Tiago P. Nascimento

Multi-robot nonlinear model predictive formation control: Moving target and target absence

This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of targets absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed.

Perception-driven multi-robot formation control

Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the teams formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked targets cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

Formation control driven by cooperative object tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles. Formation control with dynamic formation geometry.Goal is to minimize the uncertainty about the cooperative observation of a target.Uncertainty term is part of a cost functional minimized by the formation geometry.Cooperative target estimator based on a particle filter.Simulated and real heterogeneous robot results (indoors and outdoors).

Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem

This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Intelligent state changing applied to multi-robot systems

The target searching problem is a situation where a formation of multi-robot systems is set to search for a target and converge towards it when it is found. This problem lies in the fact that the target is initially absent and the formation must search for it in the environment. During the target search, false targets may appear dragging the formation towards it. Therefore, in order to avoid the formation following a false target, this paper presents a new methodology using the Takagi-Sugeno type fuzzy automaton (TS-TFA) in the area of formation control to solve the target searching problem. The TS fuzzy system is used to change the formation through the modifications in the states of the automaton. This change does not only switch the rules and therefore the state of each robot, but also the controllers and cost functions. This approach amplifies the versatility of the formation of mobile robots in the target searching problem. In this paper, the TS-TFA is presented and its implications in the formation are explained. Simulations and results with real robot are presented where it can be noticed that the formation is broken to maximize the perception range based on each robots observation of a possible target. Finally this work is concluded in the last section.

Multi-Robot Systems Formation Control with Obstacle Avoidance

Abstract This paper deals with the problem of active target tracking with obstacle avoidance for multi-robot systems. A nonlinear model predictive formation control is presented which uses potential functions as terms of the cost function. These terms penalize the proximity with mates and obstacles, splitting the problem of obstacle avoidance into two repulse functions. Experimental results with real robots are presented to demonstrate the performance of the approach.

Verification of Embedded System Designs through Hardware-Software Co-Simulation

This work proposes an environment for real- time testing of heterogeneous embedded systems through co- simulation. The verification occurs on real-time between the system software and hardware platform using the High Level Architecture (HLA) as a middleware between the hardware device and the simulated model. The novelty of this approach is not only providing support for simulations, but also allowing the synchronous integration with any physical hardware devices. In this paper we use the Ptolemy framework as a simulation platform. The integration of HLA with Ptolemy and the hardware models open a vast set of applications, like the test of many devices at the same time, running the same, or different applications or modules, the usage of Ptolemy for real-time control of embedded systems and the distributed execution of different embedded devices for performance improvement or collaborative execution. A case study is presented to prove the concept, showing the successful integration between the Ptolemy framework with an implementation using Atmel and ARM microcontrollers.

Nonlinear Model Predictive Formation Control: An Iterative Weighted Tuning Approach

A nonlinear model predictive formation controller (NMPFC) was used to converge a group of middle sized mobile soccer robots towards a desired target using the concept of active target tracking. This paper presents a novel approach on the formation controller’s weight tuning in order to minimize an objective function that reflects the controller’s efficiency with respect to a given criteria. This method is here called Iterative Weight Tuning (IWT). In this paper the effectiveness from the proposed method is shown by the results of simulations and experiment with real robots, compared to the tuning performed using genetic algorithms approach. The results demonstrated that the IWT method was successful in achieving a better set of weights that influenced the formation controller to converge the robots into formation in a better fashion regarding the agents’ objective function.

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the Sl0 algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach.

Testing Real-Time Embedded Systems with Hardware-in-the-Loop Simulation Using High Level Architecture

This work presents a technique for testing real-time embedded systems using Hardware-in-the-Loop (HIL) simulation, exploiting High-Level Architecture (HLA) standard for interoperability and synchronization of heterogeneous architectures. The proposed testing approach uses the Ptolemy framework to verify in real-time models running in hardware against their respective reference models developed in Ptolemy. The approach consisted in the development of new actors in Ptolemy responsible for the integration with HLA and the verification process, and a software interface to be deployed in the hardware under verification. As proof of concept, the proposed approach was applied for the testing of a simple mobile robot navigation algorithm. All data collected by sensors and the respective reactions are transferred in real-time to Ptolemy, which performs the verification against a reference model. Such technique allows different Models of Computation (MoC) to be used as reference models in Ptolemy to verify different hardware architectures synchronously based on HLA.