Unmanned Systems

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

A waypoint constrained multi-phase nonlinear optimal guidance scheme is presented in this paper for the soft landing of a spacecraft on the Lunar surface by using the recently developed computationally efficient Generalized Model Predictive Static Programming (G-MPSP). The proposed guidance ensures that the spacecraft passes through two waypoints, which is a strong requirement to facilitate proper landing site detection by the on-board camera for mission safety. Constraints that are required at the waypoints as well as at the terminal point include position, velocity, and attitude of the spacecraft. In addition to successfully meeting these hard constraints, the G-MPSP guidance also minimizes the fuel consumption, which is a very good advantage. An optimal final time selection procedure is also presented in this paper to facilitate minimization of fuel requirement to the best extent possible. Extensive simulation studies have been carried out with various perturbations to illustrate the effectiveness of the algorithm. Finally, processor-in-loop simulation has been carried out, which demonstrates the feasibility of on-board implementation of the proposed guidance.

Small Unmanned Aircraft Systems have grown in autonomy and capability and continue to complement Department of Defense mission objectives. Teaming unmanned aircraft with manned vehicles can expand mission profiles and reduce risk to human life. To fully leverage unmanned systems, vehicles must be efficient and autonomous in path planning development. The work herein explores direct orthogonal collocation optimal control techniques combined with fast geometric path planning algorithms to reduce computation time and increase solution accuracy for small unmanned aircraft systems path planning missions. Previous work in the two-dimensional plane demonstrated a methodology to provide optimal flight paths through defined simplex corridors and simplified the optimal control parameter bounds by formulating the problem in the barycentric coordinate system. These methodologies are extended in this paper for three-dimensional flight and are solved with two different formulations for flight in an urban environment. The first formulation solves the constrained optimal control problem using a single phase while modeling the building constraints with superquadric functions. The second formulation implements the simplex methodology, eliminating polygonal constraints from the search domain, and solving the optimal path in a multiple phase approach. Results illustrate the benefits gained in computation time and accuracy when implementing simplex methods into the optimal control design and provide a foundation for closing the gap to real-time, onboard operations for unmanned vehicle path planning.

In this paper, we consider a distributed resource allocation problem with communication limitation. We propose a gradient-descent algorithm to solve the distributed resource allocation problem with quantization mechanism due to the communication limitations or in order to reduce the communication cost in the network. With carefully selected parameters, the convergence and correctness of the quantized algorithm can be obtained for fixed communication topologies and moreover, extended to switching jointly connected topologies. The exact optimal value can be obtained, which is different from some existing works, whose convergence accuracies were limited by the quantization bandwidth. In addition, the data rate of the proposed algorithm is also analyzed for both fixed and switching communication networks. Particularly, in the fixed topology case, we obtain that 1 bit is sufficient to solve the distributed problem with the proposed algorithm.

A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both either horizontal or vertical polarization. This paper investigates collaborative robotic tasks of unmanned systems in a network where the agents are entangled. For instance, a leader robot sends two identical photons (e.g. with vertical polarization) to two follower robots/autonomous vehicles to communicate information about various tasks such as swarm, formation, trajectory tracking, path following and collaborative tasks. The potential advantages of quantum cooperation of robotic agents is the speed of the process, the ability to achieve security with immunity against cyberattacks, and fault tolerance, through entanglement. If a Quantum Network is implemented in a robotic application, it would present an effective solution; for example, for a group of unmanned systems working securely together. An analytical basis of such systems is investigated in this paper, and the formulation of quantum cooperation of unmanned systems is presented and discussed. The concept of experimental quantum entanglement, as well as quantum cryptography (QC), for robotics applications is presented.