F. Lobo Pereira

Bio: F. Lobo Pereira is an academic researcher from University of Porto. The author has contributed to research in topics: Optimal control & Mobile robot. The author has an hindex of 9, co-authored 38 publications receiving 307 citations. Previous affiliations of F. Lobo Pereira include Faculdade de Engenharia da Universidade do Porto.

Development and implementation of a low-cost LBL navigation system for an AUV

Abstract: A reliable navigation system is a key factor for the success of an operational mission with an AUV in a real scenario. We address the main issues involved in the implementation of a long baseline (LBL) navigation system for a REMUS AUV. This system replaces both the original hardware and software of the vehicle with a simpler, faster, less expensive and more precise system, based on a Kalman filter. We also discuss the influence of transponder location in the overall performance of the LBL navigation, and present results obtained with this new system in operational missions.

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

Abstract: Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low-cost aerial surveillance and mapping applications in remote regions. There is, however, a key limitation, which is the fact that low-cost UAVs have limited fuel capacity and, hence, require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in the fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in three-dimensional taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach.

Necessary conditions for impulsive nonlinear optimal control problems without a priori normality assumptions

Abstract: First-order and second-order necessary conditions of optimality for an impulsive control problem that remain informative for abnormal control processes are presented and derived. One of the main features of these conditions is that no a priori normality assumptions are required. This feature follows from the fact that these conditions rely on an extremal principle which is proved for an abstract minimization problem with equality constraints, inequality constraints, and constraints given by an inclusion in a convex cone. Two simple examples illustrate the power of the main result.

Invariance for impulsive control systems

Abstract: The conventional concepts of invariance are extended in this article to include impulsive control systems represented by measure driven differential inclusions. Invariance conditions and some of their main features are derived. The solution concept plays a critical role in the extension of the conditions for conventional problems to the impulsive control context.

Coordinated Control of Networked Vehicles: An Autonomous Underwater System

Abstract: The specification and design of coordinated control strategies for networked vehicle systems are discussed. The discussion is illustrated with an example of the coordinated operation of two teams of autonomous underwater vehicles collecting data to find the local minimum of a given oceanographic scalar field.

Role of optimal control theory in cancer chemotherapy.

Abstract: This paper presents a review of the ways in which optimal control theory interacts with cancer chemotherapy. There are three broad areas of investigation. One involves miscellaneous growth kinetic models, the second involves cell cycle models, and the third is a classification of "other models." Both normal and tumor cell populations are included in a number of the models. The concepts of deterministic optimal control theory are applied to each model in such a way as to present a cohesive picture. There are applications to both experimental and clinical tumors. Suggestions for designing better chemotherapy strategies are presented.

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform

Abstract: The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Abstract: The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

Coordinated Path Following for Time-Critical Missions of Multiple UAVs via L1 Adaptive Output Feedback Controllers

Abstract: This paper develops a complete framework for coordinated control of multiple unmanned air vehicles (UAVs) that are tasked to execute collision-free maneuvers under strict spatial and temporal constraints in restricted airspace. The framework proposed includes strategies for deconicted real-time path generation, nonlinear path following, and multiple vehicle coordination. Path following relies on the augmentation of existing autopilots with L1 adaptive output feedback control laws to obtain inner-outer loop control structures with guaranteed performance. Multiple vehicle coordination is achieved by enforcing temporal constraints on the speed proles of the vehicles along their paths in response to information exchanged over a communication network. Again, L1 adaptive control is used to yield an inner-outer loop structure for vehicle coordination. A rigorous proof of stability and performance bounds of the combined path following and coordination strategies is given. Flight test results obtained at Camp Roberts, CA in 2007 demonstrate the benets of using L1 adaptive control for path following of a single vehicle. Hardware-in-the-loop simulations for two vehicles are discussed and provide a proof of concept for time-critical coordination of multiple vehicles over communication networks with xed topologies.

Learning and detecting emergent behavior in networks of cardiac myocytes

Abstract: We address the problem of specifying and detecting emergent behavior in networks of cardiac myocytes, spiral electric waves in particular, a precursor to atrial and ventricular fibrillation. To solve this problem we: (1) apply discrete mode abstraction to the cycle-linear hybrid automata (CLHA) we have recently developed for modeling the behavior of myocyte networks; (2) introduce the new concept of spatial superposition of CLHA modes; (3) develop a new spatial logic, based on spatial superposition, for specifying emergent behavior; (4) devise a new method for learning the formulae of this logic from the spatial patterns under investigation; and (5) apply bounded model checking to detect the onset of spiral waves. We have implemented our methodology as the EMERALD tool suite, a component of our EHA framework for specification, simulation, analysis, and control of excitable hybrid automata. We illustrate the effectiveness of our approach by applying EMERALD to the scalar electrical fields produced by our CELLEXCITE simulation environment for excitable-cell networks.