Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the swarm system is designed to work as an independent entity. The swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.

Recently, there has been an increasing interest in robotics related to multi-robot applications. Such systems can be involved in several tasks such as collaborative search and rescue, aerial transportation, surveillance, and monitoring, to name a few. There are two possible architectures for the autonomous control of multi-robot systems. In the centralized architecture, a master controller communicates with all the robots to collect information. It uses this information to make decisions for the entire system and then sends commands to each robot. In contrast, in the distributed architecture, each robot makes its own decision independent from a central authority. While distributed architecture is a more portable solution, it comes at the expense of extensive information exchange (communication). The extensive communication between robots can result in decision delays because of which distributed architecture is often impractical for systems with strict real-time constraints, e.g. when decisions have to be taken in the order of milliseconds. In this thesis, we propose a distributed framework that strikes a balance between limited communicated information and reasonable system-wide performance while running in real-time. We implement the proposed approach in a game setting of two competing teams of drones, defenders and attackers. Defending drones execute a proposed linear program algorithm (using only onboard computing modules) to obstruct attackers from infiltrating a defense zone while having minimal local message passing. Another main contribution is that we developed a realistic simulation environment as well as lab and outdoor hardware setups of customized drones for testing the system in realistic scenarios. Our software is completely open-source and fully integrated with the well-known Robot Operating System (ROS) in hopes to make our work easily reproducible and for rapid future improvements.

No description available.

Decision-making is an important operation for any autonomous system. Robots in particular must observe their environment and compute appropriate responses. For solitary robots and centralized multiple-robot systems, decision-making is a relatively straightforward operation, since only a single agent (either the solitary robot or the single central controller) is solely responsible for the operation. The problem is much more complex in a decentralized system, to the point where optimal decision-making is intractable in the general case. Decentralized multiple-robot systems (dec-MRS) are robotic teams in which no robot is in authority over any others. The globally observed behaviour of dec-MRS emerges out of the individual robots’ local interactions with each other. This makes system-level decision-making, an operation in which an entire dec-MRS cooperatively makes a decision, a difficult problem. Social insects have long been a source of inspiration for dec-MRS research, and their example is followed in this work. Honeybees and Temnothorax ants must make group decisions in order to choose a new nest site whenever they relocate their colonies. Like the simple robots that compose typical dec-MRS, the insects utilize local, peer-to-peer behaviours to make good, cooperative decisions. This thesis examines their decision-making strategies in detail and proposes a three-phase framework for system-level decision-making by dec-MRS. Two different styles of decision are described, and experiments in both simulation and with real robots were carried out and presented here to demonstrate the framework’s decision-making ability. Using only local, anonymous communication and emergent behaviour, the proposed collective decision-making framework is able to make good decisions reliably, even in the presence of noisy individual sensing. Social cues such as consensus and quorum testing enables the robots to predicate their behaviour during the decision-making process on the global state of their system. Furthermore, because the operations carried out by the individual robots are so simple, and because their complexity to the individual robots is independent of the population size of a dec-MRS, the proposed decision-making framework will scale well to very large population sizes.

In this thesis fundamental pieces of multi robot systems have been discussed and researched, in order to develop and build a system with easily obtainable electronics and to answer how much communication is needed as well as which design choices are important to make it robust. Our work will hopefully contribute to others in the community who are working with Raspberry Pi and Windows 10 IoT Core by being open source. As a result a proof of concept system of two simple robots has been implemented. In this paper we have used trigonometry and dead-reckoning for localization, when coordinating our robots a leader/follower model has been applied. The communication has been developed using the AllJoyn framework to develop an interface that has IoT capabilities. The results show that our system has working communication and simulated localization, however the limitations in the hardware results in an error in localization which we present in this paper. To answer our research questions the amount of communication needed is dependent on the problem and how many robots you need to apply in order to solve it and the most important design choice for current multi robot systems is a controlled environment.

