André Scolari Conceição

Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot

This paper presents and discusses the implementation results of a model-predictive control (MPC) scheme with friction compensation applied to trajectory following of an omnidirectional three-wheeled robot. A cascade structure is used with an inverse kinematics block to generate the velocity references given to the predictive controller. Part of the control effort is used to compensate for the effects of static friction, allowing the use of efficient algorithms for linear MPC with constraints. Experimental results show that the proposed strategy is efficient in compensating for frictional effects as well as for tracking predefined trajectories.

Practical Approach of Modeling and Parameters Estimation for Omnidirectional Mobile Robots

This paper presents a nonlinear modeling approach for omnidirectional mobile robots. Three experimental methods of estimation of the parameters related to dynamic equations of the robots model are developed. The estimation methods are based on least-squares methods to obtain the viscous friction coefficients, the coulomb friction coefficients, and the moment of inertia of the robot. Simulation results and real results of navigation are provided to demonstrate the performance of the proposed modeling approach.

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).

Model Predictive Control based on LMIs Applied to an Omni-Directional Mobile Robot

Abstract The paper presents a methodology for state feedback MPC synthesis applied to the trajectory tracking control problem of a three wheeled omnidirectional mobile robot. The MPC design used here is based on a cost function developed over finite horizon and LMI framework. It is shown that MPC concepts well established for robot applications, for instance, the use of open loop predictions, receding horizon control, constraints manipulation, are preserved in this new formulation. The stability of the closed loop system is guaranteed by LMI conditions related with the cost function monotonicity. Simulation results of navigation are provided to demonstrate the performance of the proposed control strategy.

TRAJECTORY TRACKING FOR OMNI-DIRECTIONAL MOBILE ROBOTS BASED ON RESTRICTIONS OF THE MOTOR'S VELOCITIES

Abstract In this paper, we propose an algorithm that combine the restriction on motors velocities and the kinematic model of Omni-Directional mobile robots to improve the trajectorys following. The algorithm verifies the reference velocities of the robot and redefine them if necessary, in order to prevent possible saturation on motors velocities. Simulation results of the algorithm applied to an omnidirectional mobile robot are presented.

Modeling and friction estimation for wheeled omnidirectional mobile robots

In this study, a model for wheeled mobile robots that includes a static friction model in the force balance at the robots center of mass is presented. Additionally, a least-squares method to linearly combine functions is proposed to estimate the friction coefficients. The experimental and simulation results are discussed to demonstrate the effectiveness of this approach in indoor environments for two floor types.

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.