We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally around an equilibrium under mild assumptions. The proposed strategy does not require solving the Hamilton–Jacobi–Bellman equation, i.e., a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the Hamilton–Jacobi–Bellman equation is replaced with an easy-solvable state-dependent Lyapunov matrix equation. We exploit a linear-like factorization of the underlying nonlinear system and a policy-iteration algorithm to yield a linear-like policy-iteration for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an asymptotically exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via four examples.

This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

The paper proposes a novel computing and net-working framework that can be implemented for the realization of different disaster management applications or real-time surveillance. The framework is based on networks of unmanned aerial vehicles (UAVs) equipped with different sensors including cameras. The framework represents a holistic approach that exploits the distributed architecture of clusters of UAVs and cloud computing resources located on the ground. The proposed framework is characterized by the hierarchical organization among framework elements. In such a framework, each UAV is assumed to be fully autonomous and locally implements a state-of-the-art deep learning algorithms for real-time route planning, obstacle avoidance and object detection on aerial images. The main operating modules of the proposed framework have been presented, with the emphasis on the improvements which the proposed framework can bring in terms of event detection time and accuracy, energy consumption and reliability of application in disaster management systems. The proposed framework can serve as the foundation for the development of more reliable, faster in terms of disaster event detection and energy-efficient disaster management systems based on UAV networks.

The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.

ABSTRACT Multirotor Aerial Vehicles may be fault-tolerant by design when rotor-failure is possible to measure or identify, especially when a large number of rotors are used. For instance, an octocopter can be capable to complete some missions even when a double-rotor fault occurs during the execution. In this paper, we study how a rotor-failure reduces the vehicle control admissible set and its importance with respect to the selected mission, i.e. we perform mission-related fault-tolerant analysis. Furthermore, we propose a risk-sensitive motion-planning algorithm capable to take into account the risks during the planning stage by means of mission-related fault-tolerant analysis. We show that the proposed approach is much less conservative in terms of selected performance measures than a conservative risk planner that assumes that the considered fault will certainly occur during the mission execution. As expected, the proposed risk-sensitive motion planner is also readier for accepting failures during the mission execution than the risk-insensitive approach that assumes no failure will occur.

We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally in general. The proposed strategy avoids solving the Hamilton-Jacobi-Bellman (HJB) equation, that is a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the HJB equation is replaced with an easy-solvable state- dependent Lyapunov matrix equation without introducing any approximation. We achieve this exploiting a linear-factorization of the underlying nonlinear system and a policy-iteration algorithm (PI) to yield a linear-like PI for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via two examples.

Side-scan sonar mapping of an unknown large-scale seafloor area by a marine vehicle is nowadays very common. It is also important that a-priori unknown interesting parts of the seafloor area are scanned in more detail, i.e. sonified from both sides. However, completely autonomous and time-efficient coverage path (re)planning for such missions is still an open issue. In contrast to the standard overlap-all-sonar-ranges lawnmower pattern offline static coverage problem solution for side-scan sonar missions, in this paper two online sonar data-driven coverage algorithms are proposed as extensions of authors’ prior work. Analytical upper and lower bounds on performance of the proposed coverage planning algorithms are given and validated through extensive mission parameters variation simulations. Statistical performance analysis of the proposed coverage planning algorithms’ performance shows significant complete coverage time efficiency improvements w.r.t. the classical unadaptive lawnmower approach. Also, a detailed comparison of coverage planning algorithms proposed by the authors so far is provided.

In this paper Failure Mode and Effects Analysis (FMEA) for a large scale multirotor systems (with moving mass) based on novel system for aircraft control will be presented. This system uses four petrol engines for lift and a moving mass system to control the vehicle. Analysis presented in this paper assesses the vulnerabilities of the system during the vehicle operation. The main objective of the analysis is to understand the cause and severity of the failures that can occur to the petrol engines and the moving mass system. Our unmanned aerial vehicle system is used for environmental monitoring and maritime security developed under MORUS project funded under NATO SPS Program. The ultimate goal of our research and design is to make an unmanned aerial vehicle that can lift larger amount of load (approximately 40kg). During its operation time the unmanned aerial vehicle might fail to complete a certain assignment so failure mode and effects analysis is needed to account for such problems and to find appropriate activities to reduce the overall risk the system faces during the mission.

Mapping an unknown large-scale marine area by a side-scan sonar onboard a marine vehicle as quickly as possible is often of great importance. It is also important that a-priori unknown interesting parts of the area are scanned in more detail, i.e. with the removal of sonic shadows. In contrast to the standard overlap-all-sonar-ranges lawnmower pattern, which is an offline static coverage problem solution for side-scan sonar missions, here a novel online side-scan sonar data-driven coverage solution is proposed. The proposed coverage algorithm provides a coverage solution based on local information gain from side-scan sonar data. At the same time, the solution is generated in such a way that coverage path length is minimized while covering the same area as the standard lawnmower. Upper and lower bounds of the proposed algorithm's improvement compared to the overlap-all-sonar-ranges lawnmower method are estimated analytically and validated through extensive mission parameters variation simulations. Simulation results show that our approach can cut down coverage path length significantly compared to the standard lawnmower method in most application cases.