The frequent occurrences of natural and technological disasters have incurred grave loss of life and damage to property. For mitigating the miserable aftermaths, multi-robot teleoperation systems have been developed and deployed to cooperate with human rescuers in post-earthquake scenarios, and to sample, monitor and clean pollutants in marine environments. With a bidirectional communication channel, human users can deliver commands/requests to guide the motions of the remote robots, and can receive visual/audio feedback to supervise the status of the remote environment, throughout multi-robot teleoperation. Furthermore, the remote robots can send force feedback to human operators to improve their situational awareness and task performance. This way, a closed-loop multi-robot teleoperation system becomes bilateral in which coordinated robots physically interact and exchange energy with human users, and hence needs to be rendered passive for safe human-robot interaction. Beyond guaranteeing closed-loop passivity, the control of a bilateral multi-robot teleoperation system faces two challenging problems: preserving the communication connectivity of the remote robots; and dispatching the teleoperation authority to multiple human users. Because wireless transmission of radio/acoustic signals between the remote robots is constrained by their distances, bilateral multi-robot teleoperation control must coordinate the motions of the remote robots appropriately so as to maintain their communication network connected. Further, multiple human users can send possibly conflicting teleoperation commands to the remote robots, a distributed authority dispatch algorithm is thus needed for the remote robot network to recognize and follow the most urgent user commands at runtime. This thesis develops an energy shaping strategy to preserve the connectivity of the remote robots, and to dispatch control authority over the remote robots to human users, during bilateral multi-robot teleoperation. Chapter 1 introduces the application background of multi-robot teleoperation as well as the state-of-the-art development in related research areas. In Chapter 2, a dynamic interconnection and damping strategy is proposed to reduce and constrain the position error between the local and remote robots to any prescribed bound during bilateral teleoperation. Chapter 3 derives a gradient plus damping control from a bounded potential function and then unifies it into an indirect coupling framework to preserve all communication links of an autonomous multi-robot system with time-varying delays and bounded actuation. On these bases, Chapter 4 develops a dynamic feedforward-feedback passivation strategy to preserve all communication links and thus the connectivity of the tree network of the remote robots while rendering the bilateral multi-robot teleoperation close loop passive. Specifically, by blending the sliding variable in Chapter 2 with the bounded potential function in Chapter 3, the dynamic passivation strategy decomposes the dynamics of the remote robots into a power-preserving interconnection of two subsystems, and regulates the energy behaviour of each subsystem to preserve the tree communication connectivity of the remote robots. To handle time-varying communication delays, the strategy further transforms the communication channels between the local and remote robots into a dynamic controller for passivating bilateral teleoperation. Superior to existing controls, the strategy using a bounded potential function can circumvent numerical instability, reduce noise sensitivity and facilitate future extensions to accommodate robot actuator saturation. On the other side, Chapter 5 designs a distributed and exponentially convergent winners-take-all authority dispatch algorithm that activates the teleoperation of only human users with the most urgent requests in real time. After formulating the problem as a constrained quadratic program, we employ an exact penalty function method to construct a distributed primal-dual dynamical system that can solve the problem at an exponential rate. Because the equilibrium of the system changes with user requests, we then interconnect the dynamical system with physical robot dynamics in a power-preserving way, and passivate closed-loop multi-robot teleoperation using multiple storage functions from a switched system perspective. Finally, Chapter 6 provides some conclusive remarks and two problems regarding connectivity preservation and authority dispatch for future study. / Graduate

