Control Systems Engineering

The role of fractional calculus in modeling biological phenomena: A review

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot technologies utilized in cultivation fields, which transform traditional farming practices into a new era of precision agriculture. In this paper, we perform a survey of the last research on IoT and UAV technology applied in agriculture. We describe the main principles of IoT technology, including intelligent sensors, IoT sensor types, networks and protocols used in agriculture, as well as IoT applications and solutions in smart farming. Moreover, we present the role of UAV technology in smart agriculture, by analyzing the applications of UAVs in various scenarios, including irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. Furthermore, the utilization of UAV systems in complex agricultural environments is also analyzed. Our conclusion is that IoT and UAV are two of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.

Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Heuristic approaches in robot path planning

Display Omitted A novel hierarchical global path planning approach for mobile robots in a cluttered environment.A proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle.Providing optimal global robot paths with computational efficiency. In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.

/pdf/a-hierarchical-global-path-planning-approach-for-mobile-3v3z7u8dt3.pdf

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment

This paper proposes a solution to real-time collision-free path planning for an AR. Drone 2.0 UAV using only on-board visual and inertial sensing. The proposed solution consists in a modified potential field method to overcome the non-reachable goal problem. The approach comprises three key components: pattern-based ground for localization, proposed potential field method for path planning and PD controllers for steering commands. By applying the proposed method, the quadrotor is successful in avoiding known/unknown obstacles and reaching the target in complex indoor environment. The results demonstrate the feasibility of the proposed strategy, which opens new possibilities for the agent to perform autonomous navigation.

Improved potential field method for unknown obstacle avoidance using UAV in indoor environment

This paper describes the process of identification and closed-loop control of an Parrot AR.Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications.

Cosmin Copot

Papers

Heuristic approaches in robot path planning

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment

Improved potential field method for unknown obstacle avoidance using UAV in indoor environment

Identification and path following control of an AR.Drone quadrotor