Browse Topic: Trajectory control
Helicopters' Vertical Take-Off and Landing (VTOL) capabilities are essential for maritime operations, especially for small-deck naval vessels. Unmanned Aerial Vehicles (UAVs) offer a cheaper, expendable, and efficient alternative for certain tasks, such as reducing pilot risk and lowering fuel consumption. While the procedures to approach and land on (moving) ships are standardized and bound to established operational limits in the case of crewed helicopters, UAVs lack such guidelines. This study investigates optimal rotary-wing UAV approach trajectories to a moving ship, for varying wind conditions and relative initial positions, and for different objectives. The goal is to provide preliminary guidelines for maritime UAV recovery operations, and a preliminary estimation of performance-based operational limits. The optimal trajectories are obtained using a global path-performance optimization framework based on Optimal Control Theory. The trajectories are compared to each other and to reference cases using the Longest Common SubSequence (LCSS) similarity measure, revealing how the unmanned helicopter adjusts its path to exploit the wind direction and profile for more efficient ground speeds. The violation of performance and/or geometric constraints is used to preliminarily indicate the presence of operational boundaries. The control effort and energy consumption are used to identify optimal starting positions for the helicopter approach phase for a given wind profile and intensity.
This paper presents a distributed algorithm to track a desired target while fostering the emergence of a swarm formation and providing obstacle avoidance capability to deal with unknown scenarios. The proposed approach is based on the merge between a Flight Management System for global path planning and the definition of virtual forces through a custom Artificial Potential Field to prevent drones collisions between each other, with external objects and to provide cohesion of the swarm configuration. Each drone independently computes its global route and adjusts its path based on an optimal control action to minimize a potential energy function induced by its neighbors and obstacles. This approach results in a high cost-effective strategy to enhance UAVs autonomy level by managing a large group of drones, guaranteeing a low cost per unit thanks to the low computational effort and low-budget sensor suit while providing all the capabilities to accomplish the desired mission.
Complex vertical takeoff and landing configurations that transition between vertical and forward flight modes necessitate advanced flight control systems to substantially reduce pilot workload. Prior work demonstrated the Trajectory Control System, a flight control architecture that enables such Simplified Vehicle Operations. However, there may also be scenarios or applications that require more aggressive maneuvering with rates and attitudes that exceed the nominal envelope. This paper demonstrates a flight control architecture with a middle-loop that harmonizes the Trajectory Control System with a Tactical Maneuvering System that enables more aggressive maneuvering, with seamless in-flight transitions between the two. In both cases, the middle-loop is linked with an explicit model-following inner-loop control system. Flight test results for the Trajectory Control System and maneuver simulation results for the Tactical Maneuvering System are shown for a subscale tilt-wing configuration.
The ongoing development of numerous novel vertical takeoff and landing configurations necessitates flight control system design that enables the Simplified Vehicle Operations paradigm. This paper shows flight test results for one subscale lift-plus-cruise and one tilt-wing configuration employing such a flight control system architecture. Pilot inceptor inputs are used to synthesize trajectory commands that are processed by a full-envelope trajectory control system that generates propulsor thrust commands, a wing angle command, and attitude and rate commands for linear quadratic integral and explicit model-following inner-loop control systems. Commonalities and differences in the flight control implementation for the two configurations are highlighted. Results are shown for both configurations subject in manually piloted flights. The flight test results demonstrate that the flight control system designs allow a minimally trained operator to operate the two flight test vehicles safely and proficiently.
