Multi-robot Teams Research Articles

A Routing Framework for Heterogeneous Multi-Robot Teams in Exploration Tasks

This letter proposes a routing framework for heterogeneous multi-robot teams in exploration tasks. The proposed framework deals with a combinatorial optimization problem, and provides a new solving algorithm, for Generalized Team Orienteering Problem (GTOP). In this letter, a route optimization problem is formulated for a heterogeneous multi-robot system. A novel problem solver is also proposed based on self-organizing map. The proposed framework has a strong advantage in its scalability because the processing time is independent from the number of robots, and the heterogeneity of the team. The validity of the proposed framework is evaluated in the exploration, and mapping tasks by heterogeneous robot team with overlapping abilities. The simulation results show the effectiveness of the proposed framework, and how it outperforms the conventional greedy exploration scheme.

How are Your Robot Friends Doing? A Design Exploration of Graphical Techniques Supporting Awareness of Robot Team Members in Teleoperation

While teleoperated robots continue to proliferate in domains including search and rescue, field exploration, or the military, human error remains a primary cause for accidents or mistakes. One challenge is that teleoperating a remote robot is cognitively taxing as the operator needs to understand the robot’s state and monitor all its sensor data. In a multi-robot team, an operator needs to additionally monitor other robots’ progress, states, notifications, errors, and so on to maintain team cohesion. We conducted a design exploration of novel graphical representations of robot team-member state, to support a person controlling one robot to maintain awareness of other robots in the team. Through a series of evaluations, we examined several design parameters (text, icon, facial expression, use of color, animation, and number of team robots), resulting in a set of guidelines for graphically representing team robot states in the remote team teleoperation.

Nonlinear observability of unicycle multi-robot teams subject to nonuniform environmental disturbances

In this work, we consider the problem of localizing a team of robots, without access to direct pose measurements, under the influence of nonuniform environmental disturbances and measurement bias. Specifically, we are interested in the conditions under which teams remain range-only localizable when the environmental disturbances vary from robot to robot. We approach this problem through nonlinear observability and graph theory. After analyzing the system’s observability properties, we present theorems that identify the structural conditions under which the system maintains local weak observability. We demonstrate that rigid structures are important not only in defining multi-robot interactions, but also in characterizing the influence of nonuniform disturbances. We also give several example systems to cement intuition on the derived conditions. An observability-based planner is then presented that guides a subset of robots toward trajectories that are highly observable through finite-horizon optimization on robot headings. Simulations are then presented, along with an extended Kalman filter for state estimation, and a comparison to previous methods, to corroborate and demonstrate the results derived.

An AI‐based virtual simulation experimental teaching system in space engineering education

AbstractIn recent years, cognitive computing has represented an artifical intelligence (AI)‐based application of the next‐generation interaction among human beings. This study analyses the cognitive internet of things and proposes a Soft K‐Means Fuzzy Clustering approach. The paper presents the data collection procedure from the space applications, the data analysis by cognitive computing, and the ways of overcoming problems of scalability and flexibility. The interactions between robot team members and people become necessary for many real‐life applications. This study mainly focuses on the precision and complexity of the model, time of computation, and scalability. Furthermore, it demonstrates the AI's ability to enhance the communication skills of the multirobot team by making complex activities to carry out all tasks more effectively.

Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks

Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this paper, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.

Persistently Excited Adaptive Relative Localization and Time-Varying Formation of Robot Swarms

In this article, we investigate the problem of controlling a multirobot team to follow a leader in formation, supported by a relative position estimate derived from distance and self-displacement measurements, thus waiving the need of external localization infrastructure. The main challenge of the problem, which is to simultaneously fulfill both relative localization and control tasks, is efficiently and novelly resolved by embedding a distance-displacement-based persistently excited adaptive relative localization technique into a time-varying formation with bounded control input (PEARL-TVF). By assuming that the leader is globally reachable and by selecting proper parameters, it is shown that the PEARL-TVF ensures exponentially convergent localization, which leads to exponentially convergent formation when the leader's behavior is deterministic, and bounded formation error for a nondeterministic leader. Numerical simulations and experiments on quadcopters are provided to verify the theoretical findings.

Incorporation of Contingency Tasks in Task Allocation for Multirobot Teams

Complex logistics support missions require the execution of spatially separated information gathering and situational awareness tasks. Mobile robot teams can play an important role in the automated execution of these tasks to reduce mission completion time. Planning strategies for such missions must take into account the formation of effective coalitions among available robots and assignment of tasks to robots with the goal of minimizing the expected mission completion time. The occurrence of unexpected situations that adversely interfere with the execution of the mission may require the execution of contingency tasks so that the originally planned tasks may proceed with minimal disruption. Initially reported potential contingency tasks may not always affect mission tasks due to the uncertainty in the mission environment. When potential contingency tasks are reported, the planner updates its existing plan to minimize the expected mission completion time based on the probability of these contingency tasks impacting the mission, their impact on the mission, and other task characteristics. We describe various heuristic-based strategies to compute task allocations for robots for mission execution. We perform simulation experiments to compare them and analyze the computational performance of the best performing strategy. We show that the proactive approach to contingency task management outperforms both the conservative and reactive approaches. Note to Practitioners —The work reported in this article will be useful for deploying multirobot teams to support complex logistics missions spread over a large area where the robots must be prepared to handle contingencies that can adversely impact the mission. The proposed proactive approach can be used to handle contingencies in information gathering, surveillance, guarding, and situational awareness tasks to support safe and secure transportation of important assets through crowded areas. We use port operation as an illustrative example where unmanned surface and aerial vehicles can be useful in ensuring the safety and security of the ports. This is a computationally challenging problem. This article proposes heuristic algorithms to solve the task allocation problem among many different agents efficiently. The approach presented in this article integrates the available information regarding mission and contingencies, along with the resource constraints to plan the mission execution.

Socially Acceptable Navigation of People with Multi-robot Teams

Socially acceptable navigation is a subject that involves developments regarding Human-Robot Interaction (HRI) and autonomous mobile robot navigation. In this context, there is little research considering people interacting with a robot team, and even fewer considering multi-robot teams with people. In this paper, a study on socially acceptable navigation involving a human and a robot team is presented. Four navigation strategies that consider social aspects are presented and compared in simulated environment by terms as the average number of robots invading the personal space and the number of robots to the person’s side, with two of them using Asymmetric Gaussian Functions (AGFs) as the person’s social zone model; a navigation perception comparison is made involving people to investigate their view on navigating with three robots in contrast to a single robot. Simulated and real-world experiments were performed showing that the proposed methods have advantages over each other on different aspects. The follow-up techniques for interaction between humans and a team of robots suggest that people’s perception may not be affected significantly when interacting with more than one robot.

Active Target Tracking With Self-Triggered Communications in Multi-Robot Teams

We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario, where a team of robots is allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past, which guarantees that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on decentralized strategies to reduce the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to the desired formation when the target is stationary. For the case of a mobile target, we use a decentralized Kalman filter with covariance intersection to share the beliefs of neighboring robots. We evaluate all the approaches through simulations and a proof-of-concept experiment. Note to Practitioners —We study the problem of tracking a target using a team of coordinating robots. Target tracking problems are prevalent in a number of applications, such as co-robots, surveillance, and wildlife monitoring. Coordination between robots typically requires communication amongst them. Most multi-robot coordination algorithms implicitly assume that the robots can communicate at all time steps. Communication can be a considerable source of energy consumption, especially for small robots. Furthermore, communicating at all time steps may be redundant in many settings. With this as motivation, we propose an algorithm where the robots do not necessarily communicate at all times and instead choose specific triggering time instances to share information with their neighbors. Despite the limitation of limited communication, we show that the algorithm converges to the optimal configuration both in theory as well as in simulations.

Interaction Templates for Multi-Robot Systems

This letter describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs), which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios, which previously required a task planning method. We show improved performance over a current TMP planning approach.

