In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.

This paper presents a multi-layer scheme for the adaptive coordinated cooperative control of n mobile manipulator. Each layer works as an independent module, dealing with a specific part of the problem of cooperation, coordination and adaptation, thus giving more flexibility to the scheme. Also, the redundancy of the n mobile manipulators is used for the avoidance of obstacles by the mobile platforms-without deforming the virtual structure and maintaining its desired trajectory- and the singular configuration prevention through the system's manipulability control. Stability and robustness are proved by using Lyapunov's theory. Simulation results show a good performance of the proposed control scheme as proved by the theoretical design.

This paper proposes a solution to the problem of path-following by a formation of mobile robots (three robots are considered, forming a triangle on the ground). The proposed solution allows planning the motion of the desired formation without needing specifying how each robot should move. The proposed strategy is based on the cluster space approach, and embeds a reactive method to give the formation the capability of avoiding obstacles during the path-following. Theoretical analysis and experimental results are presented, which demonstrate the effectiveness of the proposed control system to guide the multi-robot formation to accomplish a path-following task.

This paper presents a multi-layer scheme for the adaptive cooperative coordinated control of mobile manipulator robots. Each layer works as an independent module, dealing with a specific part of the problem of cooperation, coordination and adaptation, thus giving more flexibility to the scheme. The adaptive dynamic compensation layer is capable of updating the estimated parameters for all robots, which are directly related to physical parameters of each mobile manipulator. Also, the redundancy of the mobile manipulators is used for the avoidance of obstacles by the mobile platforms - without deforming the virtual structure and maintaining its desired trajectory - and the singular configuration prevention through the system's manipulability control. Stability and robustness are proved by using Lyapunov's method. Simulation results show a good performance of the proposed multi-layer scheme as proved by the theoretical design.

This paper proposes a new reactive method to avoid obstacles during trajectory tracking, considering unicycle mobile robots navigating in semi-structured environments, which can be used in association with any trajectory tracking controller. A meaningful improvement in the obstacle avoidance process is achieved analyzing the layout of the obstacles in relation to the planned trajectory. Experimental results show that the evasion algorithm is fairly efficient, reducing the time spent to avoid obstacles and shortening the path traveled by the robot during such procedure as well.

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. Incremental positions of the end effector are related to incremental positions of encoders of the motors of the robot. A kinematic model of the robot is modified in order to take into account possible errors of kinematic parameters. The solution of the model utilizes incremental positions of the resolvers and end effector, the new parameters minimizes errors in the kinematic equations. Spherical properties and intrinsic camera parameters are utilized to model sphere projection in order to improve spatial measurements. The robot system is designed to carry out tracking tasks and the calibration of the system is finally validated by means of integrating the errors of the visual controller.

This paper proposes a solution to a door crossing problem in unknown environments for an autonomous wheelchair. The problem is solved by a dynamic path planning algorithm implementation based on successive frontier points determination. An adaptive trajectory tracking control based on the dynamic model is implemented on the vehicle to direct the wheelchair motion along the path in a smooth movement. An EKF feature-based SLAM is also implemented on the vehicle which gives an estimate of the wheelchair pose inside the environment. The SLAM allows the map reconstruction of the environment for future safe navigation purposes. The entire system is evaluated in a real time simulator of a robotic wheelchair.

In this work a robot cooperation strategy based on omnidirectional vision is presented. Such strategy will be applied to a mobile robot team formed by small and simple robots and a bigger leader robot with more computational power. The leader must control team formation. It has an omnidirectional camera and sees the other robots. Color segmentation and Kalman filtering is used to obtain the pose of the followers. This information is then used by a nonlinear stable controller to manage team formation. Simulations and some preliminary experiments were run. The current results are interesting and encourage towards the next steps.

This paper presents a switching controller for positioning a unicycle-like mobile robot at a desired point with final orientation avoiding obstacles in completely unknown environments. To this aim two complementary algorithms are included: the first decides whether to avoid an obstacle around its right or left side, and the second is intended to detect when an obstacle was successfully avoided. The obstacle avoidance is performed using a laser-based reactive contour-following controller. The switching controllers include the stability analysis at the switching times, using common and multiple Lyapunov functions. Finally, experimental results in a typical unicycle-like mobile robot show the performance of the proposed hybrid control system.

