A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased

An Experimental Platform for Multi-spacecraft Phase-Array Communications

The emergence of small satellites and CubeSats for interplanetary exploration will mean hundreds if not thousands of spacecraft exploring every corner of the solar-system. Current methods for communication and tracking of deep space probes use ground based systems such as the Deep Space Network (DSN). However, the increased communication demand will require radically new methods to ease communication congestion. Networks of communication relay satellites located at strategic locations such as geostationary orbit and Lagrange points are potential solutions. Instead of one large communication relay satellite, we could have scores of small satellites that utilize phase arrays to effectively operate as one large satellite. Excess payload capacity on rockets can be used to warehouse more small satellites in the communication network. The advantage of this network is that even if one or a few of the satellites are damaged or destroyed, the network still operates but with degraded performance. The satellite network would operate in a distributed architecture and some satellites maybe dynamically repurposed to split and communicate with multiple targets at once. The potential for this alternate communication architecture is significant, but this requires development of satellite formation flying and networking technologies. Our research has found neural-network control approaches such as the Artificial Neural Tissue can be effectively used to control multirobot/multi-spacecraft systems and can produce human competitive controllers. We have been developing a laboratory experiment platform called Athena to develop critical spacecraft control algorithms and cognitive communication methods. We briefly report on the development of the platform and our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace Applications Worksho

Experiments in cooperative human multi-robot navigation

In this paper, we consider the problem of a group of autonomous mobile robots and a human moving coordinately in a real-world implementation. The group moves throughout a dynamic and unstructured environment. The key problem to be solved is the inclusion of a human in a real multi-robot system and consequently the multiple robot motion coordination. We present a set of performance metrics (system efficiency and percentage of time in formation) and a novel flexible formation definition whereby a formation control strategy both in simulation and in real-world experiments of a human multi-robot system is presented. The formation control proposed is stable and effective by means of its uniform dispersion, cohesion and flexibility

Effects of spatial ability on multi-robot control tasks

Working with large teams of robots is a very complex and demanding task for any operator and individual differences in spatial ability could significantly affect that performance. In the present study, we examine data from two earlier experiments to investigate the effects of ability for perspective-taking on performance at an urban search and rescue (USAR) task using a realistic simulation and alternate displays. We evaluated the participants' spatial ability using a standard measure of spatial orientation and examined the divergence of performance in accuracy and speed in locating victims, and perceived workload. Our findings show operators with higher spatial ability experienced less workload and marked victims more precisely. An interaction was found for the experimental image queue display for which participants with low spatial ability improved significantly in their accuracy in marking victims over the traditional streaming video display. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

General detection model in cooperative multirobot localization

The cooperative multirobot localization problem consists in localizing each robot in a group within the same environment, when robots share information in order to improve localization accuracy. It can be achieved when a robot detects and identifies another one, and measures their relative distance. At this moment, both robots can use detection information to update their own poses beliefs. However some other useful information besides single detection between a pair of robots can be used to update robots poses beliefs as: propagation of a single detection for non participants robots, absence of detections and detection involving more than a pair of robots. A general detection model is proposed in order to aggregate all detection information, addressing the problem of updating poses beliefs in all situations depicted. Experimental results in simulated environment with groups of robots show that the proposed model improves localization accuracy when compared to conventional single detection multirobot localization.FAPESPCNP

Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.Fil: Mas, Ignacio Agustin. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kitts, Christopher. Santa Clara University; Estados Unido

A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments

Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009