Sparsity Structure and Optimality of Multi-Robot Coverage Control

The structure of the Hessian matrix obtained from the locational cost used in coverage control is investigated to provide conditions on the optimality of coverage control solutions. It is shown that in arbitrary dimensions, the Hessian matrix is composed of the direct sum of three well-structured matrices: 1) a diagonal matrix; 2) a block-diagonal matrix; and 3) block-Laplacian matrix. This structure is exploited in the one-dimensional case, where an alternative proof of a sufficient condition for optimality is given. A relationship is shown between centroidal Voronoi tessellation (CVT) configurations and the sufficient condition for optimality via the spatial derivative of the density provided in the cost. A decomposition is used to provide insight into the terms which most affect optimality. Several classes of density functions are analyzed under the proposed condition. Experiments on a multi-robot team are shown to verify theoretical results.

N-learning: An Approach for Learning and Teaching Skills in Multirobot Teams

SummaryWe propose the N-learning practical approach for teaching and learning behaviors in a multirobot system, which is performed through mandatory behavior acquisition based on interactions between the robots at execution time. The proposed methodology can be used to self-program the robots of a team by programming only a single robot with a set of codes that contain behaviors to be transferred and used by other robots as necessary. These codes are implemented in a modular fashion. An advantage of our approach is that when a team of robots is required to perform a specific mission, the set of behaviors required to accomplish that mission can be implemented only once in a single robot or in a distributed fashion. Then, these distributed behaviors are transferred to each of the other robots in the team according to their demand, without the need to reprogram them by hand since the robots in the team can share them autonomously. As an application example, a human critic can teach (or program) only one or a few robots, and these robots are thus able to exchange knowledge with the other team members since they have been preinstalled to run the N-learning system basics. Simulated and real robot experiments are performed to demonstrate the feasibility and validation of our approach.

Scale-Free Vision-Based Aerial Control of a Ground Formation With Hybrid Topology

We present a novel vision-based control method to make a group of ground mobile robots achieve a specified formation shape with unspecified size. Our approach uses multiple aerial control units equipped with downward-facing cameras, each observing a partial subset of the multirobot team. The units compute the control commands from the ground robots’ image projections, using neither calibration nor scene scale information, and transmit them to the robots. The control strategy relies on the calculation of image similarity transformations, and we show it to be asymptotically stable if the overlaps between the subsets of controlled robots satisfy certain conditions. The presence of the supervisory units, which coordinate their motions to guarantee a correct control performance, gives rise to a hybrid system topology. All in all, the proposed system provides relevant practical advantages in simplicity and flexibility. Within the problem of controlling a team shape, our contribution lies in addressing several simultaneous challenges: the controller needs only partial information of the robotic group, does not use distance measurements or global reference frames, is designed for unicycle agents, and can accommodate topology changes. We present illustrative simulation results.

Survey on artificial intelligence based techniques for emerging robotic communication

This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The research of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently. We present a comprehensive review of the intelligent solutions for robot communication which have been proposed in the literature in recent years. This survey contributes to a better understanding of the AI techniques for enhancing robot communication and sheds new lights on future research direction in the subject area.

THE CRITERION FOR FEATURE INFORMATIVENESS ESTIMATION IN MULTI ROBOT TEAMS CONTROL

Context. The task of automation of feature set informativeness estimation process in multi robot teams control is solved. The object of the research is the process of multi robot teams control. The subject of the research is the criterion of feature set informativeness estimation. Objective. The research objective is to develop the criterion for feature set informativeness estimation in multi robot teams control. Method. The criterion for feature set informativeness estimation is proposed. The developed criterion is based on the idea that feature set informativeness is computed according to values of the prior probabilitіes of finding features in the descriptions of the environment states. The use of the proposed criterion allows to efficiently solve the problem of feature set informativeness estimation, leading to effective solution of the multi robots control task. The developed criterion is based on the maximizing mutual information criterion and can be applicable when measurements are interdepended and environment has a variable number of states. The criterion doesn’t require to construct models based on the estimated feature combinations, in such a way considerably reducing time and computing costs for multi robot teams control. Application of the proposed criterion for feature set informativeness estimation allows to make a decision how much a new observation will increase the certainty of the robots’ beliefs about the environment state which is observed. Results. The software which implements the proposed criterion for feature set informativeness estimation and allows to manage multi robot teams has been developed. Conclusions. The conducted experiments have confirmed operability of the proposed criterion for feature set informativeness estimation and allow to recommend it for multi robot teams control in practice. The prospects for further researches may include the modification of the known multi robot teams control methods and the development of new ones based on the proposed criterion for feature set informativeness estimation.

