Leader Robot Research Articles

Restoring Connectivity in Robotic Swarms – A Probabilistic Approach

Connectivity is an integral trait for swarm robotic systems to enable effective collaboration between the robots in the swarm. However, connectivity can be lost due to events that could not have been a priori accounted for. This paper presents a novel probabilistic connectivity-restoration strategy for swarms with limited communication capabilities. Namely, it is assumed that the swarm comprises a group of follower robots whose global connectivity to a base can only be achieved via a localized leader robot. In this context, the proposed strategy incrementally restores swarm connectivity by searching for the lost robots in regions-of-interest (RoIs) determined using probability theory. Once detected, newly found robots are either recruited to help the leader in the restoration process, or directly guided to their respective destinations through accurate localization and corrective motion commands. The proposed swarm-connectivity strategy, thus, comprises the following three stages: (i) identifying a discrete set of optimal RoIs, (ii) visitation of these RoIs, by the leader robot, via an optimal inter-region search path, and (iii) searching for lost robots within the individual RoIs via an optimal intra-region search path. The strategy is novel in its use of a probabilistic approach to guide the leader robot’s search as well as the potential recruitment of detected lost robots to help in the restoration process. The effectiveness of the proposed probabilistic swarm connectivity-restoration strategy is represented, herein, through a detailed simulated experiment. The significant efficiency of the strategy is also illustrated numerically via a comparison to a competing random-walk based method.

Mapping for stable bilateral teleoperation of manipulators considering time delays

Teleoperation of manipulators has allowed extending the human skills to several areas, where the commands generated by a human operator on the leader robot are followed by a remote robot. For this purpose, position-position tracking requires a mapping given the mismatch between robot workspaces. This paper compares a pair of functions for mapping the haptic device commands to handle a remote robot, considering non-identical workspaces and a classic P+d control scheme. As part of the theoretical analysis, a Lyapunov-Krasovskii-based stability analysis is developed in steps, allowing us for determining the boundedness of velocities and coordination errors. Subsequently, the performance of the teleoperation system is compared using two mapping functions. To achieve a fair comparison of results, a test protocol is developed, where different levels of haptic device damping, time delays, and workspace scales are used to evaluate the time to complete the task and the average coordination errors. Based on the results, the mapping function influences the calculation of force feedback, stability of the system, and performance metrics. During a pick-and-place task, a reduction in coordination error is observed when the linear function is chosen, while the time to complete the task is lower when a non-linear function is considered.

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.

Active self-touch restores bodily proprioceptive spatial awareness following disruption by 'rubber hand illusion'.

Bodily self-awareness relies on a constant integration of visual, tactile, proprioceptive, and motor signals. In the 'rubber hand illusion' (RHI), conflicting visuo-tactile stimuli lead to changes in self-awareness. It remains unclear whether other, somatic signals could compensate for the alterations in self-awareness caused by visual information about the body. Here, we used the RHI in combination with robot-mediated self-touch to systematically investigate the role of tactile, proprioceptive and motor signals in maintaining and restoring bodily self-awareness. Participants moved the handle of a leader robot with their right hand and simultaneously received corresponding tactile feedback on their left hand from a follower robot. This self-touch stimulation was performed either before or after the induction of a classical RHI. Across three experiments, active self-touch delivered after-but not before-the RHI, significantly reduced the proprioceptive drift caused by RHI, supporting a restorative role of active self-touch on bodily self-awareness. The effect was not present during involuntary self-touch. Unimodal control conditions confirmed that both tactile and motor components of self-touch were necessary to restore bodily self-awareness. We hypothesize that active self-touch transiently boosts the precision of proprioceptive representation of the touched body part, thus counteracting the visual capture effects that underlie the RHI.

State observer-based Physics-Informed Machine Learning for leader-following tracking control of mobile robot

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment.

Leader-follower formation control based on non-inertial frames for non-holonomic mobile robots.

A chain formation strategy based on mobile frames for a set of n differential drive mobile robots is presented. Considering two consecutive robots in the formation, robots Ri and Ri+1. It is intended that robot Ri+1 follows the delayed trajectory, τ units of time, of the leader robot Ri. In this way, the follower robot Ri+1 becomes the leader robot for robot Ri+ 2 in the formation and so on. With this formation policy, the trailing distance between two consecutive robots varies accordingly to the velocity of the Ri leader robot. Mobile frames are located on the body of the vehicles, in such a way that the position of robot Ri is determined with respect to the frame located on Ri+1 robot. The strategy relies on the fact that the general leader robot R1 describes any trajectory generated by bounded linear v1(t) and angular ω1(t) velocities. For the remaining vehicles in the string, the strategy considers a desired trajectory for the follower robot Ri+1 obtained by an estimation of the delayed trajectory of the leader robot Ri. This desired estimated trajectory is obtained under the knowledge of the actual and past input velocities of the Ri robot. To formally prove the convergence of the formation strategy, the equations describing the time variation of the relative posture between any pair of consecutive vehicles in the formation are obtained, and a feedback law based on local measurements is proposed to get the convergence of robot Ri+1 to the delayed trajectory, τ units of time, of the trajectory previously described by robot Ri. Lyapunov techniques are considered for this fact. The effectiveness of the chain formation solution is evaluated by means of numerical simulations and real time experiments showing an adequate convergence.

