Leader-follower Strategy Research Articles

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

Adaptive immune fuzzy quasi-sliding mode control for leader–follower formation of wheeled mobile robots under uncertainties and disturbances with obstacle avoidance

PurposeThis paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.Design/methodology/approachTo deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.FindingsTo verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.Originality/valueThe proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

A varying-parameter complementary neural network for multi-robot tracking and formation via model predictive control

In this paper, a varying-parameter complementary neural network (VPCNN) is designed and combined with model predictive control (MPC) to solve the multi-robot tracking and formation problems via a leader–follower strategy. First, multi-robot tracking and formation problems are transformed into quadratic programming (QP) problems employing an MPC approach. Second, a nonlinear complementary function approach, i.e., the Fischer–Burmeister function, is used to map the Karush–Kuhn–Tucker conditions of the QP problem with double-ended inequality constraints to a system of nonlinear equations. Finally, the VPCNN is designed to solve the multi-robot tracking and formation problem. The effectiveness of the proposed method is demonstrated by numerical simulations, and the advantage of VPCNN in terms of solution speed is indicated by comparisons with a primal–dual neural network.

Wireless Communication-aware Path Planning and Multiple Robot Navigation Strategies for Assisted Inspections

Among the many challenges robots encounter in the mining industry, exploring confined environments receives significant attention. This work tackles problems associated with robot communication in hazardous and confined environments, where its cluttered and extensive nature frequently precludes traditional cable-based and wireless solutions. Our methods resort to off-the-shelf long-range radio frequencies to profile the signal propagation behaviour over the geometrical map to assist navigation algorithms that seek to preserve the connection. We consider mathematical models to predict signal power behaviour and serve as input to path planning. We also propose a semi-autonomous leader-follower scheme, with signal repeater units forming a mobile wireless network to enable inspection in hard-to-reach locations. Finally, we present a multi-robot connection-aware system, combining path planning based on radio signal power with multiple robot navigation. Results show the applicability of the proposed solutions, generating single and multi-robot paths for optimal signal reception based on power estimation, thus enabling operations in remote and isolated areas with no line-of-sight between the command base and the robotic inspection device. Experiments conducted in long corridors and in a representative mining environment using the EspeleoRobô and Pioneer platforms demonstrate significant improvements over the traditional communication methods for robotic operation regarding communication quality and inspection range limits.

Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.

Trajectory Planning for Cooperative Double Unmanned Surface Vehicles Connected with a Floating Rope for Floating Garbage Cleaning

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

Two-player nonlinear Stackelberg differential game via off-policy integral reinforcement learning

For the nonzero-sum games, players have taken different strategies to achieve the Nash equilibrium. However, regarding hierarchical optimization and asymmetric information, Nash equilibrium is ineffective. The Stackelberg game provides us with a leader–follower strategy to cope with this problem. This paper solved two-player continuous-time nonlinear Stackelberg differential games with unknown system dynamics using an off-policy integral reinforcement learning (IRL) technique. A two-level hierarchy optimal control problem is the Stackelberg differential game, and locating the open-loop Stackelberg equilibrium is equivalent to solving the coupled Hamiltonian-Jacobi (HJ) equations. First, the optimal strategies for the leader and the follower are derived. Then, the closed-loop system’s asymptotic stability of optimal control solution is proved. Based on the policy iteration (PI) algorithm, an off-policy IRL algorithm is developed to solve the Stackelberg game for completely unknown system dynamics. The control strategies and cost functions of the leader and follower are approximated using neural networks (NNs). It is demonstrated that NNs weight errors of off-policy algorithm are uniformly ultimately bounded (UUB). Lastly, two simulation examples demonstrate that the proposed learning algorithm is capable of obtaining two-player Stackelberg equilibrium strategies.

Time-Varying Topology Formation Reconfiguration Control of the Multi-Agent System Based on the Improved Hungarian Algorithm

Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system’s stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment.

