D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

Visual active tracking is a growing research topic in robotics due to its key role in applications such as human assistance, disaster recovery, and surveillance. In contrast to passive tracking, active tracking approaches combine vision and control capabilities to detect and actively track the target. Most of the work in this area focuses on ground robots, while the very few contributions on aerial platforms still pose important design constraints that limit their applicability. To overcome these limitations, in this paper we propose D-VAT, a novel end-to-end visual active tracking methodology based on deep reinforcement learning that is tailored to micro aerial vehicle platforms. The D-VAT agent computes the vehicle thrust and angular velocity commands needed to track the target by directly processing monocular camera measurements. We show that the proposed approach allows for precise and collision-free tracking operations, outperforming different state-of-the-art baselines on simulated environments which differ significantly from those encountered during training

Exploring Deep Reinforcement Learning for Robust Target Tracking using Micro Aerial Vehicles

The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control scheme based on deep reinforcement learning for controlling a micro aerial vehicle to persistently track a flying target while maintaining visual contact. The proposed method leverages relative position data for control, relaxing the assumption of having access to full state information which is typical of related approaches in literature. Moreover, we exploit classical robustness indicators in the learning process through domain randomization to increase the robustness of the learned policy. Experimental results validate the proposed approach for target tracking, demonstrating high performance and robustness with respect to mass mismatches and control delays. The resulting nonlinear controller significantly outperforms a standard model-based design in numerous off-nominal scenarios

Optimal Low-Thrust Orbit Transfers Made Easy: A Direct Approach

The optimization of low-thrust, multi-revolution orbit transfer trajectories is often regarded as a difficult problem in modern astrodynamics. In this paper, a flexible and computationally efficient approach is presented for the optimization of low-thrust orbit transfers under eclipse constraints. The proposed approach leverages a new dynamic model of the orbital motion and a Lyapunov-based initial guess generation scheme that is very easy to tune. A multi-objective, single-phase formulation of the optimal control problem is devised, which provides a convenient way to trade off fuel consumption and time of flight. A distinctive feature of such a formulation is that it requires no prior information about the structure of the optimal solution. Simulation results for two benchmark orbit transfer scenarios indicate that minimum-time, minimum-fuel and mixed time/fuel-optimal instances of the control problem can be readily solved via direct collocation, while incurring a significantly lower computational demand with respect to existing techniques

Learning-Based Parameter Optimization for a Class of Orbital Tracking Control Laws

This paper presents a learning algorithm for tuning the parameters of a family of stabilizing nonlinear controllers for orbital tracking, in order to minimize a cost function which combines convergence time and fuel consumption. The main feature of the proposed approach is that it achieves performance optimization while guaranteeing closed-loop stability of the resulting controller. This property is exploited also to restrict the class of admissible controllers and hence to expedite the training process. The learning algorithm is tested on three case studies: two different orbital transfers and a rendezvous mission. Numerical simulations show that the learned control parameters lead to a significant improvement of the considered performance measure

The Applicability of Pulsed Plasma Thrusters to Rendezvous and Docking of Cubesats

Despite the interest in formation flying and cubesats over something like the last decade or so, close controlled formation flying of two cubesat size spacecraft has not been achieved. Similarly, despite the growing interest in propulsion systems for cubesats, there are very few commercially available and flight ready systems. If these are coupled with the realization that formation flying could be an enabling technology for cubesats [1] enhancing capabilities and opening up possibilities for new types of missions, it seemed timely to revisit the problem of rendezvous and docking for cubesats to examine what might be possible with a pulsed propulsion system based on a pulsed plasma thruster(PPT) that is close to flight qualification The approach taken was to build on previous wor

Minimum switching limit cycle oscillations for systems of coupled double integrators

In this paper, we study the limit cycle oscillations of multiple double integrators with coupled dynamics, subject to a constant disturbance term and switching inputs. Such systems arise in a variety of control problems where the minimization of both fuel and number of input transitions is a key requirement. The problem of finding the minimum switching limit cycle, among all the fuel-optimal solutions satisfying given state constraints, is addressed. Starting from well known results available for a single double integrator, two suboptimal solutions are provided for the multivariable case. First, an analytic upper bound on the number of input switchings is derived. Then, a less conservative numerical solution exploiting the additional degrees of freedom provided by the phases of the limit cycles is presented. The proposed techniques are compared on two simulation examples

Time-Optimal Control of a Multidimensional Integrator Chain With Applications

International audienceThis letter studies the time-optimal control problem for a chain of multidimensional integrators subject to convex state and input constraints. It is shown that, by performing a suitable change of variables, this problem can be cast as a single convex program for certain realizations of the boundary conditions. For general realizations, the problem is nonconvex. A constructive procedure based on the solution of a sequence of convex problems is proposed in order to approach asymptotically a global optimum. The proposed approach is demonstrated on three applications involving aerial and space vehicle systems

Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target

In this paper, the trajectory planning problem for autonomous rendezvous and docking between a controlled spacecraft and a tumbling target is addressed. The use of a variable planning horizon is proposed in order to construct an appropriate maneuver plan, within an optimization-based framework. The involved optimization problem is nonconvex and features nonlinear constraints. The main contribution is to show that such problem can be tackled effectively by solving a finite number of linear programs. To this aim, a specifically conceived horizon search algorithm is employed in combination with a polytopic constraint approximation technique. The resulting guidance scheme provides the ability to identify favorable docking configurations, by exploiting the time-varying nature of the optimization problem endpoint. Simulation results involving the capture of the nonoperational EnviSat spacecraft indicate that the method is able to generate optimal trajectories at a fraction of the computational cost incurred by a state-of-the-art nonlinear solver

Minimum switching control for systems of coupled double integrators

This paper studies the minimum switching control problem for a system of coupled double integrators with on–off input signals, in the presence of a constant disturbance term. This type of problem is relevant to a variety of applications in which the number of transitions of on–off actuators must be minimized, in order to prevent actuator wear. Two solutions are presented in terms of steady state limit cycles. The first one provides an analytic upper bound to the maximum number of transitions per input signal. The second solution exploits the relative phases of the trajectories of the state variables, thus providing a less conservative upper bound. Additionally, a control law is presented, which steers the system in finite time to the previously derived limit cycles. The proposed techniques are demonstrated on a spacecraft attitude control application