because it can automatically find the best path according to the size and position of gaps between the obstacles in the dynamic environment, finally our designed mobile robot and corresponding Android APP are introduced, the path planning and obstacle avoidance algorithms are tested on this mobile robot, the testing results show this algorithm has the advantage of responding quickly, global optimized and hardware resource saved compared with other algorithms, it is suitable for the mobile robot that is designed on the embedded system and it can meet our designed requirement.

The increasing integration of mobile robots in various industries necessitates efficient navigation strategies amidst dynamic environments. Path planning plays a crucial role in guiding mobile robots from their starting points to target destinations, contributing to automation and enhancing human-robot collaboration. This study focuses on devising a tailored path-planning approach for a fleet of mobile robots to navigate through dynamic obstacles and reach designated trajectories efficiently. Leveraging particle swarm optimization (PSO), our methodology optimizes the path while considering real-time environmental changes. We present a simulation-based implementation of the algorithm, where each robot maintains position, velocity, cost, and personal best information to converge towards the global optimal solution. Different obstacles consist of circles, squares, rectangles, and triangles with various colors and five handle-points used. Our findings demonstrate that PSO achieves a global best cost of 5.1017, indicative of the most efficient path, minimizing overall distance traveled.

Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, pathplanning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot pathplanning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field.

Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.

The recent advances in robotics has resulted in a more convenient use of mobile robots in alications such as assisting the disabled, deliveries and domestic purposes. The main challenge faced by mobile robots is navigation in a dynamic environment, which is path planning for dynamic obstacle avoidance. This paper proposes a novelty method for solving the path planning problem for mobile robots posed by dynamic obstacles based on SLAM (Simultaneous Localisation and Maing) algorithm and Reinforcement Learning. The algorithms implemented relied on the Kinect sensor for maing and rotary encoder for localisation of the robot in the map. The SLAM algorithm implemented resulted in a mean error metric of 4.07%. The modified Q-learning algorithm implemented in this paper allowed the mobile robot to avoid dynamic obstacles by re-planning the path to find another optimal path different from the previously set global optimal path. From the investigation, it was shown that it is possible for a robot to navigate in a dynamic using the Reinforcement Learning technique.

Purpose This review paper aims to provide a comprehensive review of Dubins robot path planning, which has become increasingly important in mobile robotics, autonomous vehicles and industrial applications. The authors identify key developments, analyze existing approaches and highlight the fundamental constraints, algorithms and future opportunities involving curvature-constrained, forward-only path planning. Design/methodology/approach The review consists of three parts: basic Dubins theory, seven decades of development and future opportunities. In the first part, the authors summarize problem formulations and representative computational strategies/algorithms. In the second part, the seventy years of development are divided into four stages, with each stage summarizing developments in important topics such as point-to-point planning, obstacle avoidance and variable speed planning. In addition, the authors analyze current challenges and evaluate recent extensions in cooperative, real-time and learning-based planning frameworks. Findings Dubins path planning has evolved from simple analytical models to complex multirobot and dynamic scenarios. While exact solutions exist for basic tasks, the scalability, obstacle constraints and real-time responsiveness remain active challenges. Promising future directions include integrating machine learning for adaptive planning, and extending to Reeds–Shepp and nonholonomic systems in three-dimensional environments. Originality/value This review offers a structured and in-depth overview of path planning for Dubins robots, connecting core theoretical concepts with the latest developments and real-world implementations. Unlike prior surveys that roughly treat general nonholonomic planning or sampling-based methods, this paper for the first time emphasizes the curvature-constrained planning, especially under the Dubins constraints. By identifying underexplored areas such as dynamic environments, real-time applications, this review offers actionable insights and future research directions valuable to both researchers and practitioners in mobile and industrial robots.

In this paper, a framework for co-operative path planning and task assignment of a multi-agent system is presented. For a candidate algorithm, it is difficult to handle multiple agents in dynamic environments, where it is necessary to replan a new path to perform the given tasks without any collision. To address this problem, a path-planning algorithm based on Particle Swarm Optimization is proposed here to find an optimal path for the multi-agent system working under certain prescribed tasks. The proposed framework is shown to be highly effective due to its reactive management structure to fulfil its mission successfully. The simulation results show that it is a reliable and robust way of solving co-operative path planning problems.

