Leader-follower Approach Research Articles

Distributed fixed-time formation tracking control for multiple underactuated USVs with lumped uncertainties and input saturation

A fixed-time distributed formation control strategy is investigated for multiple underactuated unmanned surface vehicles (USVs) with unmeasured velocities and input saturation. Initially, a necessary coordinate transformation is applied to the mathematical model of USVs to address the underactuated issue. Subsequently, a fixed-time extended state observer (FESO) is constructed to estimate unmeasured velocities and lumped disturbances of USVs based on input and output data in the control loop. Meanwhile, the leader–follower approach is applied to achieve a preset formation. A fixed-time differentiator is utilized to compute real-time differential signals for virtual control laws, which simplifies the complexity of controller design. Furthermore, a fixed-time distributed formation controller is designed based on an asymmetric differentiable saturation model. The effects of input saturation are eliminated by a designed auxiliary system. Finally, the fixed-time stability of the closed-loop system is analyzed through the Lyapunov stability theory. The comparison simulation results verify the effectiveness and superiority of the proposed formation control scheme.

Attitude and position control for formation flying of space robots equipped with a robotic manipulator

This paper presents a novel six degree-of-freedom guidance and control algorithm for free-flyer robots equipped with a robotic manipulator operating on the International Space Station. The proposed algorithm combines a sliding mode controller for rotational motion and a model predictive controller for translational motion. A novel prediction-twisting sliding mode controller (PT-SMC) is introduced to stabilize configuration changes induced by the manipulator and correlated disturbances and to achieve a trade-off among control law flexibility, robustness, and accuracy. The proposed PT-SMC is shown to have improved stability compared to a first-order sliding mode controller. Model predictive control is investigated as a position controller to achieve formation flight between multiple free flyers in a constrained environment, and a leader–follower approach is used to move a triangular formation of robots. A control allocation problem is solved to address the subdivision of attitude and position controls between twelve thrusters. Simulation results of multiple robots approaching a target object demonstrate the improved performance of the proposed control approaches.

Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control.

Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader-follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control.

Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach

Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

A novel hedge algebra formation control for mobile robots

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra’s significant potential in the mobile robots’ domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain.

Robust discrete-time super twisting formation protocol for a 6-DOF Quadcopter swarm

This paper contains a discrete-time higher-order sliding mode control for achieving the formation of a 6-Degrees of Freedom (DOF) Quadcopter swarm using the concept of a discrete Multi-agent system (DMAS). A graph theory is used to represent the communication between the Quadcopters and a leader-follower approach of DMAS is used to design the discrete super-twisting (DST) protocol for the formation of a 6-DOF Quadcopter swarm. The DST protocol not only ensures robustness but also reduces the convergence time and the chattering during the formation process of Quadcopters in a swarm. The stability condition of the formation is also derived using the Lyapunov function. The protocol is validated for the formation of 5 Quadcopters in the given pattern. The simulation results show that the formation is achieved in a finite time. Further to show the efficacy of the DST formation protocol, the results are compared with the first-order discrete-time sliding mode controllers designed by switching type reaching law and non-switching type of reaching law. As the first-order discrete-time sliding mode controllers have a significant amount of quasi-sliding mode band (QSMB) in the sliding surface during the operations, the DST protocol of higher-order discrete-time sliding mode control is preferred. From the comparative analysis, it is inferred that the DST protocol performs better for the formation of a 6-DOF Quadcopter swarm.

Coordination control of the automated fiber placement system using photogrammetry-based leader–follower approach

The use of multiple robots in an automated fiber placement system (AFPS) makes it possible to manufacture composite structures with complex shapes. The robots in such system usually adopt independent trajectory planning and control methods. This makes the AFPS lack coordination, and rely heavily on the high-precision calibration of the system when completing complex fiber placement tasks. In this article, a novel photogrammetry-based leader–follower (PBLF) approach is proposed for a particular AFPS which consists of a serial manipulator and a parallel robot to improve its accuracy and coordination. The approach is composed of an offline trajectory planning module, a leader module, and a follower module. The trajectory planning algorithm in the offline trajectory planning module solves the problem of robot workspace constraints and mandrel occlusion in the fiber placement tasks for complex structure. Besides, a novel photogrammetry-based pose tracking method is proposed to make the follower robot track its desired relative trajectory with respect to the moving reference frame and keep the coordination of the two robots. The theoretical stability of the proposed approach is proved. The results of experiments indicate that the proposed approach can reduce the average mean absolute errors of the relative pose in position and orientation direction by 62.15% and 46.42%, respectively. While the corresponding average root mean square errors are reduced by 61.20% and 45.26%, respectively. Therefore, compared with Non-Coordination approach, the PBLF approach can effectively improve the coordination of the two robots.

