Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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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

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

Formation Control of Nonholonomic Multi-Agent Systems

This dissertation is concerned with the formation control problem of multiple agents modeled as nonholonomic wheeled mobile robots. Both kinematic and dynamic robot models are considered. Solutions are presented for a class of formation problems that include formation, maneuvering, and flocking. Graph theory and nonlinear systems theory are the key tools used in the design and stability analysis of the proposed control schemes. Simulation and/or experimental results are presented to illustrate the performance of the controllers. In the first part, we present a leader-follower type solution to the formation maneuvering problem. The solution is based on the graph that models the coordination among the robots being a spanning tree. Our control law incorporates two types of position errors: individual tracking errors and coordination errors for leader-follower pairs in the spanning tree. The control ensures that the robots globally acquire a given planar formation while the formation as a whole globally tracks a desired trajectory, both with uniformly ultimately bounded errors. The control law is first designed at the kinematic level and then extended to the dynamic level. In the latter, we consider that parametric uncertainty exists in the equations of motion. These uncertainties are accounted for by employing an adaptive control scheme. In the second part, we design a distance-based control scheme for the flocking of the nonholonomic agents under the assumption that the desired flocking velocity is known to all agents. The control law is designed at the kinematic level and is based on the rigidity properties of the graph modeling the sensing/control interactions among the robots. A simple input transformation is used to facilitate the control design by converting the nonholonomic model into the single-integrator equation. The resulting control ensures exponential convergence to the desired formation while the formation maneuvers according to a desired, time-varying translational velocity. In the third part, we extend the previous flocking control framework to the case where only a subset of the agents know the desired flocking velocity. The resulting controllers include distributed observers to estimate the unknown quantities. The theory of interconnected systems is used to analyze the stability of the observer-controller system