Experiments on Human-in-the-Loop Coordination for Multirobot System With Task Abstraction

In the last decade, robotic systems have penetrated human life more than human can imagine. In particular, the multi-mobile robotic systems have faced a fast growing due to the fact that by deploying a large collection of mobile robots the overall system has a high redundancy and offers the capability of handling more complex tasks. A group of mobile robots increases robustness against failures and provide flexibility to system changes. In order to achieve a fully autonomous operation, the control algorithm to coordinate the robots becomes more important and decisive. The controller has to meet different requirements that sometimes are conflicting. The main goal of this thesis is to evaluate performance of coordination control algorithms for a group of mobile robots. In the first part of this thesis, a modular framework for simulation and experiments of coordination control of mobile robots is presented. All connections between the modules follow the subscriber/publisher paradigm on the exchanged data, i.e. a component publishes data and other components can subscribe on that data. The data is identified by a named magazine, and actual data packets are called issues of the magazine. By means of simulation and experiments, it is demonstrated how the modularity in the framework allows straightforward modification of system configurations, design parameters, and control algorithms. In the second part, hierarchical coordination control of mobile robots is presented. The hierarchy consists of three layers: a high-level control for motion planning of the robots, a low-level control for motion execution, and a flexible layer to accommodate shifting of responsibilities. One advantage of using a hierarchical approach is the isolation of control design in each layer. Changes in the control strategy of a layer do not necessarily require adaptation of other layers. The proposed control algorithms are used to coordinate a group of unicycle mobile robots that realize the transportation system of an automated warehouse. Using the framework, series of simulations with different design parameters are conducted, as well as real-time experiments. Subsequently, performance analysis of the results is carried out. It is shown that the proposed algorithms are flexible to system changes and scalable to variation in the warehouse demands. The algorithms are robust against failures in the transportation systems. It is found that an algorithm, denoted high-level control, with higher throughputs requires more information sharing between the robots. This algorithm is less robust against failures compared to the algorithm, denoted low-level control, that yields lower throughput. In addition, a cost analysis of the proposed control algorithms is given. It is studied that the cost of realizing the transport using a group of mobile robots, coordinated by the proposed control algorithm, has similar performance-to-cost ratio compared to conveyor systems. This conclusion is very promising having in minds that the proposed control algorithm is not necessarily the optimal one. In the third part, the problem of simultaneous tracking of individual references and formation keeping for a group of mobile robots is investigated. The control algorithm is developed using dynamic feedback linearization. The stability proof is analyzed using the theorem on interconnected systems. Using a root-mean-square-like indicator, the trade off between individual tracking and formation keeping, as well as the influences of communication topologies are analyzed. From the analysis of real-time experiment results, it is found that the best formation keeping is obtained when all robots communicate. As a trade off, this requires a larger communication bandwidth and yields large individual tracking errors. Furthermore, the analysis suggests that to achieve optimal individual tracking and formation keeping simultaneously, there is less need to share information between the robots. In the fourth part, as a complement to the hierarchical control approach, a coordination control algorithm based on Model Predictive Control (MPC) is presented. Focusing on the practical aspects, a sequentially decentralized MPC, i.e. a single MPC computes the control signals of all robots where priority rules are used to determine which robots handled earlier in optimization procedure, is implemented. Using the similar automated warehouse environment, the sequentially decentralized MPC is validated in simulations and real-time experiments. For comparison purposes, a centralized MPC, i.e. a single MPC computes the control signals of all robots, is also implemented. Using completion time as indicators, the influences of the MPC parameters are investigated. It is found that the centralized MPC is better than the sequentially decentralizwed MPC, but with the cost of high computation load and limited number of robots in real-time application. Relevance and performance comparison between the MPC and the hierarchical control approach are presented. It is found that regardless of the control algorithm choices, in terms of completion time, a better performance is obtained when the control algorithm uses information from all robots in computing the control signals.

This paper shows the prospects and areas of application of groups of mobile robots. The implementation of many practical tasks by groups of robots requires the formation of a specific configuration. Different approaches to solving the formation task in groups of mobile robots are considered. It is shown that the complexity of group’s control increases with the number of robots. Moreover, in large groups of robots, the linear dimensions of the system may exceed the range of direct communication between robots using onboard communication devices. A formal statement of the formation task in a large group of mobile robots is given. The features of the periodic formation consisting of repeating geometric patterns are considered. A new method for the formation of a periodic system in a large group of mobile robots in conditions of limited communications has been proposed. The proposed method is based on the principles of decentralized control and swarm intelligence. The possibility of forming a complex periodic formations and multi-level formations in large groups of mobile robots is considered. The formation of such a system can be used in the tasks of monitoring, mapping, data collection and other applications.

The method to organize a safe and optimal joint movement of a group of intelligent mobile robots that solve common tasks in a potentially dangerous territory is purpose. Such territories include: territory covered by fire, chemically contaminated territory, and another territory with the manifestation of an emergency in which robots will be of high utility. To solve this problem, a software platform for organizing group interactions of mobile objects has been developed and the principles of building its modular architecture are given. Using the platform, the developed technique for solving the problem was implemented and a series of experiments was conducted to evaluate its effectiveness. The technique is based on algorithms for efficiently representing the movement space in the form of a directed graph, finding the shortest path based on the a-star algorithm with an optimal heuristic function, and the logic of joint interaction of a group of mobile robots with the possibility of task distribution. Evaluation of the effectiveness of the developed method was carried out on the basis of a generalized method for calculating the stabilized value of the indication of the effectiveness criterion. A demonstration experiment is the task of simultaneously and safely collecting a group of robots in a target area in a potentially dangerous territory. The main parameter to be evaluated is simultaneity of the arrival of a group of robots depending on the number of robots in a group.

In this paper we study the problem of forming coalitions for dynamic tasks in multirobot systems. As robots, either individually or in groups, encounter new tasks for which individual or group resources do not suffice, robot coalitions that are collectively capable of meeting these requirements need to be formed. We propose a hybrid approach to this problem where coalitions proceed with the task if they have sufficient resources after liberating redundant members while they report it to a task coordinator in cases where their resources do not suffice. In turn, the task coordinator forms capable coalitions based on coalition formation games in which groups of robots are evaluated together in regards to each task’s required resources and cost of forming a coalition. The resulting coalitions are such that no group of robots has a viable alternative to staying within their assigned coalition. Thus, as new tasks are confronted, coalitions merge and split so that the resulting coalitions are capable of the newly encountered tasks. Simulations and experiments performed on groups of heterogeneous mobile robots demonstrate the effectiveness of the proposed approach.

This paper investigates a novel approach for transporting an elastic plate using a group of small swarm mobile robots. A formation of omnidirectional mobile robots transports a plate through an unknown environment purely by normal and friction forces between the robots and the plate. The scheme includes the calculation of normal forces by solving a linear complementary problem and the formation control of swarm robots while they keep the stability of the plate. An improved artificial potential field method is used for path planning and formation control of the mobile robots. The robots are controlled fully distributedly and the proposed scheme is carefully and successfully tested in various simulations posing rather sophisticated challenges both to its navigation and formation control subaspects.

In this article, the problem of performing different tasks with a group of mobile robots is addressed. To cope with issues like regulation to a point or trajectory tracking, a consensus scheme is considered. Three topologies were tested in simulation. The first goal was to make consensus in the group of robots, after the consensus point was relocated to achieve a regulation control. The last objective was to follow a desired trajectory moving the consensus point along the predefined path. The proposal was validated through experimental test with a group of three differential mobile robots.

SUMMARYThis paper considers finite‐time formation control problem for a group of nonholonomic mobile robots. The desired formation trajectory is represented by a virtual dynamic leader whose states are available to only a subset of the followers and the followers have only local interaction. First of all, a continuous distributed finite‐time observer is proposed for each follower to estimate the leader's states in a finite time. Then, a continuous distributed cooperative finite‐time tracking control law is designed for each mobile robot. Rigorous proof shows that the group of mobile robots converge to the desired geometric formation pattern in finite time. At the same time, all the robots can track the desired formation trajectory in finite time. Simulation example illustrates the effectiveness of our method. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents system framework and control algorithm that enable a human operator to simultaneously interact with a group of swarm robots in a remote environment. In this control system, several characteristics of the configuration of the swarm robots are encoded as task functions, for which a human operator can specify desired values that are conveyed to the end-effector of the master robot. Stability and tracking performance of the proposed control system are investigated in the presence of communication delays so that the swarm robots can be manipulated remotely. Moreover, the swarm robots, which perform like a redundant robotic system, can also regulate their position to achieve secondary tasks autonomously. The proposed control algorithms are validated via numerical simulations on a 3-DOF robot manipulator with a group of mobile robots.

The article is devoted to the problem of long-term monitoring of vast territories and water areas by groups of mobile robots in order to timely prevent dangerous consequences of man-made or natural accidents. A group of robots starts from the base and begins collecting data from several work areas. For this, the group must be divided into subgroups according to the number of work areas. Some of the group’s robots remain in reserve at the base. When a robot runs out of energy, it can be replaced by another robot of the subgroup, or a robot from the reserve. In this paper, we propose a method and algorithms for the distribution of robots in a group into subgroups based on the multi-agent interaction of agents in working areas and agents of mobile robots. The differences between homogeneous and heterogeneous groups of robots are taken into account. The results of experimental studies are presented.KeywordsGroup of robotsDistributed computingFog roboticsMulti-agent technologies

