In networked robot systems (NRS), robots and robotic devices are distributed in the environment; typically tasks are performed by cooperation and coordination of such multiple networked components. NRS offer advantages over monolithic systems in terms of modularity, flexibility and cost effectiveness, and they are thus becoming a mainstream approach to the inclusion of robotic solutions in everyday environments. The components of a NRS are usually robots and sensors equipped with rich computational and communication facilities. In this thesis, we argue that the capabilities of a NRS would greatly increase if it could also accommodate among its nodes simpler entities, like small ubiquitous sensing and actuation devices, home appliances, or augmented everyday objects. For instance, a domestic robot needs to manipulate food items and interact with appliances. Such a robot would benefit from the ability to exchange information with those items and appliances in a direct way, in the same way as with other networked robots and sensors. Combining such highly heterogeneous devices inside one NRS is challenging, and one of the major challenges is to provide a common communication and collaboration infrastructure. In the field of NRS, this infrastructure is commonly provided by a shared middleware. Unfortunately, current middlewares lack the generality needed to allow heterogeneous entities such as robots, simple ubiquitous devices and everyday objects to coexist in the same system. In this thesis we show how an existing middleware for NRS can be extended to include three new types of “citizens” in the system, on peer with the other robots. First, we include computationally simple embedded devices, like ubiquitous sensors and actuators, by creating a fully compatible tiny version of the existing robotic middleware. Second, we include augmented everyday objects or home appliances which are unable to run the middleware on board, by proposing a generic design pattern based on the notion of object proxy. Finally,we go one step further and include humans as nodes in the NRS by defining the notion of human proxy. While there exist a few other NRS which are able to include both robots and simple embedded devices in the same system, the use of proxies to include everyday objects and humans in a generic way is a unique feature of this work. In order to verify and validate the above concepts, we have implemented them in the Peis-Ecology NRS model. We report a number of experiments based on this implementation, which provide both quantitative and qualitative evaluations of its performance, reliability, and interoperability.

<div>Multi-robot systems have demonstrated strong capability in handling environmental operations. In this study, We examine how a team of robots can be utilized in covering and removing spill patches in a dynamic environment by executing three consecutive stages: deployment, coverage, and rendezvous. </div><div> </div><div>For the deployment problem, we aim for robot allocation based on the discreteness of the patches that need to be covered. With the deep neural network (DNN) based spill detector and remote sensing facilities such as drones with vision sensors and satellites, we are able to obtain the spill distribution in the workspace. Then, we formulate the allocation problem in a general optimization form and provide solutions using an integer linear programming (ILP) solver under several realistic constraints. After the allocation process is completed and the robot team is divided according to the number of spills, we deploy robots to their computed optimal goal positions. In the robot deployment part, control laws based on artificial potential field (APF) method are proposed and practiced on robots with a common unicycle model. </div><div> </div><div>For the coverage control problem, we show two strategies that are tailored for a wirelessly networked robot team. We propose strategies for coverage with and without path planning, depending on the availability of global information. Specifically, in terms of coverage with path planning, we partition the workspace from the aerial image into pieces and let each robot take care of one of the pieces. However, path-planning-based coverage relies on GPS signals or other external positioning systems, which are not applicable for indoor or GPS-denied circumstances. Therefore, we propose an asymptotic boundary shrink control that enables a collective coverage operation with the robot team. Such a strategy does not require a planned path, and because of its distributedness, it shows many advantages, including system scalability, dynamic spill adaptability, and collision avoidance. In case of a large-scale patch that poses challenges to robot connectivity maintenance during the operation, we propose a pivot-robot coverage strategy by mean of an a priori geometric tessellation (GT). In the pivot-robot-based coverage strategy, a team of robots is sent to perform complete coverage to every packing area of GT in sequence. Ultimately, the entire spill in the workspace can be covered and removed.</div><div> </div><div>For the rendezvous problem, we investigate the use of graph theory and propose control strategies based on network topology to motivate robots to meet at a designated or the optimal location. The rendezvous control strategies show a strong robustness to some common failures, such as mobility failure and communication failure. To expedite the rendezvous process and enable herding control in a distributed way, we propose a multi-robot multi-point rendezvous control strategy. </div><div> </div><div>To verify the validity of the proposed strategies, we carry out simulations in the Robotarium MATLAB platform, which is an open source swarm robotics experiment testbed, and conduct real experiments involving multiple mobile robots.</div>