Rafael Fierro

A vision-based formation control framework

The invention relates to a rack for electronic plug-in units, comprising a backplane. The backplane comprises at least one connector to which a connector provided in the electronic plug-in unit connects when the plug-in unit is pushed into the rack. The backplane is attached to the rack with a fastener made of a resilient material. A moment arm is formed between a point which attaches the fastener to the rack and the backplane. When the plug-in unit is pushed into the rack, there is a tolerance for alignment of the connectors enabling connection of the connectors. Furthermore, when the plug-in unit is in the rack, the mobility of the backplane prevents the connectors and/or the backplane from breaking as the rack moves.

Agile Load Transportation : Safe and Efficient Load Manipulation with Aerial Robots

In the past few decades, unmanned aerial vehicles (UAVs) have become promising mobile platforms capable of navigating semiautonomously or autonomously in uncertain environments. The level of autonomy and the flexible technology of these flying robots have rapidly evolved, making it possible to coordinate teams of UAVs in a wide spectrum of tasks. These applications include search and rescue missions; disaster relief operations, such as forest fires [1]; and environmental monitoring and surveillance. In some of these tasks, UAVs work in coordination with other robots, as in robot-assisted inspection at sea [2]. Recently, radio-controlled UAVs carrying radiation sensors and video cameras were used to monitor, diagnose, and evaluate the situation at Japans Fukushima Daiichi nuclear plant facility [3].

Trajectory generation for swing-free maneuvers of a quadrotor with suspended payload: A dynamic programming approach

In this paper, we address the problem of agile swing-free trajectory tracking of a quadrotor with a suspended load. This problem has great practical significance in many UAV applications. However, it has received little attention in the literature so far. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with this problem, we propose a technique based on dynamic programming which ensures swing-free trajectory tracking. We start by presenting the mathematical model of a quadrotor with suspended load dynamics and kinematics. A high-level planner is used to provide desired waypoints, and then a dynamic programming approach is used to generate the swing-free trajectory for the quadrotor carrying a suspended load. Effectiveness of this method is demonstrated by numerical simulations and experiments.

Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodelled bounded disturbances and/or unstructured unmodelled dynamics in the vehicle.

Practical point stabilization of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.

Hybrid control for a class of underactuated mechanical systems

This paper considers a stabilizing hybrid scheme to control a class of underactuated mechanical systems. The hybrid controller consists of a collection of state feedback controllers plus a discrete-event supervisor. When the continuous-state hits a switching boundary, a new controller is applied to the plant. Lyapunov theory is used to determine the switching boundaries and to guarantee the stability of the closed-loop hybrid system. This approach is applied to the well-known swing up and balancing control problem of the inverted pendulum.

A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network

A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane. The sensing-pursuit problem considered in this paper is analogous to the Marco Polo game, in which the pursuer must capture multiple mobile targets that are sensed intermittently, and with very limited information. In this paper, the mobile sensor network consists of a set of robotic sensors that must track and capture mobile targets based on the information obtained through cooperative detections. Since the sensors are installed on robotic platforms and have limited range, the geometry of the platforms and of the sensors field-of- view play a key role in obstacle avoidance and target detection. Thus, a new cell decomposition approach is presented to formulate the probability of detection and the cost of operating the robots based on the geometric properties of the network. Numerical simulations verify the validity and flexibility of our methodology.

Adaptive Control of a Quadrotor with Dynamic Changes in the Center of Gravity

Abstract In this paper, we address the problem of quadrotor stabilization and trajectory tracking with dynamic changes in the quadrotors center of gravity. This problem has great practical significance in many UAV applications. However, it has received little attention in literature so far. In this paper, we present an adaptive tracking controller based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. Effectiveness and robustness of the proposed adaptive control scheme is verified through simulation results. The proposed controller is an important step towards developing the next generation of agile autonomous aerial vehicles. This control algorithm enables a quadrotor to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs.

A framework for hybrid control design

This paper presents a hybrid system framework which considers simultaneously the control and decision-making issues. This reconfigurable framework can accommodate a wide range of situations, from aircraft control systems to mobile manipulators. A continuous-state plant is supervised by a discrete-event system which is based on a theory of linked finite state machines. The composite system is viewed as an iterative process where a task is carried out by changing the structure of the continuous-state plant. An algorithm for a hybrid control design is provided and illustrated through a mobile manipulator example.

Learning swing-free trajectories for UAVs with a suspended load

Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are not only subject to unknown system dynamics, but also to specific task constraints. This paper presents a motion planning method for generating trajectories with minimal residual oscillations (swing-free) for rotorcraft carrying a suspended loads. We rely on a finite-sampling, batch reinforcement learning algorithm to train the system for a particular load. We find criteria that allow the trained agent to be transferred to a variety of models, state and action spaces and produce a number of different trajectories. Through a combination of simulations and experiments, we demonstrate that the inferred policy is robust to noise and the unmodeled dynamics of the system. The contributions of this work are 1) applying reinforcement learning to solve the problem of finding swing-free trajectories for rotorcraft, 2) designing a problem-specific feature vector for value function approximation, 3) giving sufficient conditions for successful learning transfer to different models, state and action spaces, and 4) verification of the resulting trajectories in both simulation and autonomous control of quadrotors with suspended loads.