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

Cooperative Object Transport in Multi-robot Systems:A Review of the State-of-the-Art

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increase the visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectivelypublishersversionPeer reviewe

Attractor dynamics generates robot formations: from theory to implementation

We show how non-linear attractor dynamics can be used to implement robot formations in unknown environments. The desired formation geometry is given through a matrix where the parameters in each line (its leader, desired distance and relative orientation to the leader) define the desired pose of a robot in the formation. The parameter values are then used to shape the vector fields of the dynamical systems that generate values for the control variables (i.e. heading direction and path velocity). Then these dynamical systems are tuned such that the control variables are always very close to one of the resultant attractors. The advantage is that the systems are more robust against perturbations because the behavior is generated as a time series of asymptotically stable states. Experimental results (with three Khepera robots) demonstrate the ability of the team to create and stabilize the formation, as well as avoiding obstacles. Flexibility is achieved in that as the senses world changes, the systems may change their planning solutions continuously but also discontinuously (tunning the formation versus split to avoid obstacle).Fundação para a Ciência e a Tecnologia (FCT) - (POSI/SRI/38051/2001

Cluster Control of Automated Surface Vessels

This research focuses on the design and control of a fleet of robotic kayaks, and presents experimental data regarding the functionality and performance of the system. One of the key technical challenges in fielding multi-robot systems for real-world applications is the coordination and relative motion control of the individual units. Coordinated formation control of the fleet is implemented through the use of the cluster space control architecture, which is a full-order controller that treats the fleet as a virtual, articulating, kinematic mechanism. The resulting system is capable of autonomous navigation utilizing a centralized controller, currently implemented via a shore-based computer that wirelessly receives ASV data and relays control commands. Using the cluster space control approach, these control commands allow a cluster supervisor to oversee a flexible and mobile formation formed by the ASV cluster. This paper includes an extended appendix which includes MatLab and Simulink code as well as two publications completed in the process of this research

Attractor dynamics approach to formation control : theory and application

In this paper we show how non-linear attractor dynamics can be used as a framework to control teams of autonomous mobile robots that should navigate according to a predefined geometric formation. The environment does not need to be known a priori and may change over time. Implicit to the control architecture are some important features such as establishing and moving the formation, split and join of formations (when necessary to avoid obstacles). Formations are defined by a formation matrix. By manipulating this formation matrix it is also possible to switch formations at run time. Examples of simulation results and implementations with real robots (teams of Khepera robots and medium size mobile robots), demonstrate formation switch, static and dynamic obstacle avoidance and split and join formations without the need for any explicit coordination scheme. Robustness against environmental perturbations is intrinsically achieved because the behaviour of each robot is generated as a time series of asymptotically stable states, which contribute to the asymptotic stability of the overall control system.This work was supported through COOP-DYN (POSI/SRI/38051/2001), financed by the Portuguese Foundation for Science and Technology (FCT) and project fp6-IST2 EU-project JAST-Joint Action Science and Technology (project number 003747). We thank Miguel Vaz and Nzoji Hipolito for their help in the implementations with real robots. We would like also to thank the anonymous reviewers for their insightful comments which helped improving the paper

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201