Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Stable Matching based Resource Allocation for Service Provider\u27s Revenue Maximization in 5G Networks

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled service providers (SPs) and service requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs\u27 revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a distributed many-to-many stable matching-based solution. Moreover, we offer an adaptive stable matching based distributed algorithm to solve the formulated problem in a dynamic network model. Through extensive theoretical and simulation analysis, we have shown the effect of different parameters on the resource allocation objectives and achieves 94 percent of optimum network performance

Adaptive real-time predictive collaborative content discovery and retrieval in mobile disconnection prone networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols