Agile fixed-wing aircraft integrate the efficient, high-speed capabilities of conventional fixed-wing platforms with the extreme maneuverability of rotorcraft. This work presents a nonlinear control strategy that harnesses these capabilities to enable autonomous flight through aggressive, time-constrained, three-dimensional trajectories. The cascaded control structure consists of two parts; an inner attitude control loop developed on the Special Orthornormal group that avoids singularities commonly associated with other parametrizations, and an outer position control loop that jointly determines the thrust command and attitude references by implementing a novel vector-projection algorithm. The objective of the algorithm is to decouple roll from the reference attitude to ensure that thrust and lift forces can always be pointed such that position errors converge to zero. The proposed control system represents a single, unified solution that remains effective throughout the aircraft's flight envelope, including aerobatic operation. Controller performance is verified through simulations and experimental flight tests; results show the unified control scheme is capable of performing a wide range of operations that would normally require multiple, single-purpose controllers, and their associated switching logic.

This paper presents the modeling and control of a passively-coupled tilt-rotor vertical takeoff and landing aircraft. The aircraft consists of a quadrotor frame attached to a fixed-wing aircraft by an unactuated hinged mechanism. The platform is capable of smooth transitions from hover to forward flight without the use of tilting actuators. The transition from hover to forward flight is made possible by differential thrust between the fore and aft propellers of the quadrotor frame. In this paper, the coupled dynamics between the quadrotor frame and the aircraft frame are modeled as a constrained multi-body system. The equations of motion are established using a constrained Lagrangian approach and the model developed is used to build a realistic simulation environment for control design purpose. A cascaded control architecture based on P/PID controllers is proposed to achieve inner-loop attitude, height and forward velocity control. Simulated and experimental results are obtained with a close match for hover, transitions, forward flight, and banked turn maneuvers.

Unmanned aerial vehicles (UAVs) are increasingly being proposed for a wide range of applications. A promising new class of these vehicles, known as agile fixed-wing UAV s, is intended to bridge the gap between conventional fixed-wing aircraft, which can cover long distances efficiently, and rotorcraft, which are typically very maneuverable. This paper addresses the implementation of a controller for agile UAVs, beginning with a hardware-in-the-loop (HIL) simulator, followed by testing on a real platform, both implemented on the Pixhawk microcontroller. We replace the Xplane physics engine used in the standard Pixhawk HIL with our own in-house Matlab/Simulink high-fidelity simulation of an agile UA V. The HIL simulator is found to provide substantial advantages in the transition from pure simulation to experimental testing. Once the controller is integrated into the flight platform, flight tests are conducted, and the results of those tests are compared to those from the HIL simulation and those obtained from the pure simulation environment, for maneuvers including hover, aggressive turnaround, knife-edge, and rolling Harrier. The desired position and orientation time histories were successfully tracked with the proposed implementation, demonstrating the impressive autonomous maneuverability that can be achieved by this type of aircraft.

Unmanned aerial vehicles (UAVs) have become popular in a wide range of applications, including many military and civilian uses. State of the art control strategies for these vehicles are typically limited to a portion of the vehicle's flight envelope, and are tailored to a specific type of platform. This article presents a single physics-based controller capable of aggressive maneuvering for the majority of UAVs. The controller is applicable to UAVs with the ability to apply a force along a body-fixed direction, and a moment about an arbitrary axis, which includes UAVs such as multi-copters, conventional fixed-wing, agile fixed-wing, flying-wing with two thrusters, most tailsitters, and some tilt-rotor/wing platforms. We demonstrate autonomous flight for a quadrotor and agile fixed-wing aircraft in a simulation environment. To specifically demonstrate the extreme maneuvering capability of the control logic, we perform a rolling flip with the quadrotor and an aggressive turnaround with the fixed-wing aircraft, all using a single controller with a single set of gains.

A motion planner is developed for guiding a small aerobatic fixed-wing unmanned aerial vehicle to a desired goal region in a highly constrained, three-dimensional, known environment with static obstacles. The planner is based on the Rapidly-Exploring Random Trees (RRT) algorithm, and pieces together feasible trajectories from a library of motion primitives. Among other more conventional motion primitives, the library includes three extreme maneuvers: a cruise-to-hover transition, a hover-to-cruise transition, and an aggressive turn-around. The algorithm is efficient; it can be run in real-time to rapidly generate a plan starting from the aircraft's configuration at run-time. The motion planner is closely coupled to a feedback controller. Simulations using an aircraft dynamics model demonstrate the effectiveness of the system to guide and control the aircraft to a desired goal region. Preliminary flight test results are also presented.

Underwater operations present unique challenges and opportunities for robotic applications. These can be attributed in part to limited sensing capabilities, and to locomotion behaviours requiring control schemes adapted to specific tasks or changes in the environment. From enhancing teleoperation procedures, to providing high-level instruction, all the way to fully autonomous operations, enabling autonomous capabilities is fundamental for the successful deployment of underwater robots. This paper presents an overview of the approaches used during underwater sea trials in the coral reefs of Barbados, for two amphibious mobile robots and a set of underwater sensor nodes. We present control mechanisms used for maintaining a preset trajectory during enhanced teleoperations and discuss their experimental results. This is followed by a discussion on amphibious data gathering experiments conducted on the beach. We then present a tetherless underwater communication approach based on pure vision for high-level control of an underwater vehicle. Finally the construction details together with preliminary results from a set of distributed underwater sensor nodes are outlined.

