Khalid Al-Ali

Polymorphic Control Reconfiguration in an Autonomous UAV with UGV Collaboration

The emergence of distributed technologies as a reliable infrastructure for real-time control is enabling a new generation of distributed plug-and-play control architectures and methodologies; increasingly common are control systems that pass real-time data across traditional system boundaries to utilize distributed remote sensing, processing, and actuation. The polymorphic control systems (PCS) project formalizes constructs that permits topological reconfiguration of control systems that span multiple heterogeneous systems and multiple communication mediums, towards the goal of control coordination and strategy optimization in a multi-system environment, increased resilience to failure and uncertainty, increased overall and individual performance, and better utilization of available resources. This paper presents the concepts behind PCS, and presents results from a flight test experiment involving distributed reconfiguration of an autonomous landing controller in a collaborative multi-vehicle environment. These flight test experiments demonstrate one of the goals of polymorphic reconfiguration: providing emergency assistance and collaborative coordination between multiple systems to achieve safely the mission critical objectives, where a system failure would have resulted in the loss of the aircraft.

Topological Constructs for Automatic Reconfiguration of Polymorphic Control Systems

The emergence of distributed technologies as a reliable infrastructure for real-time control is enabling a new generation of distributed plug-and-play control architectures and methodologies; increasingly common are control system formulations configured across traditional system boundaries that utilize distributed remote sensing, processing, and actuation. The formalization of topological control constructs that permit autonomous reconfiguration in an unrestrained topology over distributed system architectures to enhance the level of resilience and adaptation demonstrated by composite systems has the promise of providing controllers that demonstrate increased resilience to failure and uncertainty, and increased performance by better utilization of available resources. This formalization enables the realization of control structures that give rise to polymorphic control systems: such systems can fundamentally restructure in a timely manner to optimize control system topologies that share resources across multiple heterogeneous systems with disparate individual system goals to take advantage of temporal topological possibilities, restructuring accordingly as the composite systems evolve over time and the availability of resources change. Autonomous reconfiguration would increase system resilience to component-failure, enable systems to exploit temporary configuration possibilities to decrease objective costs, and provide adaptability under the face of uncertainty. This paper introduces a topological formulation to model the domain of distributed plug-and-play control systems and demonstrates its application to an autonomous multi-vehicle control problem.

Polymorphic Control of an Autonomous Ground Vehicle over Wireless Mobile Networks

Adapting existing autonomous vehicle platforms with hardware to operate in new environments can be expensive and risky; an alternative approach is to enable cooperative control of remote resources with entities concurrently operating in the environment. Polymorphic Control Systems research investigates highly dynamic control structures that can automatically reconfigure across vehicles and to share remote sensors, actuators, and other resources available throughout the system of vehicles. In this paper, we investigate extending this framework to a smart space environment, allowing a poorly instrumented resource constrained autonomous ground vehicle to navigate a complex indoor environment by coordinating with concurrently operating building control system agents. We develop a complex simulation of the environment, develop polymorphic control laws that restructure the control system over multiple entities during operation, and present a trajectory generation approach concurrently addresses topological optimization and dynamic control optimization of the collaborative control structure. Our results demonstrate viability of solving the two-part polymorphic control optimization problem utilizing pseudo-optimizing solvers that trade optimality for feasibility and real-time performance, and show that control polymorphism provides robust resource sharing between agents in a smart building infrastructure.

Vision Aided Inertial Navigation with Measurement Delay for Fixed-Wing Unmanned Aerial Vehicle Landing

Usually, standard inertial navigation unit (INU) with global positioning system (GPS) provides relatively poor accuracy in altitude estimation, while autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation. In this paper, a UAV navigation system with aid from an external camera for landing is investigated. This paper presents: (i) a sensor fusion algorithm for passive monocular vision and INU based on the extended Kalman filter (EKF) considering measurement delay to improve the accuracy of position estimates, and (ii) a robust object-detection vision algorithm using optical flow. Pilot controlled landing experiments on a NASA UAV platform and the filter simulations validate the feasibility and performance of the proposed approach.

Modeling Error Driven Robot Control

In this paper, we present a modeling error driven adaptive controller for control of a robot with unknown dynamics. In general, modeling error is not used since the ideal parameters are not known. However, using a feedback linearization approach we show that the modeling error can be obtained by a measured quantity representing the error dynamics under the ideal conditions, that is, the case for which the robot parameters are known a priori. We show that using this approach, the learning dynamics and plant dynamics are effectively decoupled and can then be analyzed separately. We present simulation examples of the 2-link manipulator that illustrates the algorithm.

Towards Autonomous Fixed-Wing Unmanned Aerial Vehicle Landing: A Vision-Aided Inertial Navigation under Sensor Reconfiguration Scenario

Abstract While autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation, the standard inertial navigation unit (INU, the inertial measurement unit with a global positioning system (GPS)) provides relatively poor accuracy in altitude estimation. A common solution for this problem is to aid the INU with additional sensors and/or ground infrastructures, but the main hurdles to the approach are the limited payload of UAVs and extra cost involved. Dynamic sensor reconfiguration can be a good alternative by constructing a new sensor system utilizing available sensors around without adding new sensory equipment to UAVs. In this paper, a sensor reconfiguration scenario for autonomous fixed-wing UAV landing is considered and the resulting vision-aided inertial navigation system is investigated. This paper presents (i) a sensor fusion algorithm for a passive monocular camera and an INU based on the Extended Kalman Filter (EKF), and (ii) an object-detection vision algorithm using optical flow. The EKF is chosen to take care of the nonlinearities in the vision system, and the optical flow is used to robustly detect the UAV from noisy background. Pilot-controlled landing experiments on a NASA UAV platform and the filter simulations were performed to validate the feasibility of the proposed approach. Promising results were obtained showing 50%-80% error reduction in altitude estimation.

Adaptive inner-loop rover control

Adaptive control technology is developed for the inner-loop speed and steering control of the MAX Rover. MAX, a CMU developed Rover, is a compact low-cost 4-wheel drive, 4-wheel steer (double Ackerman), with high-clearance agile durable chassis. It is outfitted with sensors and electronics that make it ideally suited for supporting research relevant to intelligent teleoperation, and as a low-cost autonomous robotic test bed and appliance. The control design consists of a feedback linearization based controller with a proportional-integral (PI) feedback that is augmented by an online adaptive neural network. The adaptation law has guaranteed stability properties for safe operation. The control design is retrofit in nature so that it fits below the outer-loop path planning algorithms. Successful hardware implementation of the controller is illustrated for several scenarios consisting of actuator failures and modeling errors in the nominal design