Marc Lauzon

Course Correction Fuze Concept Analysis for In-Service 155 mm Spin-Stabilized Gunnery Projectiles

Guidance and control of artillery projectiles will be critical to future military operations. With the large quantities of unguided artillery shells stockpiled around the world, the course correction fuze could provide an attractive and cost-effective solution for munition control. This paper proposes a drag brake and a spin brake course correction fuze concept, and compares their performances against the roll-decoupled four canard configuration. Specific guidance and control functions were designed and tuned for each. The analysis was based on a typical 155 mm spin-stabilized artillery projectile. Dispersion sources included variations in muzzle velocity and gun’s azimuth and elevation relative to nominal conditions, and wind velocity perturbations. Monte Carlo simulations were performed to analyze the delivery accuracy. Results show that the drag brake concept compensates for muzzle velocity and longitudinal wind perturbations efficiently. The spin brake concept compensates for perturbations in lateral wind efficiently and, to a lesser extent, in gun’s azimuth. The rolldecoupled four canard configuration counteracts gun’s azimuth and elevation perturbations very well. A course correction fuze combining the drag brake and spin brake concepts is shown as a good solution to increase the projectile accuracy when all disturbances studied are present.

A Decision Policy for the Routing and Munitions Management of Multiformations of Unmanned Combat Vehicles in Adversarial Urban Environments

The control and management of unmanned combat vehicles (UCVs) operating in an adversarial urban environment is a challenging task due, in part, to the imperfect and incomplete information available, the conflicting objectives of opposing teams, the uncertain stochastic dynamics, and the limitation in computational capability. In this paper, a decision policy built upon Markov decision processes is proposed to provide optimal routing and munitions management despite the conflicting objectives of the adversaries and the stochastic dynamics. The main novelty of the proposed decision policy lies in its handling of multiple UCV formations of varying dimensions. This multiformations capability is explicitly accounted for in the proposed formulation of the optimization problem. The UCVs, which constitute the blue team, have for objective to reach prescribed tactical target locations from a common starting point by following possibly different paths across an adversarial urban environment, within prescribed time windows and with maximum lethality. On their way, the UCVs will face an adversarial red team, which is composed of ground units that can engage any nearby UCV. The rendezvous objective of the blue team can be interpreted as a constraint in an optimization problem, aimed at minimizing damage while maximizing the total number of remaining munitions at the time the multiformations reach the targets. The blue and red teams play the roles of cost-function minimizer and maximizer, respectively. The worst-case minimization objective of the blue team is formulated as a finite-time optimization, which is solved by means of a dynamic programming equation with value function evolving according to a graph of feasible UCV paths. The resulting decision policy takes the form of a lookup table, which is ideal for online implementations. The practical case of imperfect information on the classification and the location of the adversarial ground units is addressed by means of a one-step lookahead rollout policy using estimates provided by a recursive Bayesian filter. Simulation results show that the concept of multiformations provides, on average, an improvement in performance when compared with single-formation routing.

Maneuverability Analysis of the Conventional 155 mm Gunnery Projectile

The feasibility of guiding the in-service 155 mm conventional indirect fire spinning projectiles was evaluated by developing an algorithm solution for the guidance and control and proving its applicability in an analytical study. Unguided projectiles are stockpiled in large quantities around the world, so a retrofitting solution to add a guidance and control capability is an attractive option. However, there was a concern that it would be difficult to guide an originally unguided projectile which was designed for a ballistic flight. So, the existence of a solution in terms of capable guidance and control algorithms was explored to perform a maneuverability analysis using an analytical approach based on modeling and simulation. The concept studied was a 155 mm equipped with a roll-decoupled course correction fuze providing corrections in both azimuth and elevation planes. This course correction fuze replaces the existing fuze with one containing the sensors, computer and actuators, all integrated in a small form factor. The results show that guidance and control of this projectile is possible at the higher airspeeds, specifically above Mach 1, in harsh launch and environmental conditions. However, at the lower airspeeds, below Mach 1, the airframe input-output interaction becomes predominant and the projectile dynamic response becomes too slow for effective guidance and control.

Receding Horizons with Heading Constraints for Collision Avoidance

This paper shows an application of model predictive control with receding horizons for the cooperative control of unmanned aerial vehicles in unknown environments. Line of sight and range constrain the perceived environment. It is shown that the receding horizons can be divided into temporal and spatial horizons. The cooperative control problem investigated in this paper uses short spatial horizons and it is decentralized. A method is proposed and evaluated for collision avoidance between vehicles and with obstacles. Sample scenarios show the effectiveness of the proposed collision avoidance algorithm. Finally, it is shown that model predictive control with receding horizons can handle task allocation, path planning and trajectory generation in one completely unified method.

Heading and position receding horizon control for trajectory generation

This paper presents a comparative study of two formulations of model predictive control with receding horizons for the cooperative control of a team of unmanned aerial vehicles. In the first formulation, the vehicle trajectories are solved dynamically as sequences of vehicle headings over prediction horizons and executed over shorter action horizons. This formulation takes advantage of an implementation of collision avoidance based on vehicle heading constraints. In the second formulation, the vehicle trajectories are solved as sequences of vehicle positions, rather than vehicle headings. This formulation handles collision avoidance with vehicle position constraints. An efficient branch-and-bound algorithm is proposed to support the mixed integer constraints, and a collision avoidance solution based on heading constraints is evaluated. This paper shows that both receding horizon formulations produce exactly the same vehicle trajectories when they are used without collision avoidance constraint. It is shown however that heading receding horizon control requires less computing power than position receding horizon control whether in situations of collision avoidance or not.

