Unmanned Surface Vehicles (USVs) are nowadays used in various applications for security, inspection and delivery among others. To operate in dynamic and complex environments efficiently demands an optimal path planning where multiple factors should be taken into account. In this paper, the multi-objective path planning problem of USV is formulated aiming to minimize the traveled distance maximizing in parallel the trajectory smoothness and energy efficiency. To address this multi-objective path planning problem with contradicting terms, the popular Ant Colony Optimization (ACO) algorithm is employed enhanced with the proposed Fuzzy Pareto framework. In particular, ACO is used to solve the problem by finding the Pareto solutions optimizing each single objective. Then these solutions are evaluated via the Mamdani fuzzy inference system to identify the optimal one. The ranking of the solutions is based on the defuzzification values. A case study is performed in a simulation area based on Saronic Gulf topology. The results showed that depending the needs of an operation and the conditions of the area of operations (number of obstacles, currents, and distance from the initial to the target point), each objective can impact the path quality differently.

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.

Autonomy of underwater vehicles has become an imperative feature due to increasingly challenging deep sea mission scenarios. In particular, for trajectory-tracking problems of Autonomous Underwater Vehicles (AUVs), the use of Lyapunov theory tools in state-of-the-art methods is common practice. These often require special assumptions, according to the application considered, and ‘intuition’ for the choice of a control law, which often leads to conservative results. This article suggests a systematic analysis for the horizontal motion of an AUV which ensures global asymptotic stability for the closed loop system. A nonlinear underactuated AUV system is considered with linear and angular velocity constraints. The Takagi–Sugeno (TS) framework design is adopted for the representation of the original nonlinear system. The control law is synthesised using the standard parallel distributed compensation (PDC) control law structure and stability is guaranteed for the closed loop system. The design criteria are posed as linear matrix inequalities (LMIs) where sufficient conditions for the design of the control law are shown. The proposed approach can be easily adopted for different types of autonomous vehicles with minor modifications.