One of the problems of group control, the distribution, of tasks in a group of mobile robots, is considered. The basic solution algorithm is proposed to use the “swarm intelligence” implemented on the basis of ant algorithms. This article is a development of the previous authors’ work, which reviewed the known approaches to the distribution of problems using swarm algorithms, described the ant algorithm in detail, and analyzed the resulting solution to the problem of single-criteria optimization. The problem statement, the working space model are presented, the robot function objectives and parameters characterizing their work are formalized. To solve the multi-criteria problem, the following quality criteria were selected: The amount of energy expended, the time it took to complete the tasks, and the number of robots involved. For multi-criteria optimization, the resulting vector optimality criterion is linearly converged by introducing additional parameters characterizing group control: The total efficiency of a group of robots, the specific amount of energy for the operation of the support group and the energy for moving one robot to the given coordinates. To implement the ant algorithm, the problem was presented in the form of a set of undirected weighted graphs on which the “ants” will build solutions. An example is given that shows a detailed transition from the initial problem setting to graphs. The experiments were carried out on the basis of a software-implemented algorithm for finding the optimal group behavior strategy for a multi-criteria objective function with weight coefficients. For a group of three robots, experimental data were obtained and the results analysed, and a solution was modeled for several groups of robots with different weights.KeywordsTask distributionGroup of robotsSwarm intelligenceAnt algorithm

Energy is the critical resource of most living mechanisms. Recent research in robotics has been mostly considered in behavioural autonomy rather than in energy autonomy. This chapter presents our study in “randomized robot trophallaxis”. The chapter consists of three main parts: modeling, simulation, and implementation. In the first section, we model energy trophallaxis in multi-robot system through probabilistic modelling. Deterministic modelling of large groups of interacting mobile robots leads to highly complex nonlinear hybrid models, likely to be highly sensitive to preconditions, i.e. chaotic. Thus, any imprecision in pre-conditions would turn results from such a model useless even for moderate time horizons. However, chaotic systems often exhibit smooth ergodic properties, i.e. time averages have limit values independent of initial conditions and only smoothly dependent on model parameters, etc. Randomness and ergodic properties may exist naturally in such systems or even be intentionally enforced by introducing inherent uncertainty/randomization into the behaviour of individual robots in order to prevent non productive cyclic behaviour such as deadlock or livelocks. Ergodic properties and randomness calls for probabilistic modelling. We propose a combined probabilistic model covering energy exchange between robots, energy consumption in individual robots, charging at predefined charging stations and finally random mobility, where the latter comes in the shape of highly versatile Markovian mobility model. Stationary results furnish overall system performability analysis, such as the impact of individual behaviour on overall system survivability. The section presents the proposed model and comprises central parts of model development as well as illustrative numerical results. In the second section, we simulate aspects of energy autonomy inspired by natural phenomena of animal behaviour. Trophallaxis is a natural phenomenon, biologically observed from social insects or vertebrate animals, to exchange food between colony members. This section describes the concept, “Randomized Robot Trophallaxis”, based on a group of autonomous mobile robots with capabilities of self-refueling energy and selfsharing energy. We firstly clarify the concept “Randomized Robot Trophallaxis” by given examples of natural animal societies. Secondly, we examine the concept by simulation results in order to point out considerable advantages of trophallactic features when deploying multiple mobile robots. The section is concluded with discussion of “randomization” and its appearances in multi-robot system. O pe n A cc es s D at ab as e w w w .in te hw eb .c om

Multi-Robot system became an important research area in the Robotics and Artificial Intelligence. In the presence of obstacles, uncertainty and incomplete information Multi-Robot system is used to accomplish the task in more efficient way as compared to single robot system. Navigation of robots in its surrounding is essential to avoid unacceptable situation such as avoiding obstacles, trajectory planning. Cooperative behavior of multi-robots provides efficient way to avoid collision. It is a matter of concern that how a group of mobile robots should be controlled so that they can move in a cohesive way toward a single direction. For the problem of cooperation among robots, flocking strategy is a good solution. To learn cooperation among robots, various machine learning strategies have been developed. One of the machine learning techniques is called reinforcement learning. It is a very challenging issue in the area of robotics and artificial intelligence. Combining cooperative and learning strategy collision avoidance can be improved. Survey of multi-agent reinforcement learning and flocking control is presented in this paper.

Coordination between mobile robots is a mature research topic. Yet, different approaches and new idea in improving stability condition emerge. Other aspect is on the practicability of solution for over actuated mobile robot type. In this work we propose a practical control algorithm that coordinates tracking of the reference trajectories for a group of four-wheels omnidirectional mobile robots. Exploiting the kinematic property of the robot, coordination can be achieved by a combination of inverse based individual trajectory control and information sharing between robots. A globally asymptotically stable error dynamics is proven using Lyapunov approach. A simulation validation shows that the proposed controller can achieved tracking accuracy up to 0.02 [m] and responsive to disturbances occurred in the group.

The coordinated motion of a group of autonomous mobile robots for the achievement of a goal has been of high interest in the last decade. Previous research has revealed that one of the essential problems in the area is to avoid collision of the robots with obstacles and each other while they still achieve the goal. In this work, we develop a novel coordination scheme for a group of mobile robots and a new online collision avoidance algorithm. Several coordinated tasks have been presented and the proposed scheme is verified by simulations.

In this work the method of communication and maintenance of integrity of data in the decentralized network of autonomous mobile robots is suggested. The software and hardware feasibility of such method is proposed. For collective storage and maintain data integrity in decentralized group of mobile robots, the algorithm of checksum calculations is proposed. The local voting protocol is used for load balancing of mobile robots. For the programming implementation of the data communication, multi-agent technology is used. An ultra light, unmanned aerial vehicle (UAV) is considered as the mobile robot.