A Distributed Network Enabled Weapon-Target Assignment for Combat Formations

A distributed decision-making capability resulting in near-optimal weapon-target assignments for formations of unmanned combat vehicles is proposed. The decision-making is based on a modified version of the cross-entropy method distributed over the formations. For the formations to agree about a single consistent target assignment, a new consensus algorithm is proposed so that exact agreement can be reached in finite time through a communications graph that is at least weakly connected. The decision-making algorithm enables the blue-team combat vehicles to engage, or visit, an ordered sequence of targets while grouping into formations of varying dimensions on their way to the sites. The additional degree of freedom in the formulation of the optimization problem allows mitigating the risks of destruction of the combat vehicles when facing hostile red units. The weapon-target assignment aims at maximizing a global utility function that expresses the overall weapons effects. Constraints on the autonomy of each formation is taken into account in the formulation of the optimization problem. The engagement dynamics is represented by means of an attrition model. It is shown through numerical simulations that the proposed weapon-target assignment outperforms, in specific cases, the solution to a travel salesman problem, where all the vehicles are grouped into a single formation. The numerical simulations also suggest that the performance of the proposed algorithm is dependent on the number of formations per blue team, and on the number of vehicles per formation.

A Networked Decision and Information System for increased agility in teaming unmanned combat vehicles

A networked decision and information system (NDIS) architecture is proposed for the routing and munitions management (RMM) of multiple unmanned combat vehicles (UCVs) evolving in an imperfectly known and adversarial environment. Increased UCV teaming agility is evidenced by the online decision policy proposed in this paper. The policy exploits the multiformations capability of UCVs to group into large formations and to divide into smaller formations on their way to high-value, tactical targets. The NDIS architecture is composed of two principal components: (i) a sensory information management network (SIM-Net), which handles data from a set of mobile sensors and then determines whether sensor redundancy should and can be used, and estimates an information state vector on the locations of the adversarial ground units and decoys; (ii) the networked UCVs calculate the worst-case minimization policy for the RMM problem based on the available information state vector. NDIS adopts a distributed one-step lookahead approach, thereby enabling time-constrained approximations of the expected cost-to-go function.

A dual-network health state estimator and decision policy for unmanned combat teams

We propose a one-step lookahead rollout policy in closed-loop with a health state estimator to ensure effective cooperation among unmanned combat teams despite intermittent wireless communications breakdowns. To ensure effective cooperation despite network faults, the proposed scheme relies on dual networks. On the one hand, a sensory information management network (SIM-Net) provides the most probable distribution on the location and classification of the adversarial ground units by fusing mobile sensor measurements obtained by a team of surveillance vehicles. On the other hand, a routing and munitions management network (RMM-Net) enables unmanned combat vehicle (UCV) communications, which are required for their effective path planning and for the distribution of the rollout decision policy over the formations. Simulation results demonstrate the effectiveness of the proposed health state estimator and decision policy.

Network enabled sensing for unmanned urban combats

Summary form only given. The routing and munitions management of multiple formations of unmanned combat aerial vehicles (UCAVs) through network enabled sensing will be discussed. The ability of the UCAVs to group into large formations and to divide into smaller formations on their way to high-value, tactical targets will be cast in the optimization. To solve such problem a sensory information management (SIM) network will handle data from a set of mobile sensors. SIM will determine whether sensor redundancy should and can be used, before it estimates an information state vector on the locations of the adversarial ground units and decoys. The networked UCAVs will calculate the worst-case minimization policy for an effective routing and munitions management relying on the SIM-net supplied information state vector. As in any time-critical control problem, closing the loop requires that sensor management, estimation, and high-level decision be accomplished within hard real-time constraints. Heuristic algorithms aiming at achieving real-time performance will be discussed. The effectiveness of UCAV routing and munitions management will be illustrated by means of examples featuring trajectory planning of multiple formations evolving in a dynamic urban theatre.

UAV autonomy for complex environments

Complexity is a dominant, multi-dimensional attribute of the battlespace, and is evident in the geography, manmade infrastructure, force asymmetry and organizational processes. The Unmanned Aerial Vehicle represents a strategic enabler for military operations in complex environments by providing a flexible means of acquiring real-time information and deriving actionable knowledge. Limitations arising from remotely piloted UAV operation together with the desired operational flexibility in complex environments both dictate the need for increasingly autonomous UAV operation within a rigorous airspace integration framework. UAV autonomy relies primarily on access to missioncritical information from on-board sensors and networked datalink, together with comprehensive, efficient and robust algorithms for decisions on course of action. Global battlefield networking extends the notion of individual vehicle operation to a coordinated team, whose members carry out complementary and/or redundant tasks. DRDC research on cooperative teaming of UAVs covers in particular the development and implementation of cooperative control based on model predictive control. In the context of operations in complex environments, the present paper discusses the selected approach to cooperative control, and presents applications to formation flight, collision avoidance, real-time implementation and multi-processing, and fault-detection, isolation and recovery.