Online Learning and Teaching of Emergent Behaviors in Multi-Robot Teams

In this manuscript, we propose an approach that allows a team of robots to create new (emergent) behaviors at execution time. Basically, we improve the approach called N-Learning used for self-programming of robots in a team, by modifying and extending its functioning structure. The basic capability of behavior sharing is increased by the catching of emergent behaviors at run time. With this, all robots are able not only to share existing knowledge, here represented by blocks of codes containing desired behaviors but also to creating new behaviors as well. Experiments with real robots are presented in order to validate our approach. The experiments demonstrate that after the human-robot interaction with one robot using Program by Demonstration, this robot generates a new behavior at run time and teaches a second robot that performs the same learned behavior through this improved version of the N-learning system.

Multi-robot coordination for connectivity recovery after unpredictable environment changes

Abstract In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of new obstacles. After the changes, the team is divided into different groups of robots. The groups have a limited communication range and only a partial information in their field of view about the current scenario. Their objective is to form a chain from a static base station to a goal location. In the proposed distributed replanning approach, the robots predict new plans for the other groups from the new observed information by each robot in the changed scenario, to restore the connectivity with a base station and reach the initial joint objective. If a solution exists, the method achieves the reconnection of all the groups in a unique chain. The proposed method is compared with other two cases: 1) when all the agents have full information of the environment, and 2) when some robots must move to reach other waiting robots for reconnection. Numerical simulations are provided to evaluate the proposed approach in the presence of unpredictable scenario changes.

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams.

Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.

Multi-Robot Cyber Physical System for Sensing Environmental Variables of Transmission Line.

The normal operation of a power grid largely depends on the effective monitoring and maintenance of transmission lines, which is a process that has many challenges. The traditional method of the manual or remote inspection of transmission lines is time-consuming, laborious, and inefficient. To address this problem, a novel method has been proposed for the Multi-Robot Cyber Physical System (MRCPS) of a power grid based on inspection robots, a wireless sensor network (WSN), and multi-agent theory to achieve a low-cost, efficient, fault-tolerant, and remote monitoring of power grids. For the sake of an effective monitoring system for smart grids, the very research is conducted focusing on designing a methodology that will realize the efficient, fault-tolerant, and financial balance of a multi-robot team for monitoring transmission lines. Multiple testing scenarios are performed, in which various aspects are explored so as to determine the optimal parameters balancing team performance and financial cost. Furthermore, multi-robot team communication and navigation control in smart grid environments are introduced.

Investigating Human-Robot Teams for Learning-Based Semi-autonomous Control in Urban Search and Rescue Environments

Teams of semi-autonomous robots can provide valuable assistance in Urban Search and Rescue (USAR) by efficiently exploring cluttered environments and searching for potential victims. Their advantage over solely teleoperated robots is that they can address the task handling and situation awareness limitations of human operators by providing some level of autonomy to the multi-robot team. Our research focuses on developing learning-based semi-autonomous controllers for rescue robot teams. In this paper, we specifically investigate the influence of the operator-to-robot ratio on the performance of our proposed MAXQ hierarchical reinforcement learning based semi-autonomous controller for USAR missions. In particular, we propose a unique learning-based system architecture that allows operator control of larger numbers of rescue robots in a team as well as effective sharing of information between these robots. A rigorous comparative study of our learning-based semi-autonomous controller versus a fully teleoperation-based approach was conducted in a 3D simulation environment. The results, as expected, show that, for both semi-autonomous and teleoperation modes, the total scene exploration time increases as the number of robots utilized increases. However, when using the proposed learning-based semi-autonomous controller, the rate of exploration-time increase and operator-interaction effort are significantly lower, while task performance is significantly higher. Furthermore, an additional case study showed that our learning-based approach can provide more scene coverage during robot exploration when compared to a non-learning based method.