A distributed optimization framework for localization and formation control: applications to vision-based measurements

Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures

Unmanned vehicles formation control in 3D space and cooperative search

The first problem considered in this dissertation is the decentralized non-planar formation control of multiple unmanned vehicles using graph rigidity. The three-dimensional formation control problem consists of n vehicles operating in a plane Q and r vehicles that operate in an upper layer outside of the plane Q. This can be referred to as a layered formation control where the objective is for all vehicles to cooperatively acquire a predefined formation shape using a decentralized control law. The proposed control strategy is based on regulating the inter-vehicle distances and uses backstepping and Lyapunov approaches. Three different models, with increasing level of complexity are considered for the multi-vehicle system: the single integrator vehicle model, the double integrator vehicle model, and a model that represents the dynamics of a class of robotics vehicles including wheeled mobile robots, underwater vehicles with constant depth, aircraft with constant altitude, and marine vessels. A rigorous stability analysis is presented that guarantees convergence of the inter-vehicle distances to desired values. Additionally, a new Neural Network (NN)-based control algorithm that uses graph rigidity and relative positions of the vehicles is proposed to solve the formation control problem of unmanned vehicles in 3D space. The control law for each vehicle consists of a nonlinear component that is dependent on the closed-loop error dynamics plus a NN component that is linear in the output weights (a one-tunable layer NN is used). A Lyapunov analysis shows that the proposed distance-based control strategy achieves the uniformly ultimately bounded stability of the desired infinitesimally and minimally rigid formation and that NN weights remain bounded. Simulation results are included to demonstrate the performance of the proposed method. The second problem addressed in this dissertation is the cooperative unmanned vehicles search. In search and surveillance operations, deploying a team of unmanned vehicles provides a robust solution that has multiple advantages over using a single vehicle in efficiency and minimizing exploration time. The cooperative search problem addresses the challenge of identifying target(s) in a given environment when using a team of unmarried vehicles by proposing a novel method of mapping and movement of vehicle teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, the vehicles have an efficient travel path while performing searches due to this partitioning approach. Second, a team of unmanned vehicles that move in a cooperative manner and utilize the Tabu Random algorithm is used to search for target(s). Due to the ever-increasing use of robotics and unmanned systems, the field of cooperative multi-vehicle search has developed many applications recently that would benefit from the use of the approach presented in this dissertation, including: search and rescue operations, surveillance, data collection, and border patrol. Simulation results are presented that show the performance of the Tabu Random search algorithm method in combination with hexagonal partitioning

Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance

We address the controller synthesis problem for distributed formation control. Our solution requires only relative bearing measurements (as opposed to full translations), and is based on the exact gradient of a Lyapunov function with only global minimizers (independently from the formation topology). These properties allow a simple proof of global asymptotic convergence, and extensions for including distance measurements, leaders and collision avoidance. We validate our approach through simulations and comparison with other stateof-the-art algorithms.ARL grant W911NF-08-2-0004, ARO grant W911NF-13-1-0350, ONR grants N00014-07-1-0829, N00014-14-1-0510, N00014-15-1-2115, NSF grant IIS-1426840, CNS-1521617 and United Technologies

Robust Distance-Based Formation Control of Multiple Rigid Bodies with Orientation Alignment

This paper addresses the problem of distance- and orientation-based formation control of a class of second-order nonlinear multi-agent systems in 3D space, under static and undirected communication topologies. More specifically, we design a decentralized model-free control protocol in the sense that each agent uses only local information from its neighbors to calculate its own control signal, without incorporating any knowledge of the model nonlinearities and exogenous disturbances. Moreover, the transient and steady state response is solely determined by certain designer-specified performance functions and is fully decoupled by the agents' dynamic model, the control gain selection, the underlying graph topology as well as the initial conditions. Additionally, by introducing certain inter-agent distance constraints, we guarantee collision avoidance and connectivity maintenance between neighboring agents. Finally, simulation results verify the performance of the proposed controllers.Comment: IFAC Word Congress 201

A Low-Cost Experimental Testbed for Multi-Agent System Coordination Control

A multi-agent system can be defined as a coordinated network of mobile, physical agents that execute complex tasks beyond their individual capabilities. Observations of biological multi-agent systems in nature reveal that these ``super-organisms” accomplish large scale tasks by leveraging the inherent advantages of a coordinated group. With this in mind, such systems have the potential to positively impact a wide variety of engineering applications (e.g. surveillance, self-driving cars, and mobile sensor networks). The current state of research in the area of multi-agent systems is quickly evolving from the theoretical development of coordination control algorithms and their computer simulations to experimental validations on proof-of-concept testbeds using small-scale mobile robotic platforms. An in-house testbed would allow for rapid prototyping and validation of control algorithms, and potentially lead to new research directions spawned by experimentally-observed issues. To this end, a custom experimental testbed, TIGER Square, has been designed, developed, built, and tested at Louisiana State University. In this work, the completed design and test results for a centralized testbed is presented. That is, the individual robots follow an overarching control entity and are reliant on a global structure, such as a central processing computer. As part of the validation process, a series of formation control experiments were executed to assess the performance of the testbed. In order to eliminate single-point failures, a multi-agent system must be fully decentralized or distributed. This means that the responsibilities of processing, localization, and communication are distributed to each agent. Therefore, this work concludes with the introduction of a prototype localization module that will be integrated into the existing centralized testbed. This initial step allows for the future decentralization of TIGER Square and opens the path to achieve a fully capable multi-agent system testbed

Radiative Contour Mapping Using UAS Swarm

The work is related to the simulation and design of small and medium scale unmanned aerial system (UAS), and its implementation for radiation measurement and contour mapping with onboard radiation sensors. The compact high-resolution CZT sensors were integrated to UAS platforms as the plug-and-play components using Robot Operation System. The onboard data analysis provides time and position-stamped intensities of gamma-ray peaks for each sensor that are used as the input data for the swarm flight control algorithm. In this work, a UAS swarm is implemented for radiation measurement and contour mapping. The swarm of UAS has advantages over a single agent based approach in detecting radiative sources and effectively mapping the area. The proposed method can locate sources of radiation as well as mapping the contaminated area for enhancing situation awareness capabilities for first responders. This approach uses simultaneous radiation measurements by multiple UAS flying in a circular formation to find the steepest gradient of radiation to determine a bulk heading angle for the swarm for contour mapping, which can provide a relatively precise boundary of safety for potential human exploration