Single Mobile Robot Research Articles

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (<i>STGA</i>) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed <i>Node-Filter</i> to generate enough topological nodes, and simplified the node set by <i>Node-Selection</i>. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by <i>STGA</i> had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. <i>Note to Practitioners</i>&#x2014;Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

The Spatiotemporal Dynamic Impact Mechanism of Soil Greenhouse Gases under Precipitation Based on Environmental Law

There is a chain reaction between precipitation patterns and atmospheric greenhouse gases. Understanding the impact mechanism of the spatiotemporal dynamics of soil greenhouse gases under precipitation changes is of great significance, allowing for a more accurate assessment of soil greenhouse gas budgets under future precipitation patterns. In view of this, the research uses sensors to collect environmental sample data and gas concentration data, using Conv-LSTM to achieve data analysis. The research also introduces the kernel DM model to optimize the gas distribution modeling problem. Compared to manual periodic monitoring or gas monitoring using a single mobile robot, the gas distribution model used in this study is innovative. The innovation lies in its ability to capture global gas flow trends in data sampling and predictive analysis. The results show that when soil moisture changes between 5% and 35%, the soil carbon dioxide gas flux after the water addition treatment takes a 20% soil moisture level as the inflection point, showing a trend of first increasing, and then decreasing. This indicates that the mathematical model proposed in this study is effective in collecting and analyzing environmental data.

2D LiDAR Based Reinforcement Learning for Multi-Target Path Planning in Unknown Environment

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.

Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.

Temporal Logic Planning and Receding Horizon Control for Signal Source Localization

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot’s trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization.

A non-potential orthogonal vector field method for more efficient robot navigation and control

To navigate and control a single mobile robot or a robotic swarm with higher efficiency, a novel non-potential orthogonal vector field method is proposed in this paper, which is modified from the traditional artificial potential field method. The improvement strategy aims at making the overall repulsive vector field orthogonal to the attractive vector field in some conditions. And the same potential field function is still applied to the Lyapunov-based stability analysis. In short, such an improvement strategy combines the advantages of the artificial potential field method with the non-potential vector field method, namely a combination of theoretical completeness and control efficiency. Finally, the effectiveness of the proposed method is validated both by numerical simulations with statistical significance and real experiments. The comparisons between our method and other methods are also presented.

Collaborative Mobile Robotics for Semantic Mapping: A Survey

Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.

An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target.

Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of "tumbling" and "swimming" behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the "brain," body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.

Cooperative distributed nonlinear model predictive control of a formation of differentially-driven mobile robots

Due to the increasing complexity of today’s tasks and the demand for higher performance and robustness, using a single robot is not always expedient in robotics applications. Therefore, having multiple autonomous robotic agents collaborate utilizing explicit communication is gaining more attention. The goal of this article is to develop a distributed algorithm that allows the formation control of multiple differentially-driven mobile robots. The formation control goal is formulated in a novel manner by leveraging results on the control of a single differentially-driven mobile robot, which is sophisticated due to the present non-holonomic constraint. This results in a nonlinear distributed control problem. The fundamental functionality of the developed algorithm is analyzed in simulation scenarios. The applicability to real-life scenarios is demonstrated through experiments with custom hardware. To the best of the authors’ knowledge, this is the first time that nonlinear distributed model predictive control is applied to a formation of differentially-driven mobile robots using a theoretically-founded cost function and, moreover, that the results are verified with hardware experiments.

Multi-Objective Scheduling of Single Mobile Robot Based on Grey Wolf Optimization Algorithm

Multi-Objective Scheduling of Single Mobile Robot Based on Grey Wolf Optimization Algorithm

A Path-Following Controller for Guiding a Single Robot or a Multi-robot System

A solution to the problem of path-following by a team of terrestrial mobile robots is proposed in this paper. The proposal, in this case, is a formation controller dealing with three robots navigating in a coordinate way (as a formation). Based on the controller proposed to guide the formation to follow a prescribed path, an extension to the case of path-following with a single terrestrial mobile robot is also proposed. When regarding a formation of mobile robots, the proposed solution consists in applying a path-following controller to the center of mass of the formation, dealt with as a single virtual robot, and trajectory-tracking controllers to the individual robots in the formation. The advantage of such approach is that it allows planning the motion of the desired formation without specifying how each robot should move. The movement is specified for the formation as a whole, using a representation called cluster-space, and the movement of the individual robots is derived from the specification of the formation movement directly, using transformations from the cluster-space to the space of the robots and vice versa. In the sequel, the path-following controller designed for the virtual robot is analyzed in detail, now dealing with the possibility of being also used as a path-following controller applied to a single real robot. Theoretical stability analysis is presented, as well as some experimental results, whose analysis allows claiming that the proposed controller is suitable to guide either a single robot or a multi-robot formation when following a path.

Neural dynamics based complete grid coverage by single and multiple mobile robots

Navigation of mobile robots in a grid based environment is useful in applications like warehouse automation. The environment comprises of a number of free grid cells for navigation and remaining grid cells are occupied by obstacles and/or other mobile robots. Such obstructions impose situations of collisions and dead-end. In this work, a neural dynamics based algorithm is proposed for complete coverage of a grid based environment while addressing collision avoidance and dead-end situations. The relative heading of the mobile robot with respect to the neighbouring grid cells is considered to calculate the neural activity. Moreover, diagonal movement of the mobile robot through inter grid cells is restricted to ensure safety from the collision with obstacles and other mobile robots. The circumstances where the proposed algorithm will fail to provide completeness are also discussed along with the possible ways to overcome those situations. Simulation results are presented to show the effectiveness of the proposed algorithm for a single and multiple mobile robots. Moreover, comparative studies illustrate improvements over other algorithms on collision free effective path planning of mobile robots within a grid based environment.

Simultaneous transportation of multiple shelves by single mobile robot and passive docking mechanism

Simultaneous transportation of multiple shelves by single mobile robot and passive docking mechanism

Slave Coordinated Speed Analysis and Coordinated Control Method

When the container is being repaired, it is necessary to transfer the container to be repaired between the temporary storage area and the repair area. Due to the large size, when manually transported by forklift, the line of sight is blocked, there is a great safety hazard and efficiency. It is relatively low, and it is inconvenient to use a single mobile robot to carry it. Therefore, to solve this problem, a dual-mobile robot collaborative operation system is developed, and two mobile robots are used to move containers together to complete container maintenance and handling. Through the analysis of the cooperative handling of the dual mobile robots, a mathematical model of the cooperative movement of the dual mobile robots is established, and kinematics analysis is carried out to provide a theoretical basis for controlling the cooperative operation of the dual mobile robots.

Intelligent Control of Mobile Robot Via Waypoints Using Nonlinear Model Predictive Controller and Quadratic Bezier Curves Algorithm

This article introduces a new control methodology to intelligently drive the motion of a mobile robot via given waypoints. A nonlinear model predictive controller (NMPC) is utilized to track the waypoints that are placed randomly at different positions in a given environment. Hence, various tracking paths can be generated based on locations of waypoints. Additionally, a Quadratic Bezier Curves algorithm has been applied for obstacle avoidance. It is combined with the NMPC via a switching mechanism. Hence, the transportation of the mobile robot will take the priority of avoiding obstructing obstacles if exist before moving forward to the next target waypoint. Single and multiple mobile robots have been simulated into several scenarios to investigate the performance of the developed control scheme.

Optimal path search and control of mobile robot using hybridized sine-cosine algorithm and ant colony optimization technique

Purpose This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments. Design/methodology/approach The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point. Findings Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these. Originality/value Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

Topological Design Methods for Mecanum Wheel Configurations of an Omnidirectional Mobile Robot

A simple and efficient bottom-roller axle intersections approach for judging the omnidirectional mobility of the Mecanum wheel configuration is proposed and proved theoretically. Based on this approach, a sub-configuration judgment method is derived. Using these methods, on the basis of analyzing the possible configurations of three and four Mecanum wheels and existing Mecanum wheel configurations of robots in practical applications, the law determining wheel configuration is elucidated. Then, the topological design methods of the Mecanum wheel configurations are summarized and refined, including the basic configuration array method, multiple wheels replacement method, and combination method. The first two methods can be used to create suitable multiple-Mecanum-wheel configurations for a single mobile robot based on the basic Mecanum wheel configuration. Multiple single robots can be arranged by combination methods including end-to-end connection, side-by-side connection, symmetrical rectangular connection, and distributed combination, and then, the abundant combination configurations of robots can be obtained. Examples of Mecanum wheel configurations design based on a symmetrical four-Mecanum-wheel configuration and three centripetal configurations using these topological design methods are presented. This work can provide methods and a reference for Mecanum wheel configurations design.

A Comparative Study of Various Intelligent Algorithms based Path Planning for Mobile Robots

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot. Simulation results, which carried out using MATLAB 2014 environment, show the validity of the kinematic model for Nonholonomic mobile robot and demonstration that the proposed algorithm perform better than original PSO and FF algorithms under the same environmental constraints by providing the smoothness velocity and shortest path for each mobile robot. &#x0D;

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.