This work presents an approach to the abstract mathematical analysis of the evolution of a species. A mathematical model is proposed as well that characterizes the natural evolution phenomena of a species. On the basis of this model, the convergence of a new computational evolutionary algorithm is developed and proven, that is applied to minimizing (or maximizing) a costs function that meets certain requirements. Simulation studies are carried out in order to validate the theoretical aspects and to evaluate the performance of the evolutionary algorithm.

In this paper a switching controller for mobile robots is presented that allows the contour-following of objects the size of which is at least comparable to that of the robot. The controller uses the information from distance sensors and is based on a wall-following controller described in the paper. The stability of the overall switching system is proved by using a common Lyapunov function. Experimental results in a Pioneer III unicycle-like mobile robot are provided to show the feasibility of the proposed control strategy

This work presents a new approach to calculate the optical flow, which uses the decentralized information filter to accomplish a data fusion step. The proposal consists in taking some least mean square estimates of the optical flow vector associated to a certain region of the image considered and to fuse them in order to get a more accurate final estimate of such a vector. Besides this, the way the initial least mean square estimates are obtained is also modified, by reducing the number of observations used to get each one of them, in order to make the overall algorithm faster. The idea is that such initial estimates can be rougher, once the fusion engine improves the accuracy of the final estimate, in the sense that the decentralized information filter generates a final estimate whose variance is lower than the smallest variance associated to the initial estimates.

This paper proposes an alternative approach to address the problem of coordinating behaviors in mobile robot navigation: fusion of control signals. Such approach is based on a set of two decentralized information filters, which accomplish the data fusion involved. Besides these two fusion engines, control architectures designed according to this approach also embed a set of different controllers that generate reference signals for the robot linear and angular speeds. Such signals are delivered to the two decentralized information filters, which estimate suitable overall reference signals for the robot linear and angular speeds, respectively. Thus, the background for designing such control architectures is provided by the nonlinear systems theory, which makes this approach different from any other yet proposed. This background also allows checking control architectures designed according to the proposed approach for stability. Such analysis is carried out in the paper, and shows that the robot always reaches its final destination, in spite of either obstacles along its path or the environment layout. As an example, a control architecture is designed to guide a mobile robot in an experiment, whose results allows checking the good performance of the control architecture and validating the design approach proposed as well.

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

We propose a tracking adaptive impedance controller for robots with visual feedback. It is based on a generalized impedance concept where the sensed distance is introduced as a fictitious force to the control in order to avoid obstacles in restricted motion tasks. The controller is designed to compensate for full nonlinear robot dynamics. Robot parameters adjustment is introduced to reduce the sensuality of the controller design to dynamic uncertainties of the robot and the manipulated load. It is proved that the vision control errors are ultimately bounded in the image coordinate system. Simulations are carried out to evaluate the controller performance.

In this paper, an adaptive controller for robot manipulators which uses neural networks is presented. The proposed control scheme is based on PD feedback plus a feedforward compensation of full robot dynamics. The feedforward signal is obtained by summing up the weighted outputs of a set of fixed multilayer neural nets. The controller is adaptive to robot dynamics and payload uncertainties. A stability analysis which takes into account neural network learning errors is included. Simulation results showing the feasibility and performance of the approach are given.>

Petri nets have been proven to be specially adequate for modeling parallel and distributed systems such as flexible manufacturing systems. Moreover, they have a well-founded theory of analysis that allows for investigating a great number of qualitative properties of the modelled systems. In order to validate complex production system models with this tool, this work proposes a formal technique for qualitative validation of the system's specifications, based on the place-invariants of its coloured Petri net models. One example taken from the literature on FMSs is modelled by coloured Petri nets and analyzed by the proposed formal validation techniques.>

Impedance control specifications for robot manipulators are given in terms of a desired motion trajectory and a desired dynamic relationship between position errors and interaction forces. An adaptive implementation is proposed as an alternative to reduce the design sensitivity due to model-manipulator parameter mismatch. Two adaptive controllers that globally achieve the impedance objective for the general nonlinear dynamic model are presented. The controller structures consist of a nonlinear feedback of positions, velocities, and end-effector applied forces. Computer simulations were carried out to demonstrate stability and performance control.>