Game-Based Approximate Optimal Motion Planning for Safe Human-Swarm Interaction.

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.

A Leader-follower formation control of mobile robots by position-based visual servo method using fisheye camera

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

Future Service Robot Scenarios in South Korea

Advances in digital technology, periodic threats from infectious diseases, and shrinking working-age populations have increased the demand for autonomous systems. South Korea is now in crisis because its society is aging and has limited resources. The implementation of service robots is one of the possible alternative plans that has been receiving attention both for sustainable economic growth and as a solution to social problems. However, many things should be considered for service robots to be widely used in society. The aim of this study was to identify key factors that will affect the future of service robots and discuss corresponding policy measures. Four scenarios were developed using general morphology analysis (GMA). The scenarios were defined according to six key factors: technological development, infrastructure development, commercial acceptance, social acceptance, policy and regulatory environments, and technological competition. In scenario A, policy measures need to ensure that South Korea will continue as a global service robot leader. In scenario B, it is necessary to narrow the gap between South Korea and competitors in terms of service robot technology development and adoption. In scenario C-1, policies should encourage the adoption of service robot technologies both domestically and abroad. In scenario C-2, it is necessary to develop service robot technologies and promote the service robot industry.

The Robust Adaptive Control of Leader–Follower Formation in Mobile Robots with Dynamic Obstacle Avoidance

In this paper, the problem of formation control with regard to leader–follower mobile robots in the presence of disturbances and model uncertainties, without needing to know the velocity of the leader robot, is presented. For this purpose, at first, a first-order kinematic model of leader–follower and leader–leader formations is obtained, and considering the absolute velocity of the leader robots as an uncertainty, a robust adaptive controller is designed to keep the desired formation. In this case, the upper bound of uncertainty is unknown and is obtained via stable adaptive laws. Afterwards, in order to deal with the accelerated robots and obstacles, second-order leader–follower and leader–leader formation models are obtained from the previous models. A robust adaptive controller is then designed to stabilize the entire system in the presence of disturbances and modeling uncertainties, without needing to know the parameters or matrices of the formation models. In addition, by considering one of the leaders in the leader–leader model as a virtual obstacle, the challenge of avoiding moving obstacles is also addressed in the presence of uncertainties. The simulation results show the effect of the presented controllers in effectively keeping the desired leader–follower formations.

Leader-Follower Based Formation Control of Heterogeneous UAV-AGV Multi-Agent System

This paper deals with a leader-follower formation control of a heterogenous robot swarm. The heterogeneous swarm consists of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The ground robot is the leader robot, and the drones are the followers. A centralized system receives the information about the robots, and the decision for the robots is made from there. The robots create the V formation shape and are assigned to the formation points using the Hungarian algorithm. The robots go to the formation points with a proportional controller. The system was developed using the ROS2 framework. Turtlebot3 and Crazyflie robots were used for the robot swarm. The study was tested in Webots simulation environment. Different tests were performed, and the results were observed.

Towards Multi-AUV Collaboration and Coordination: A Gesture-Based Multi-AUV Hierarchical Language and a Language Framework Comparison System

The underwater environment is a harmful environment, yet one of the richest and least exploited. For these reasons the idea of a robotic companion with the task of supporting and monitoring divers during their activities and operations has been proposed. However, the idea of a platoon of robots at the diver’s disposal has never been fully addressed in these proposals due to the high cost of implementation and the usability, weight and bulk of the robots. Nevertheless, recent advancements in swarm robotics, materials engineering, deep learning, and the decreasing cost of autonomous underwater vehicles (AUVs), have rendered this concept increasingly viable. Therefore, this paper introduces, in the first part, a novel framework that integrates a revised version of a gesture-based language for underwater human–robot interaction (Caddian) based on insights gained from extensive field trials. The newly introduced objective of this framework is to enable the cooperation and coordination of an AUV team by one or more human operators, while allowing a human operator to delegate a robot leader to instruct the other robotic team members. The work, in the second part, provides an evaluation of the new language proposed thanks to a fifty million sentence corpus and describes a comparison framework, which is used to estimate it with respect to other existing underwater human–robot interaction languages.

Decentralized navigation method for swarm robots with individual loss due to failure

This paper presents a decentralized navigation method with a leader robot guiding a robotic swarm and considers the possible occurrence of a failure. Multi-robot systems have attracted attention in part because they can maintain their performance as a swarm even when some robots fail. Such systems are also expected to work well in environments where a disaster has occurred, given that the chance of robot crashes is high in such situations. Therefore, it is important to design a navigation method that considers the possibility of failures so that the surviving robots in a swarm will not be impaired because of failed robots. In this paper, we design connectivity maintenance and a repair method for swarm robots in which each robot utilizes only local information. We then prove that the proposed method ensures the swarm follows the movement of a leader robot and reconstructs connectivity in the case one of the robots fails. Finally, we present experimental and numerical results confirming the effectiveness of the proposed method.

