The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Pre-tuned fuzzy inference architectures lack robustness under such unmodeled conditions. This work introduces an adaptive distributed technique for the autonomous control of flock systems. Its relatively flexible structure is based on online fuzzy reinforcement learning schemes which simultaneously target a number of objectives; namely, following a leader, avoiding collision, and reaching a flock velocity consensus. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature.

Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals. This enforces several difficult-to-solve constraints that are associated with the error dynamical equations using classical optimal tracking and adaptive control approaches. An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems with partial prior knowledge of the system dynamics. The actuation forces are decided using innovative forms of temporal difference equations relevant to the vessel's surge and angular velocities. The solution is implemented using an online value iteration process which is realized by employing means of the adaptive critics and gradient descent approaches. The adaptive learning mechanism exhibited well-functioning and interactive features in react to different desired reference-tracking scenarios.

Consequent to authors' previous work on developing a coverage control algorithm using multiple autonomous agents/robots, this paper focuses on implementing real-time simulations of the previously published coverage control algorithm using differential drive mobile robots. Mobile robots are deployed in a planar area subject to a suitable nonuniform scalar metric. In particular, we deploy multiple P3-DX differential drive mobile robots in a two-dimensional planar workspace to be covered where the non-uniform metric is defined by density function representing scalar measure. A platoon of P3-DX robots is spatially deployed in the workspace in such a way that the area coverage is maximized with respect the scalar field (density). The density in the workspace defines the importance of a point in that workspace. The implementation of the coverage optimization algorithm takes advantage of the Voronoi tessellation technique to partition the workspace. Each robot then moves towards the centroid of the partition (called herein Voronoi cell) it is responsible for. The robots are equipped with multiple sensors. Hence, the objective is to converge a set of P3-DX robots in a workspace such that the coverage is maximized taking into consideration the robots' sensory performance and nonuniform density defined throughout the workspace. The asymptotic convergence is validated by a set of computer simulations using a commercially available robot simulator that mimics a real scenario in an indoor workspace.

A typical firing doctrine is the Shoot-Look-Shoot tactic. In this tactic, the defence launches a salvo of interceptors against the targets (Shoot), assesses the outcomes of the engagements (Shoot-Look), and launches another salvo (Shoot-Look-Shoot) if time and the inventory of interceptors permit. In the open literature, it is often assumed that the targets are identical. This is not always true as targets come in with different ranges, speeds, sizes, cross sections etc. In this paper, we consider two types of targets. Each type of target has a different number engagement opportunities due to their ranges and speeds. Through the use of dynamic programming, a genetic algorithm, and a recursive generating function, we determine the probability of raid annihilation (the probability of neutralizing all of the targets) for two different Shoot-Look-Shoot (SLS) tactics. The first SLS tactic is based on variable size salvos and maximizes the probability of raid annihilation (PRA) for heterogeneous targets. The second SLS tactic is based on fixed-size salvos and is robust as it is independent of the number and types of targets. Theoretical results are validated through some computer simulations.