Pedro U. Lima

Feasible formations of multi-agent systems

Formations of multi-agent systems, such as satellites and aircraft, require that individual agents satisfy their kinematic equations while constantly maintaining inter-agent constraints. In this paper, we develop a systematic framework for studying formations of multiagent systems. In particular, we consider undirected formations for centralized formations and directed formations for decentralized formations. In each case, we determine differential geometric conditions that guarantee formation feasibility given the individual agent kinematics. Our framework also enables us to extract a smaller control system that describes the formation kinematics while maintaining,all formation constraints.

Motion feasibility of multi-agent formations

Formations of multi-agent systems, such as mobile robots, satellites and aircraft, require individual agents to satisfy their kinematic equations while constantly maintaining interagent constraints. In this paper, we develop a systematic framework for studying formation motion feasibility of multi-agent systems. In particular, we consider formations wherein all the agents cooperate to enforce the formation. We determine algebraic conditions that guarantee formation feasibility given the individual agent kinematics. Our framework also enables us to obtain lower dimensional control systems describing the group kinematics while maintaining all formation constraints.

The road to RoboCup 2050

The ultimate goal of the RoboCup initiative is stated as follows: by mid-21st century, a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup. The authors consider this goal from the perspective of how close we are to it and what has to be done to reach it.

Omni-directional catadioptric vision for soccer robots

Abstract This paper describes the design of a multi-part mirror catadioptric vision system and its use for self-localization and detection of relevant objects in soccer robots. The mirror and associated algorithms have been used in robots participating in the middle-size league of RoboCup — The World Cup of Soccer Robots.

A Localization Method for a Soccer Robot Using a Vision-Based Omni-Directional Sensor

In this paper, a method for robot self-localization based on a catadioptric omni-directional sensor is introduced. The method was designed to be applied to fully autonomous soccer robots participating in the middle-size league of RoboCup competitions. It uses natural landmarks of the soccer field, such as field lines and goals, as well as a priori knowledge of the field geometry, to determine the robot position and orientation with respect to a coordinate system whose location is known. The landmarks are processed from an image taken by an omni-directional vision system, based on a camera plus a convex mirror designed to obtain (by hardware) the ground plane birds eye view, thus preserving field geometry in the image. Results concerning the methods accuracy are presented.

Modeling and Optimal Centralized Control of a Large-Size Robotic Population

This paper describes an approach to the modeling and control of multiagent populations composed of a large number of agents. The complexity of population modeling is avoided by assuming a stochastic approach, under which the agent distribution over the state space is modeled. The dynamics of the state probability density functions is determined, and a control problem of maximizing the probability of robotic presence in a given region is introduced. The Minimum Principle for the optimal control of partial differential equations is exploited to solve this problem, and it is applied to the mission control of a simulated large robotic population

Petri Net Plans

Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration.

Design of Intelligent Control Systems Based on Hierarchical Stochastic Automata

Review on the design of intelligent control systems learning stochastic automata stochastic grammars hierarchical intelligent machines revistied a peformance measure for intelligent control systems the intelligent machine as a hierarchical stochastic automation design methodology and execution algorithm convergence rate and convergence acceleration of learning examples and cases studies conclusions and suggested future work.

Cooperative robot localization and target tracking based on least squares minimization

In this paper we address the problem of cooperative localization and target tracking with a team of moving robots. We model the problem as a least squares minimization problem and show that this problem can be efficiently solved using sparse optimization methods. To achieve this, we represent the problem as a graph, where the nodes are robot and target poses at individual time-steps and the edges are their relative measurements. Static landmarks at known position are used to define a common reference frame for the robots and the targets. In this way, we mitigate the risk of using measurements and state estimates more than once, since all the relative measurements are i.i.d. and no marginalization is performed. Experiments performed using a set of real robots show higher accuracy compared to a Kalman filter.

Compositional Abstractions of Hybrid Control Systems

Abstraction is a natural way to hierarchically decompose the analysis and design of hybrid systems. Given a hybrid control system and some desired properties, one extracts an abstracted system while preserving the properties of interest. Abstractions of purely discrete systems is a mature area, whereas abstractions of continuous systems is a recent activity. In this paper we present a framework for abstraction that applies to discrete, continuous, and hybrid systems. We introduce a composition operator that allows to build complex hybrid systems from simpler ones and show compatibility between abstractions and this compositional operator. Besides unifying the existing methodologies we also propose constructions to obtain abstractions of hybrid control systems.