In this paper, an offline path planning module, which is capable of generating dynamically feasible 3D trajectories for a class of Vertical Takeoff and Landing (VTOL) vehicles is presented. Input to the module is a flight plan defined by a set of way-points and its output is twofold: first, it produces an improved flight plan introducing additional waypoints and speed changes based on the heuristics and dynamical constraints of the vehicle. This new plan facilitates the pilot by providing information on specific locations and changes of the original flight path. Second, it generates a set of reference points, which can be used as the initial set of inputs for an online reactive trajectory optimization algorithm. The proposed development is capable of processing both climbs and descents as well as both fly-by and flyover waypoints, and speed changes in between those way-points. The module was also designed to capture the pilot's perspective of an abstract way-point mission. NRC has implemented a nominal Nonlinear Model Predictive Control (NMPC) based online planner using the reference trajectories generated by the proposed offline planning module successfully. The generated improved mission was implemented on a simulator having Bell 412 flight dynamics and promising results were obtained.
The complex vertical takeoff and landing configurations currently under development necessitate flight control system design that enables substantial reductions of pilot workload through Simplified Vehicle Operations. This paper shows optimization and simulation of such a flight control system architecture for a subscale vectored thrust aircraft configuration. A full-envelope Trajectory Control System for longitudinal dynamics was coupled with explicit model-following inner-loop controllers, and a scheduled control allocation logic. Control system parameters were determined using a genetic algorithm optimization scheme subject to dynamic stability, robustness, and control responsiveness constraints. Flight simulation results for a series of representative maneuvers including departure and arrival transitions and forward flight maneuvers are presented to demonstrate the effectiveness of the proposed flight control system architecture.
This paper presents a path planning concept based on the Manned-Unmanned Teaming (MUM-T) between the helicopter and a drone. The drone flies ahead of the helicopter to detect possible unexpected obstacles in the mission area and sends the data to the helicopter. The path of the helicopter is automatically replanned to avoid the meteorological and physical obstacles detected by the drone. The path planning is based on the Rapidly-exploring Random Tree* (RRT*) and the Bidirectional Rapidly-exploring Random Tree (BiRRT) algorithms. The reference trajectory is planned by means of the RRT* algorithm and the replanning is performed with the BiRRT. The node connection is realized with the Dubins curves, that force the path to comply with the prescribed limitations on the helicopter's roll angle and flight path angle. The Savitzky-Golay filter is used to smooth the trajectory achieving curvature continuity. A closed-loop simulation model containing the dynamics of the pilot is used to evaluate the feasibility of a candidate trajectory basing on the pilot's workload. The pilot's dynamics is modeled through the frequency-based multi-axis Hess model. A workload assessment method is proposed basing on the aggression factor, which depends on the time histories of the control inputs. The workload quantitative assessment is carried out by comparison with the aggression factor on a "simple" manoeuvre, the spiral.
ABSTRACT Rotorcraft shipboard landing continues to be challenging due to increased pilot workload in dealing with effects of ship air wake turbulence on vehicle motion and random ship motion. Some of the recent work has proposed a pilot assist function for reduced pilot workload using model predictive control methods. This paper explores the use of a recently developed Model Predictive Path Integral (MPPI) method based on a stochastic optimal control framework for trajectory guidance solution to the shipboard landing problem. First, a proof-of-concept study is presented by applying the MPPI method to a simple point mass approximation of helicopter dynamics represented in the form of a first-order command acceleration model, representative of helicopter trajectory motion in the vertical plane. Next, the MPPI method is used in conjunction with a six degrees-of-freedom linear model of a helicopter in order to gain further insight into the applicability of the MPPI framework to the rotorcraft shipboard landing problem. The paper concludes with key observations and inferences gained in this study.
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This paper introduces a methodology for an optimization-based trajectory planner for the autonomous transition of a quadrotor biplane tailsitter (QRBP) between the flight modes of hover to forward flight and forward flight to hover. The trajectory planner uses a simplified first principles dynamic model of the QRBP in the formulation of a optimization problem for trajectory planning. Additional constraints on the trajectory are imposed based on physical limitations, such as available power, stall limits, among others. The cost function for the optimization problem is chosen to be the time-of-transition. The solution of this problem generates time-optimal state and input trajectories for transition. To validate the algorithm, the trajectories are tested on a flight dynamics simulation of a QRBP to demonstrate feasibility and tracking performance with an inner-loop PID feedback controller; and compared against trajectories generated from a heuristic approach. The results of the simulated tracking performance indicate the proposed trajectory planner is capable of generating feasible transition trajectories for the previously specified flight modes.
Autonomous flight remains a major challenge for Nano Unmanned Air Vehicles (NUAVs). This research presented a light-weight vision-based navigation system which is suitable for NUAV rotorcrafts with restrictively limited payload capacity and computing resources. A visual-inertia compensation approach was proposed to obtain accurate indoor navigation and guidance for the homemade NUAV which weighting less than 50 g. The proposed visual guidance algorithm have been successfully recognized targets such as windows, gates, and narrow-corridor with 25 Hz guidance command output. Flight experiments shown that the NUAV rotorcraft can follow the path planning to achieve autonomous flight, and guide through gates and windows within GPS-denied indoor environment.
A real-time path planning algorithm is developed to generate time-optimal trajectory for helicopter shipboard landing. The trajectory optimization problem is translated to the lower dimensional flat output space by exploiting the differential flatness property of the simplified helicopter model. Then, the flat outputs are parameterized using piecewise spline functions with adjustable coefficients, which are used to shape the trajectory and approximate the optimal solution. Further, by allowing the flexible selection of each spline segment's time-duration and enforcing additional path constraints, the time-optimality of the planned trajectory is largely preserved without violation of state and input bounds. Compared to pure temporal discretization methods, the proposed algorithm employs considerably less decision variables and significantly reduces the computational time by 75%, which only leads to a 0.5% growth in the optimal flight time as the trade-off. The improvement in computational efficiency enables the real-time recalculation of the time-optimal trajectories on-the-fly if there are unforeseen deviations from the planned flight path.
A comparison study between modeling approaches of a quadrotor biplane tailsitter aircraft is conducted. A blade element theory model with dynamic inflow is used to validate a reduced order model that incorperates a simple interference model for trajectory planning and dynamic simulation. With an appropriate interference model, the predicted power requirement through transition from hover to forward flight drops by 30-45% as the interference velocity reduces the effective angle of attack for the wing. A trajectory generation scheme is developed, which shows the importance of accurate stall modeling for the transition maneuver. Without interference modeling all transition trajectories are expected to violate the installed motor power limit or pass through an excessively stalled wing state (> 60%). The interference model dynamics are used to design a trajectory that avoids stall of the aircraft by adding a vertical climb element to the transition maneuver. A transition controller is linear dynamic inversion transition controller is described for inner loop stability over the entire flight regime.
ABSTRACT In this paper, we present a framework for collaborative uncertainty-aware navigation for swarms of vision based multirotor micro aerial vehicles (MAV). We assume that each MAV in a swarm is equipped with a forward-facing monocular camera, and that the vehicles are capable of using feature data to map the environment and perform vision based localization. Additionally, the vehicles are also capable of computing relative poses between each other in order to improve accuracy of pose estimation. For this scenario, we develop a navigation framework which seeks to improve the reconstructed maps and plan trajectories such that localization uncertainty is minimized. Within this framework, we first utilize an evolutionary algorithm that generates better viewpoints for the MAVs from which the map of the environment can be improved. This generated map is subsequently used as a source of information to perform path planning for each vehicle using a rapidly exploring random belief tree. This algorithm, while connecting start and goal poses with collision-free trajectories, ensures that the vehicles prioritize observing feature-rich areas and never lose sight of features, thus improving localization accuracy. Additionally, the algorithm is also capable of estimating when corrections would be required through relative poses and where these observations should be obtained, such that one vehicle can improve the accuracy of its neighbors. Through these approaches, we generate smooth, uncertainty-aware paths that are suitable for MAV navigation.
ABSTRACT This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other moving vehicles and measuring closest approach. We find that the UAVs not only avoid the collisions but also maintain a minimum distance specified. This method can be used in the future for trajectory planning of multiple aerial vehicles in dense airspaces.
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