Autonomous Formation Flight Control of Large-Sized Flapping-Wing Flying Robots Based on Leader–Follower Strategy

Autonomous Formation Flight Control of Large-Sized Flapping-Wing Flying Robots Based on Leader–Follower Strategy

Least global position information based control of fixed-wing UAVs formation flight: Flight tests and experimental validation

The paper addresses the design of an innovative navigation and control method for the flight control of a formation consisting of small and low-cost fixed-wing UAVs by using a leader-follower strategy and a least global position information sharing algorithm. No additional hardware is needed except the classical global positioning system and autopilot. The novel strategy firstly involves the obtaining of the relative kinematics for the formation of UAVs by using a trajectory coordinate system and a navigation algorithm based on global position coordinates. Secondly, the flight control laws for each UAV are designed by means of the Active Disturbance Rejection Control, the convergence and robustness are discussed and proved, while the flight control laws for the whole formation are derived within a PI controller which balances the robustness and the computation of the whole control architecture. Then, by considering the requirements of positioning accuracy in low-cost constrains, the control scheme is validated both by numerical simulations and experimental flight tests along a racetrack-shaped circular path. The results prove an excellent accuracy for the navigation and control method, the follower UAVs following the trajectory of the leader UAV no matter if the flight is straight or turning type.

Enhanced multi-agent systems formation and obstacle avoidance (EMAFOA) control algorithm

Most of the multi-agent formation and obstacle avoidance algorithms in the literature are computationally expensive and/or presents an ad hoc method for a specific type of systems. A general inexpensive, in term of computational complexity, multi-agent formation and obstacle avoidance algorithm is modeled and designed in this paper. The method builds on the leader-follower strategy for the simplicity and applicability for a wide range of the engineering systems. A novel artificial potential function (APF) is proposed. The proposed function has unique attributes which differentiate it superior to what is reported in the literature. The proposed algorithm can be applied for first order systems, second order systems, and non-holonomic systems. Also, the proposed method is free of local minima problem and oscillations. In addition, a novel multi-agent system formation control design is proposed in this work. The proposed design allows to embed any formation in the system paradigm and reduces complexity. Moreover, the stability of the proposed method is investigated in terms of Riccati equation and Lyapunov method. Furthermore, to show the effectiveness of the proposed method it applied and simulated for a second order leader and one follower system with specific formation. Then, a five second order agents with leader in a circular formation avoiding simple and complex obstacles are also introduced with different scenarios. Finally, twenty agents with square formation with two obstacles are simulated and investigated.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy (IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy. The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

Cooperative Control of Two-Vehicle Transportation Based on Time-Varying Distance Between Vehicles

For two-vehicle cooperative transportation control, the typical methods are to keep the relative heading angle of the two vehicles constant and to realize cooperative transportation by using the characteristics of mecanum wheels. These methods do not apply to the requirements of the highway scene for the trafficability and robustness of two-vehicle transportation. To solve these problems, a cooperative control method based on a time-varying distance model was proposed. First, a multiaxis all-wheel-steering kinematic model was constructed based on the proportional steering principle. The reference rotation angles of each wheel were output by the model predictive control (MPC), which could realize trajectory tracking, with the current vehicle state and longitudinal speed prediction sequence as inputs. Second, the leader–follower strategy was adopted to build a dynamic reference distance of the path-between-vehicles model based on trajectory tracking deviation, vehicle states, and road information. On this basis, a two-vehicle cooperative prediction equation was established, and it output the longitudinal speed of the follower through the MPC. Finally, Simulink/TruckSim cosimulation was used to verify the effectiveness of the cooperative control method, which provides a new control idea for improving the efficiency of bulk transportation. The effectiveness and robustness of the collaborative control strategy were verified by Simulink/TruckSim cosimulation and a real vehicle experiment, which provides a new control idea for improving the efficiency of large cargo transportation.

Sim-real joint experimental verification for an unmanned surface vehicle formation strategy based on multi-agent deterministic policy gradient and line of sight guidance

The formation of multiple Unmanned Surface Vehicles (USVs) is an effective way to extend the capabilities of a single USV to satisfy relatively complex tasks in practice. In this study, we proposed a formation-strategy-based deep reinforcement learning method called Multi-agent Deterministic Policy Gradient (MADDPG) to realize multi-USV formation. In this work, Line of Sight (LOS) guidance is integrated into the formation strategy under a leader-follower scheme. With the advantage of ignoring the dynamic model of the USV, the proposed formation strategy has strong migration potential to be transferred to other multi-agent systems. To evaluate the performance of the multi-USV formation, we designed two different scenarios in line with the practical tasks carried out with the multi-USV system covering observation aperture enhancement with the desired formation and dynamic non-cooperative target roundup. The performance of the proposed multi-USV formation strategy was demonstrated in both a simulation environment and a real-world environment. Compared with other deep reinforcement learning-inspired and traditional approaches, our proposed strategy based on MADDPG achieved a higher task success rate. It also outperformed the Deep Deterministic Policy Gradient (DDPG) in other metrics because it can acquire knowledge more effectively from dynamic environments by observing joint information and from the centralized training.