Multi-robot systems leverage the numbers and characteristics of different robots to accomplish an overall mission. Efficient task allocation and motion planning of multi-robot teams are essential to ensure each robot's actions contribute to the overall mission while avoiding conflict with each other. The original contribution of this thesis is an optimized, efficient, and multi-factor task allocation algorithm to comprise the main component of a task coordination framework (TCF), with motion planning as a secondary component. This algorithm determines which robot performs which tasks and in what order. It presents a novel solution to the multiple robot task allocation problem (MRTA) as an extension of the multiple travelling salesmen (MTSP) problem. This extension to the MTSP considers operational factors representing physical limitations, the suitability of each robot, and inter-task dependencies. The task allocation algorithm calculates an optimized distribution of tasks such that a global objective function is minimized to simultaneously reduce total cost and ensure an even distribution of tasks among the agents. Once an optimized distribution of tasks is calculated, the motion planning component calculates collision-free velocities to drive the robots to their goal poses to facilitate task execution in a shared environment. The proposed TCF was implemented on teams of unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). Test cases considered scenarios where the UAVs executed aerial observation tasks while UGVs executed simulated patrol and delivery tasks. The solutions were tested using real-life robots as a proof of concept and to validate simulations. The robots' kinematic and computer vision models were combined with the task coordination framework to facilitate the implementation. Large-scale simulations involving greater numbers of robots operating in a larger area were also conducted to demonstrate the task coordination framework's versatility and efficacy.

Multi-robot systems have recently witnessed a swell in interest in the past few years because of their various applications such as agricultural autonomy, medical robotics, industrial and commercial automation and, search and rescue. In this thesis, we particularly investigate the behavior of multi-robot systems with respect to stable topology control in asymmetric interaction settings. From theoretical perspective, we first classify stable topologies, and identify the conditions under which we can determine whether a topology is stable or not. Then, we design a limited fields-of-view (FOV) controller for robots that use sensors like cameras for coordination which induce asymmetric robot to robot interactions. Finally, we conduct a rigorous theoretical analysis to qualitatively determine which interactions are suitable for stable directed topology control of multi-robot systems with asymmetric interactions. In this regard, we solve an optimal topology selection problem to determine the topology with the best interactions based on a suitable metric that represents the quality of interaction. Further, we solve this optimal problem distributively and validate the distributed optimization formulation with extensive simulations.  For experimental purposes, we developed a portable multi-robot testbed which enables us to conduct multi-robot topology control experiments in both indoor and outdoor settings and validate our theoretical findings. Therefore, the contribution of this thesis is two fold: i) We provide rigorous theoretical analysis of  stable coordination of multi-robot systems with directed graphs, demonstrating the graph structures that induce stability for a broad class of coordination objectives; ii) We develop a testbed that enables validating multi-robot topology control in both indoor and outdoor settings. / Doctor of Philosophy / In this thesis, we address the problem of collaborative tasks in a multi-robot system where we investigate how interactions within members of the multi-robot system can induce instability. We conduct rigorous theoretical analysis and identify when the system will be unstable and hence classify interactions that will lead to stable multi-robot coordination. Our theoretical analysis tries to emulate realistic interactions in a multi-robot system such as limited interactions (blind spots) that exist when on-board cameras are used to detect and track other robots in the vicinity. So we study how these limited interactions induce instability in the multi-robot system. To verify our theoretical analysis experimentally,  we developed a portable multi-robot testbed that enables us to test our theory on stable coordination of multi-robot system with a team of Unmanned Aerial Vehicles (UAVs) in both indoor and outdoor settings. With this feature of the testbed we are able to investigate the difference in the multi-robot system behavior when tested in controlled indoor environments versus an uncontrolled outdoor environment. Ultimately, the motivation behind this thesis is to emulate realistic conditions for multi-robot cooperation and investigate suitable conditions for them to work in a stable and safe manner. Therefore, our contribution is twofold ; i) We provide rigorous theoretical analysis that enables stable coordination of multi-robot systems with limited interactions induced by sensor capabilities such as cameras; ii) We developed a testbed that enables testing of our theoretical contribution with a team of real robots in realistic environmental conditions.

The evolution of cloud computing has finally started to affect robotics. Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite computational power and enabling collaboration of a multitude of connected devices. However, its drawbacks include higher latency and overall higher energy consumption. Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems. In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning. / Master of Science

We present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.