A strategy based on Wave Swarm for the formation task inspired by the Traveling Salesman Problem

Multi-robots can perform complex tasks such as exploration, foraging, and formation. Efficient communication between robots can contribute to the accomplishment of collective tasks through efficient message exchange. This paper proposes a strategy based on the message’s propagation technique, Wave Swarm, for the formation task inspired by the Traveling Salesman Problem. The Wave Swarm communication approach uses the concept of wave propagation for message exchange between neighbors, establishing a Father and Son relationship between robots. However, different pairs of Father and Son robots can impact the simulation time, the average distance traveled by each robot, and the number of messages exchanged during the formation task. Thus, given a random distribution of robots into a swarm, we model the problem of assigning one position and route for each robot to achieve its place in the formation as a Traveling Salesman Problem. The routes resulting from the TSP solution establish a new parental relationship between the robots in the swarm. We performed preliminary experiments to define the technique used in the TSP resolution. We tested reinforcement learning and the genetic algorithm using the TSPLIB95 library. Thus, we develop a strategy for formation tasks based on Wave Swarm and TSP solved with reinforcement learning. We adopted the leader–follower approach in an unknown environment to validate the proposal. The results show the behavior of different sizes of robot groups for various desired shapes. Experiments with the robot simulator CoppeliaSim (V-REP) validate the proposed strategy and highlight its efficiency and robustness while running the formation task.

Nexus of Deep Reinforcement Learning and Leader–Follower Approach for AIoT Enabled Aerial Networks

The Industrial Internet of Things (IIoT) is a new industrial 4.0 paradigm that combines IoT, robotics, cyber-physical systems, and other future industrial advancements. Unmanned Aerial Vehicles (UAVs), part of the IIoT infrastructure, have a significant potential for civil and military purposes. Through the Artificial Intelligence of Things (AIoT), a well-organized group of UAVs outperforms a single large UAV in terms of device scalability, maintenance, and expense. Therefore, the UAV swarm with industry 4.0 intelligence can be used for a wide range of 24/7 security and remote monitoring applications. Though multi-UAV systems are beneficial, their application has many challenges. There is a high risk of collision in the multi-UAV system without coordination. This paper proposes an AIoT-based Navigation and Formation Control (AIoT-NFC) mechanism to scale down the collision risk by combining Deep Reinforcement Learning (DRL) with the leader-follower approach. In AIoT-NFC, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed to navigate UAVs in remote surveillance without colliding with obstacles and other UAVs. Furthermore, the AIoT-NFC system incorporates a fault tolerance mechanism that can handle the scenario of a leader's failure due to actuator malfunction. Experimental results show that the AIoT-NFC achieves faster convergence with a lower collision rate. AIoT-NFC reduced the collision rate by 14.99% compared to existing navigation methods in successful formation without colliding with the other UAVs.

Human–Machine Cooperative Decision Making Outperforms Individualism and Autonomy

The experiment reported in this article provides a first experimental evaluation of human–machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision-making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader–follower approaches by comparing them to newly proposed automation designs based on cooperative decision-making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human–machine cooperation. The experiment's results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: It may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human–machine cooperation.

Adaptive obstacle avoidance control strategy for a formation under a narrow alleyway environment

This article develops an effective adaptive obstacle avoidance control strategy containing formation decision mechanism and a model predictive control–based formation controller for a multi-robot system working in a narrow alleyway environment. First, a decision mechanism containing two obstacle avoidance methods, homogeneous deformation and heterogeneous deformation, is established to provide a flexible choice for the formation to pass through the obstacle area, taking into account the formation safety and environment constraint. Furthermore, a formation bank is preset, and a performance evaluation system containing structural deformation, convergence speed, and energy consumption is established to help determine the best obstacle avoidance configuration. Then, a model predictive control–based formation controller with the leader–follower approach is utilized to realize the configuration switching and keeping. In the end, the simulation results verify the effectiveness of the proposed control strategy.

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Tractor-Robot Cooperation: A Heterogeneous Leader-Follower Approach

In this paper, we investigated the idea of including mobile robots as complementary machinery to tractors in an agricultural context. The main idea is not to replace the human farmer, but to augment his/her capabilities by deploying mobile robots as assistants in field operations. The scheme is based on a leader–follower approach. The manned tractor is used as a leader, which will be taken as a reference point for a follower. The follower then takes the position of the leader as a target, and follows it in an autonomous manner. This will allow the farmer to multiply the working width by the number of mobile robots deployed during field operations. In this paper, we present a detailed description of the system, the theoretical aspect that allows the robot to autonomously follow the tractor, in addition to the different experimental steps that allowed us to test the system in the field to assess the robustness of the proposed scheme.

