**1. Introduction**

Teleoperation, or remote tele-manipulation of robots in inaccessible environments such as deep sea and outer space [1, 2], includes a wide variety of applications such as micro- and

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nano-teleoperation [3], tele-surgery [4], etc. A network-based teleoperation system involves distant interactions between human operators and remote robotic systems [5, 6]. In addi‐ tion, swarms of robots are widely employed in complex tasks that cannot be performed by a single robot or in tasks that are better achieved by cooperation of robots such as localization in formations [7], target tracking [8], mapping and localization [9], object pushing [10], area exploration for search and rescue [11], etc. When swarms of mobile robots are teleoperated, specific network requirements should be satisfied in order to guarantee a minimum quality of control, which results in efficient task execution. Research done on teleoperated systems showed that constraints such as bandwidth and CPU processing cause the Quality of Service (QoS) to degrade to an extent that may severely affect performance [12, 13]. To address this problem, various bandwidth management algorithms have been presented for distributed multimedia systems in order to maintain a performance that guarantees an adequate QoS [6]. However, the literature rarely tackled the problem of managing bandwidth based on sensory feedback and the quality of collaboration among robots. Accordingly, a real-time dynamic optimized bandwidth management for teleoperation of collaborative robots is introduced in this paper. The proposed method accounts for interesting events (IEs) and the change in the quality of collaboration (QoC) between robots in order to optimize the allocation of band‐ width between acting agents, where necessary, in a given environment. The developed optimization technique showed outstanding performances when implemented on a system of two collaborating humanoid robots, and thus could be considered a basis for a framework for highly complex algorithms implemented in systems involving real-time bandwidth optimi‐ zation, where multiple users control multiple collaborating robots.

Different types of resource management algorithms are used to solve the bandwidth allocation problem in robotics systems. Such applications in networked control systems fall into two main categories: static [14] and dynamic [15] bandwidth allocation. Static methods cannot adapt to changes in the system state (surrounding environment, collaboration quality, etc.). Alterna‐ tively, dynamic bandwidth allocation algorithms increase performance at the cost of increased computation. Mourikis et al. [7] address the problem of resource allocation in formations of mobile robots localizing as a group. The goal is to determine the frequency at which each individual sensor should be used in order to attain the highest possible localization accuracy. The set of frequencies mentioned is obtained by solving an optimization problem that maximizes the accuracy matrix expressed in terms of the sensors' frequencies. However, the problem is solved offline and the algorithm does not account for any dynamic events that might occur. Sugiyama et al. [8] propose a bandwidth reservation algorithm for multi-robot systems in a target tracking mission. The interesting information, corresponding to a survivor's detection, is sent to the base station with wideband signals such as dynamic picture images. The final call is left to operators to decide whether the received images indicate a real victim, by allowing/preventing the corresponding robot to reserve the bandwidth affecting the flow of various signals from other robots to the base station. In this approach, the operator's intervention is crucial in allocating bandwidth and thus the allocation process is not fully automated. Xi et al. [5] developed a bandwidth allocation mechanism based on online measured task dexterity index of dynamic tasks so that operators can control remote manip‐ ulators efficiently and smoothly even under poor network quality. However, the executed task is simple and does not require the collaboration of multiple robots to be performed. Thus, the quality of collaboration factor is not considered in the bandwidth allocation. Finally, in [10], a bandwidth management algorithm is introduced and the rate of feedback is regulated based on the amount of activities occurring in the environment. The work shows that during complex tasks, the operator's performance is affected by the rate of feedback of information. It is also confirmed that a higher sampling rate is required to maintain the same level of performance obtained when the environment is less dynamic. Yet, the implemented algorithm does not impose any constraint on the total bandwidth of the system. In addition, the notion of monitoring changes in QoC to allocate bandwidth is not mentioned since the task execution only requires the use of a single robot. To the best of the author's knowledge, there was limited research addressing bandwidth management for the specific application of collaborative robots teleoperation. In 2015, Ricardo and Guilherme designed a Dynamic Bandwidth Management Library to control the frequency of individual sensors present in a robotic environment performing a certain task [16]. This work is seeking a universal Dynamic Bandwidth Management Library designed to be used on a system with a variable number of heterogeneous robots performing any collaborative task that requires communication trans‐ actions such as the exchange of sensor data between involved agents.

Accordingly, the main contribution of the work presented in this paper is in accounting for (a) IEs occurring in the robotic swarm's environment and (b) changes in QoC among the swarm of robots in real-time optimized bandwidth management of teleoperated collaborative robots. Consequently, assessing the multi-robot swarm dynamics, the stability of the robotic swarm and the effects of packet loss and transmission delay under the proposed algorithm falls out of the scope of this paper. Factoring the latter into the proposed algorithm is possible but will alter the emphasis from the main contribution of dynamic optimized bandwidth management.

A literature review of the most relevant work in bandwidth management was presented in Section 1. The general problem is formulated in Section 2. The formulation is then implemented on an application in which an operator drives two collaborating robots. Section 3 describes the experimental set-up and the corresponding results. Finally, conclusions are presented in Section 4.