Determining the minimum distance between convex objects is a problem that has been solved using many different approaches. On the other hand, computing the minimum distance between combinations of convex and concave objects is known to be a more complicated problem. Most methods propose to partition the concave object into convex subobjects and then solve the convex problem between all possible subobject combinations. This can add a large computational expense to the solution of the minimum distance problem. In this paper, an optimization-based approach is used to solve the concave problem without the need for partitioning concave objects into convex pieces. Since the optimization problem is no longer unimodal (i.e., has more than one local minimum point), global optimization techniques are used. Simulated Annealing (SA) and Genetic Algorithms (GAs) are used to solve the concave minimum distance problem. In order to reduce the computational expense, it is proposed to replace the objects' geometry by a set of points on the surface of each body. This reduces the problem to an unconstrained combinatorial optimization problem, where the combination of points (one on the surface of each body) that minimizes the distance will be the solution. Additionally, if the surface points are set as the nodes of a surface mesh, it is possible to accelerate the convergence of the global optimization algorithm by using a hill-climbing local optimization algorithm. Some examples using these novel approaches are presented.

Determining the minimum distance between two convex objects is a problem that has been solved using many different approaches. On the other hand, computing the minimum distance between combinations of convex and concave objects is known to be a more complicated problem. Some methods propose to partition the concave object into convex sub-objects and then solve the convex problem between all possible sub-object combinations. While this method has been shown to work reliably, it adds a large computational expense when the concave objects in the scene are complicated, or when a quadratically bound object is to be linearized. An optimization approach is used to solve the concave problem without the need for partitioning the concave object into convex sub-objects. Since the optimization problem is no longer unimodal, a global optimization technique is used. Simulated annealing is used to solve the concave problem. In order to reduce the computational expense, it is proposed to replace the objects' geometry by a set of points on the surface of each body. This reduces the problem to a combinatorial problem where the combination of points that minimizes the distance will be the solution. Some examples using this method are presented.

A variety of definitions of the workspace of a mechanism can be found in the literature. In this paper, the dexterous workspace is defined as a subspace of the reachable workspace having an adequate performance of a dexterity measure, e.g. the condition number of the Jacobian matrix. The dexterous workspace of a 3-DOF parallel mechanism is analyzed. In order to study the changes of the workspace volume as the architectural parameters are changed, several plots of the dexterous workspace are presented. The shape, size and symmetry of the workspace is found to be very dependent on the particular set of architectural parameters chosen for the mechanism.

The real-time control of cooperating manipulators, mechanical hands, and walking machines involves the optimization of an underdetermined force system subject to both equality and inequality constraints. The inequality constraints arise as a result of passive frictional contacts in systems that depend on these for force transmission or when taking into account the limited torque or force capability of the actuators. Since the results of the force optimization are used to provide force or torque setpoints for the actuators, they must be obtained in real time. A technique for solving a quadratic optimization problem with equality and inequality constraints is presented for this application. The technique is compared with linear programming schemes to show its superior performance in terms of the speed and quality of the solution. The technique is well suited to problems where the solution can change discontinuously in time, as is the case with the systems considered due to changes in their topology.>

Robotic systems whose kinematic structure varies during their task are discussed. When the system's kinematic structure changes large discontinuities may occur in the solution to its inverse dynamics problem. These discontinuities can be traced to discontinuous changes in the coefficients of the dynamics equations which describe the system's behavior. When this change occurs on the left-hand side of the equations, it corresponds to a change in the kinematic structure of the system. When the change occurs on the right-hand side, it corresponds to a collision between two constituent objects in the system. Since these systems are redundantly actuated, the solution to their inverse dynamics problem may be chosen according to an optimality criterion. Two methods are presented to specify the optimization problem to reduce the effect of the discontinuities-one introduces rate-dependent terms in the objective function while the other positively limits the rate of change of the solution.>

The real-time control of cooperating manipulators, mechanical hands, and walking machines involves the optimization of an underdetermined force system subject to both equality and inequality constraints. The inequality constraints arise as a result of passive frictional contacts in systems that depend on these for force transmission, or when taking into account the limited torque or force capability of the actuators. Since the results of the force optimization are used to provide force or torque setpoints to the actuators, they must be obtained in real time. A technique is presented for solving a quadratic optimization problem with equality and inequality constraints for this application. The technique is compared with linear programming to show its superior performance in terms of speed and the quality of the solution.>

The authors establish why redundant actuation situations arise, the relationship of redundant actuation to time-varying topologies, and the desirability of redundant actuation. Various techniques are then outlined for solving the optimal force distribution problem, to obtain force setpoints for the controller in real time. Although direct substitutions is by far the fastest of these, its implementation requires a careful monitoring of numerical degeneracies. Solution of the problem using orthogonal decomposition is recommended as it provides the opportunity to introduce inequality constraints, which are important in many problems. An effective technique is introduced for smoothing the discontinuities in the optimal solution, which result from the discontinuous topology.>