Competitive Relief Supply Chain under the Uncertain Conditions

Relief operations and planning to implement them are essential due to the unpredictability of natural disaster occurrences and their concomitant damages. The Crisis and Disaster Management Organization’s responsibility is to execute plans, coordinate, control relief, and rescue operations to reduce the impacts and consequences of these disasters. Thus, this organization should attend to the struck regions’ needs with their maximum power as soon as possible. This study has initially reviewed the latest available literature regarding the humanitarian supply chain to determine the research gap. Accordingly, in this research, a mathematical model with a leader-follower approach for relief delivery in the crisis response phase was designed, which took into account the policies of the country of Iran for the distribution of relief items. The amount of inventory of suppliers was also considered as uncertain. Then, to validate it, a numerical example was solved by the metaheuristic algorithm MOEA/D. The results indicated that the designed model is valid and the selected algorithm to solve it has an acceptable performance. Finally, some effective parameters were selected in the model and the sensitivity of the model was evaluated based on their changes.

Force-torque (F-T) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise F-T sensor

This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT.

Multirobot System to Explore Unknown Environment with Connection Maintenance

This paper addresses the problem of exploring an unknown environment using multiple robots and maintaining the communication among the team of robots. The existing exploration methods assume that the communication is maintained among the team of robots throughout the exploration process. However, this is not possible when the area to be explored is very large and when there is limited communication range. The presented exploration methods provide communication maintenance among the team of robots and maintain coordination. Three different approaches are presented for exploration. They are centralized approach, leader-follower approach, and ad hoc approach. The concept of centralized approach is developed where the base station is connected to all the robots in the team. The concept of leader-follower approach is developed, where among the team of robots, a leader will be selected. The concept of ad hoc approach is developed in which even though the robots are not within the communication range of one another, communication will occur through ad hoc network. The performance evaluation is done for the above three approaches based on the path length, exploration time, and total number of cycles required to explore the complete area. Systematic approach is used to examine the effect of influence of the exploration parameters like the number of robots, communication range of a robot, and sensor range of a robot on the performance metrics. It is investigated that when there is increase in the exploration parameters, then the exploration time is reduced.

Social Profit Optimization With Demand Response Management in Electricity Market: A Multi-Timescale Leader-Following Approach

In the electricity market, it is quite common that the market participants make “selfish” strategies to harvest the maximum profits for themselves, which may cause the social benefit loss and impair the sustainability of the market in the long term. Regarding this issue, we will study how the social profit can be improved through strategic demand response management. Specifically, we explore two interaction mechanisms in the market: Nash game and Stackelberg game. At the user side, each user makes the respective energy-purchasing strategy to optimize its own profit. At the utility company (UC) side, we consider multiple self-centric UCs that play games. A social-centric governmental UC is established as the leader to optimize the social profit of the market through competition. Then, a multi-timescale leader-following problem of the UCs is formulated under the coordination of an independent system operator. By our proposed demand function amelioration strategy, the market efficiency is maximized. In addition, by considering some additional constraints of the market, two projection-based algorithms are proposed. The feasibility of the proposed algorithms is verified with an IEEE 9-bus system model in the simulation.

Exploration of Multiple Unknown Areas by Swarm of Robots Utilizing Virtual-Region-Based Splitting and Merging Technique

This paper describes an efficacious virtual-region-based shape control scheme for exploring an unknown occluded dynamic environment, likely to have multiple targets, by a swarm of robots. The traditional leader-follower strategy has been intertwined with the shape control technique to achieve group-splitting of the robotic swarm in multi-target or multi-path scenarios. If multiple passages are detected by the parent swarm during the navigational process, several sub-swarms with proportionate agents will be created for progressing through these separate paths. The splitting philosophy is dependent on the fitment of the virtual elliptical regions in each of the paths found ahead, which will guide their respective sub-swarms to navigate further. This work, subsequently, conceives a realistic condition where two or more pathways merge to form a single lane, leading to a unique target. In such situations, the sub-swarms (operating in those paths) rejoin and proceed to the goal as a unified unit. Therefore, in this work, two different features of a swarm robotics framework, namely splitting and merging, have been studied. The scalability control plan ensures strict cohesiveness amongst the agents (inside the mother swarm or any sub-swarm) during the exploration steps. To substantiate the efficacy of the proposed technique, simulation results along with hardware experiment are duly furnished in this article. Note to Practitioners—In recent years, autonomous robots have played an increasingly significant role in civil applications. The search and rescue operation is one such example. In this specific field, swarm robotic systems have the potential to significantly improve efficiency with faster search and rescue of victims, initial assessment and mapping of the environment, real-time monitoring, and surveillance operations. Motivated from this specification, in this work, we offer a novel trajectory planning technique for a robotic swarm employing the region-based shape control scheme with the leader-follower approach for achieving the splitting and merging. The proposed solution can be used in any kind of environmental circumstance, such as single or multiple targets, single or multiple pathways, and so on, such as a normal rescue operation in which several victims may be found in different locations. Moreover, to undertake complete surveillance, it would be cost-effective to deploy one large swarm and then split it into sub-swarms based on the requirements.