Abstract- The goal of navigating a mobile robot is to find the optimal path to direct its movement, so path planning is the best solution to find the optimal path. Therefore, the two most important problems of path planning must be solved; the first is that the path must avoid collision with obstacles, and second it must reduce the length of the path to a minimum. This paper will discuss finding the shortest path with the optimum cost function by using the Chaotic Particle Swarm Optimization (CPSO), and A*, compare the results between them and the proposed hybrid algorithm that combines A* and Chaotic Particle Swarm Optimization (ACPSO) algorithms to enhance A* algorithm to find the optimal path and velocities of the wheeled mobile robot. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the proposed algorithm in terms of minimum number of an evaluation function and the shortest path length as well as to obtain the optimal or near optimal wheel velocities. Index Terms—Mobile robot, Path planning, Chaotic Particle Swarm Optimization (CPSO), A* algorithm, Fixed obstacles.

Mobile robots are mostly used in many industrial and military applications. Researches in the path planning is one of the most important aspects in mobile robot researches. Path planning for a mobile robot need to find a collision free path through the robot's environment with obstacles from the specified start location to a desired(target) goal location while satisfying certain optimization conditions. Existing path planning methods like graphical methods such as visibility graph, the potential field and the cell decomposition are designed for static environments, in which there are stationary obstacles. In practical systems such as Marine Science Research, Robots in Industry, and military combat applications, robots usually face dynamic environments where both moving and stationary obstacles exist. This project proposes a heuristic based method to search the feasible initial path efficiently. The heuristic based method is then combined into the simulated annealing algorithm based approach for dynamic robot path planning. Therefore the quality of the solution is characterised by the length of the planned path and it is improved with the combined heuristic method in the simulated annealing based approach for both runtime and offline path planning.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

Nowadays, competition takes place in an environment characterized by high market shifting, rapid new technology development and introduction, global competition and customers who need focalization. Therefore, supply chain networks are becoming more dynamic and turbulent than ever before. The Distribution Centre (DC) is an important node in a supply chain network in maintaining the flow of goods and materials among manufacturers and customers. The capability of the DC to maintain the same level of performance in a highly dynamic scenario allows improvement of the competitiveness of the supply chain network. The focus of this chapter is on the allocation of customers to the DCs in very dynamic environments. A Multi-Agent System (MAS) is designed, in order to implement an architecture that is capable of rapid response to changing market conditions. A negotiation approach is proposed for coordinating the involved agents in the architecture: customers and distribution centres. The implementation of the MAS architecture was developed using the JAVA development kit; this platform was used to perform a discrete event simulation and test the proposed approach. Several scenarios are investigated, in scenarios ranging from static to highly dynamic market conditions. The simulation results highlight that the cooperation approach allows obtaining significant growth of performance in several different types of environment.

The Rapidly Exploring Random Tree Star (RRT*) is a probabilistically complete algorithm. It is recognized as a better path planning algorithm, but its path quality and path planning speed still have room for improvement. This paper proposes an improved RRT* algorithm based on alternative paths and triangular area sampling (ATS-RRT*). The alternative paths strategy generates multiple initial paths based on whether the sample points can communicate with the target points and set the path with the smallest cost as the final initial path, which can speed up the initial path planning and improve the initial path finding rate. The triangular area sampling strategy combines every three adjacent nodes to generate some triangle areas and corresponding half-triangle areas. The path quality can be improved quickly by limiting the sampling in these triangle areas. In addition, the direct connection strategy with triangle nodes and the tabu table using in the Rewire process also speeds up the algorithm. Experiments show that the speed of finding the initial path and the success rate of finding the suboptimal path are improved by 2.3 and 1.45 times respectively compared with RRT*, Quick-RRT*, and Informed + Quick-RRT*.