Social competence improves the performance of biomimetic robots leading live fish

Collective motion is commonly modeled with static interaction rules between agents. Substantial empirical evidence indicates, however, that animals may adapt their interaction rules depending on a variety of factors and social contexts. Here, we hypothesized that leadership performance is linked to the leader’s responsiveness to the follower’s actions and we predicted that a leader is followed longer if it adapts to the follower’s avoidance movements. We tested this prediction with live guppies that interacted with a biomimetic robotic fish programmed to act as a ‘socially competent’ leader. Fish that were avoiding the robot were approached more carefully in future approaches. In two separate experiments we then asked how the leadership performance of the socially competent robot leader differed to that of a robot leader that either approached all fish in the same, non-responsive, way or one that did change its approach behavior randomly, irrespective of the fish’s actions. We found that (1) behavioral variability itself appears attractive and that socially competent robots are better leaders which (2) require fewer approach attempts to (3) elicit longer average following behavior than non-competent agents. This work provides evidence that social responsiveness to avoidance reactions plays a role in the social dynamics of guppies. We showcase how social responsiveness can be modeled and tested directly embedded in a living animal model using adaptive, interactive robots.

Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.

Formation Control of Non-Holonomic Mobile Robots: Predictive Data-Driven Fuzzy Compensator

A key research topic in the field of robotics is the formation control of a group of robots in trajectory tracking problems. Using organized robots has many advantages over using them individually, such as efficient use of resources, increased reliability due to cooperation, and better resistance against defects. To achieve this, a controller is proposed that steers the leader robot and subsequent follower robots asymptotically to a reference trajectory. The basic controller is feedback linearization. To ensure stability against perturbations, a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy is designed. The approach involves employing a finite number of open-loop data and using the model-based predictive controller (MPC) approach to acquire sufficient criteria for stability. An infinite-horizon function is minimized online, which allows the data-based control policy to be considered the optimal control method. The gains of the constrained data-based control signal are computed at each time step to enhance accuracy. Applying the data-based state feedback controller to the system yields positive and stable state trajectories with appropriate transient responses. The suggested data-driven compensator is guaranteed to handle constraints. A practical example is simulated to evaluate the proposed strategy.

UWB-IMU-Odometer Fusion Localization Scheme: Observability Analysis and Experiments

In this article, we present a novel ultrawideband (UWB)-inertial measurement unit (IMU)-odometer fusion localization scheme for nonholonomic ground robots in global positioning system (GPS)-denied environments. To overcome the severe drift problems caused by the large acceleration bias of low-cost IMUs, rather than using the conventional IMU-only propagation model, a wheel odometer and a three-axis gyroscope are integrated to propagate the system states. Furthermore, the observability conditions of the proposed system with nonholonomic constraints are theoretically derived by a nonlinear observability analysis. The results reveal the minimum number of anchors (or leader robots) required for position observability (at least three anchors for UWB-time of arrival (ToA) measurements and one anchor for both ToA and angle-of-arrival (AOA) measurements). In addition, the system inputs (linear velocity and angular velocity along different axes) need to be excited for attitude observability. Simulations and experiments have verified that the proposed approach produces accurate position estimation and outperforms previous methods. Meanwhile, the position and attitude observability conditions have been verified through rich experiments, and the degenerated cases where the states cannot be observed are enumerated and tested, making the scheme complete.

Autonomous Leader–Follower Formation of Vehicular Robots Using the Lyapunov Method

This paper focuses on the control of two self-driven vehicles (leader–follower) in a multi-obstacle environment, while maintaining formation. The acceleration-based control input design governs the overall movement and control of the rovers. This is accomplished through the application of APF functions that support the leader robot to reach the desired target while avoiding obstacles and maintaining formation. The Lyapunov theorem was used for the control design of the leader and follower vehicles. An effective mathematical model was designed and run through the MATLAB software for simulation verification. The simulation results obtained illustrate the behavior of the leader–follower vehicles with respect to the controllers designed. Therefore, this paper looks at the efficiency of the vehicles to converge at a predefined target, from random points in a predefined workspace, while avoiding fixed and moving obstacles. The technique may be applied in transportation and defense sectors where environments are a risk prone to human health or safety.

Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control

Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration.

Cooperative Following of Multiple Autonomous Robots Based on Consensus Estimation

When performing a specific task, a Multi-Agent System (MAS) not only needs to coordinate the whole formation but also needs to coordinate the dynamic relationship among all the agents, which means judging and adjusting their positions in the formation according to their location, velocity, surrounding obstacles and other information to accomplish specific tasks. This paper devises an integral separation feedback method for a single-agent control with a developed robot motion model; then, an enhanced strategy incorporating the dynamic information of the leader robot is proposed for further improvement. On this basis, a method of combining second-order formation control with path planning is proposed for multiple-agents following control, which uses the system dynamic of one agent and the Laplacian matrix to generate the consensus protocol. Due to a second-order consensus, the agents exchange information according to a pre-specified communication digraph and keep in a certain following formation. Moreover, an improved path planning method using an artificial potential field is developed to guide the MAS to reach the destination and avoid collisions. The effectiveness of the proposed approach is verified with simulation results in different scenarios.