Leader-follower formation of light-weight UAVs with novel active disturbance rejection control

Considering that the formation composed of light-weight UAVs is highly susceptible to the interference, which may from the changes in the external environment and the uncertainty of system, a formation control method based on the inner and outer loops is proposed. In the inner loop, the single UAV stable flight can be achieved via controlling the state variables of UAV angles. In the outer loop, we can achieve multi-UAVs cooperative flight through the leader-follower strategy. In this paper, we mainly focus on the stability control of each member UAV, which is the basic composition of formation control. For this purpose, an optimized active disturbance rejection control (ADRC) is proposed which combined an improved prescribed-time extended state observer (PTESO) and weight optimization module based on broad learning. In this way, the single UAV can dynamically adjust the control weights according to different wind degrees, so that the influence of environmental changes on the control system is reduced. Then, the effectiveness and superiority of the proposed formation control methods are verified by simulations. The stability enhancement control method proposed in this paper provides a new and effective theoretical support for the actual control of the light-weight UAV formation, which has a well engineering application prospect.

Leader-follower scheme for unicycle-type mobile robots with mounted camera

This paper describes a formation control proposal for unicycle-type mobile robots under the leader-follower scheme with mounted camera. The control objective is determined directly in image space, hence this proposal corresponds with the Image-Based Visual Servoing (IBVS) alternative. Using the Lyapunov stability theory, two controllers are designed: one that depends partially on the parameters of the vision system and another one that is robust to the three-dimensional parameters of the features that are mapped to the image space (the image features). Finally, experiments on a soft real-time platform with satisfactory results are carried out to validate the proposed theory.

Design and Testing of Cooperative Motion Controller for UAV-UGV System

Unmanned ground vehicles (UGVs) and quadrotor unmanned aerial vehicles (UAVs) can work together to solve challenges like intelligent transportation, thanks to their excellent performance complements in perception, loading, and endurance. This study presents a UAV-UGV system cooperative control mechanism. To achieve collaborative trajectory tracking, the leader-follower strategy based on a centralized control structure is firstly established in conjunction with the application scenario. The fuzzy robust controller is created to control the quadrotor UAV and improve attitude stability. Meanwhile, the UGV's controller uses the pure pursuit algorithm and a proportional integral derivative (PID) controller. In order to evaluate the cooperative control strategy and algorithm, the UAV-UGV experimental platform is set up based on the QDrone and QCar, and the experimental results show the viability of the suggested plan.

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.

Terminal sliding-mode disturbance observer-based finite-time adaptive-neural formation control of autonomous surface vessels under output constraints

AbstractThis paper proposes a tracking controller for the formation construction of multiple autonomous surface vessels (ASVs) in the presence of model uncertainties and external disturbances with output constraints. To design a formation control system, the leader-following strategy is adopted for each ASV. A symmetric barrier Lyapunov function (BLF), which advances to infinity when its arguments reach a finite limit, is applied to prevent the state variables from violating constraints. An adaptive-neural technique is employed to compensate uncertain parameters and unmodeled dynamics. To overcome the explosion of differentiation term problem, a first-order filter is proposed to realize the derivative of virtual variables in the dynamic surface control (DSC). To estimate the leader velocity in finite time, a high-gain observer is effectively employed. This approach is adopted to reveal all signals of the closed-loop system which are bounded, and the formation tracking errors are semi-globally finite-time uniformly bounded. The computer simulation results demonstrate the efficacy of this newly proposed formation controller for the autonomous surface vessels.

Distributed predictive formation control for autonomous underwater vehicles under dynamic switching topology

Numerous existing autonomous underwater vehicles (AUVs) formation control works have focused on the preconfigured communication topology. However, the complex marine environment reveals the limitations and unreliability of the fix topology scheme. In this paper, the formation control of AUVs is investigated under dynamic switching topology. In order to realize this objective, the 2-D Delaunay triangulation and Jaccard index are introduced. A formation control system based on intermittent broadcast, rather than the traditional distributed control system, is established based on a leader-follower strategy. During the process of formation moving, the input constraints, vehicle collision avoidance and input saturation needed to be noted. To this end, the model predictive control algorithm is employed to design the formation controller, and the stability of the formation control system is proved by the average residence time notion and Lyapunov stability. The numerical simulations are exhibited to illustrate the effectiveness of the proposed formation control schemes in connectivity preservation and formation generation.