Experimental Testbed for Large Multirobot Teams

Experimental validation is particularly important in multirobot systems research. The differences between models and real-world conditions that may not be apparent in single robot experiments are amplified because of the large number of robots, interactions between robots, and the effects of asynchronous and distributed control, sensing, and actuation. Over the last two years, we have developed an experimental testbed to support research in multirobot systems with the goal of making it easy for users to model, design, benchmark, and validate algorithms. In this article, we describe our approach to the design of a large-scale multirobot system for the experimental verification and validation of a variety of distributed robotic applications in an indoor environment

Object Closure and Manipulation by Multiple Cooperating Mobile Robots

We address the manipulation of planar objects by multiple cooperating mobile robots using the concept of Object Closure. In contrast to Form or Force Closure, Object Closure is a condition under which the object is trapped so that there is no feasible path for the object from the given position to any position that is beyond a specified threshold distance. Once Object Closure is achieved, the robots can cooperatively drag or flow the trapped object to the desired goal. In this paper, we define object closure and develop a set of decentralized algorithms that allow the robots to achieve and maintain object closure. We show how simple, first-order, potential field based controllers can be used to implement multirobot manipulation tasks

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

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

Object Manipulation using a Multirobot Cluster with Force Sensing

This research explored object manipulation using multiple robots by developing a control system utilizing force sensing. Multirobot solutions provide advantages of redundancy, greater coverage, fault-tolerance, distributed sensing and actuation, and reconfigurability. In object manipulation, a variety of solutions have been explored with different robot types and numbers, control strategies, sensors, etc. This research involved the integration of force sensing with a centralized position control method of two robots (cluster control) and building it into an object level controller. This controller commands the robots to push the object based on the measured interaction forces between them while maintaining proper formation with respect to each other and the object. To test this controller, force sensor plates were attached to the front of the Pioneer 3-AT robots. The object is a long, thin, rectangular prism made of cardboard, filled with paper for weight. An Ultra Wideband system was used to track the positions and headings of the robots and object. Force sensing was integrated into the position cluster controller by decoupling robot commands, derived from position and force control loops. The result was a successful pair of experiments demonstrating controlled transportation of the object, validating the control architecture. The robots pushed the object to follow linear and circular trajectories. This research is an initial step toward a hybrid force/position control architecture with cluster control for object transportation by a multirobot system

A data grid strategy for non-prehensile object transport by a multi-robot system

The field of cooperative object transport within swarm and multi-robot systems (MRS) is an interesting area of research because exploring the dynamics of how multiple robots collaborate to transport objects presents fascinating opportunities to advance the capabilities of robotic teams. In this thesis we propose a control framework for non-prehensile object transport using a multi-robot system. While an object can be unmanageable for a single robot to push and transport, we demonstrate via simulations that a team of cooperative robots can be used to transport such an object. The proposed control strategy is divided into two phases: caging and cooperative transport. In the first phase, the robots start from arbitrary positions and then approach the object to be transported, forming a cage around it. The second phase consists of cooperatively transporting the object ensuring that it remains caged during transport. In the proposed strategy, the robots take a decentralized approach, wherein each robot operates autonomously while maintaining indirect communication with other robots. This is achieved by utilizing distributed data structures, such as distributed locks, sets, and maps, offered by the concept of an in-memory data grid (IMDG). By leveraging these distributed data structures, the robots can effectively share their respective states with one another. The key advantage of employing distributed data structures within our strategy is that it eliminates the need to develop a new application-specific protocol for interrobot communication. Instead, we can take advantage of the existing functionality provided by the data grid, which offers a convenient mechanism for exchanging information between robots. To our knowledge, the utilization of an IMDG in the domain of MRS is a novel concept. This thesis introduces a proposed design for a coordinated motion control strategy specifically aimed at object transport. The strategy leverages the capabilities of an IMDG, and presents it as a promising framework to simplify the communication process among the robots involved, leading to improved coordination and optimized object transport operations. We showcase the results of our research using a realistic simulator that effectively illustrates the viability of our method. This demonstration helps validate the effectiveness and potential of our methodology in various environments

Occlusion-based cooperative transport with a swarm of miniature mobile robots

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems