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Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.

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The recent development of new offshore projects in pre-salt deepwater fields has placed offshore loading operations as the main production outflow alternative, increasing the operational complexity and risks. Numerous dangerous situations are associated with oil offloading, such as the messenger line transfer during the mooring stage. Nowadays, this critical task is realized by launching a thin messenger cable using the pneumatic line throwing apparatus. This is a complex and slow process since the operation usually occurs with the ship opposite to the wind. This work proposes a hybrid flight methodology based on computer vision and sensor fusion techniques for autonomous unmanned aerial vehicles (UAVs). The UAV takes off from an oil rig and precisely reaches a specific point in the shuttle tanker without using expensive positioning devices and augmenting UAV’s orientation (yaw) precision since the compass can suffer from severe interference due to naval metallic structures near the vehicle. The proposed framework was tested in a realistic simulated environment considering several practical operational constraints. The results demonstrated both the robustness and efficiency of the methodology.

SUMMARYIn recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported.

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Abstract-Almost all luminaires that are used in street lighting system does not have communication, control and management infrastructure. Moreover, the artificial lighting system is responsible for the consumption of approximately 30% of all electricity generated in the world. Within this context, the LED technology offers flexibility and high efficiency, being suitable to be connected with smart devices, allowing the usage of possible alternatives to reduce energy consumption and providing significant economic saving. This work aims to perform an economic analysis of a controllable device with smart grid features applied to LED street lighting system in order to evaluate the efficiency of its usage as well as possible benefits on public lighting system and monitoring of low voltage grid parameters. The results have shown low payback periods and a promising internal return rate when compared with other applications, such as saving accounts. The smart grid features on the controller allows simple integration with renewable sources and central management and controlling lighting system.

Mobile robotic systems are used in a wide range of applications. Especially in the assistive field, they can enhance the mobility of the elderly and disable people. Modern robotic technologies have been implemented in wheelchairs to give them intelligence. Thus, by equipping wheelchairs with intelligent algorithms, controllers, and sensors, it is possible to share the wheelchair control between the user and the autonomous system. The present research proposes a methodology for intelligent wheelchairs based on head movements and vector fields. In this work, the user indicates where to go, and the system performs obstacle avoidance and planning. The focus is developing an assistive technology for people with quadriplegia that presents partial movements, such as the shoulder and neck musculature. The developed system uses shared control of velocity. It employs a depth camera to recognize obstacles in the environment and an inertial measurement unit (IMU) sensor to recognize the desired movement pattern measuring the user’s head inclination. The proposed methodology computes a repulsive vector field and works to increase maneuverability and safety. Thus, global localization and mapping are unnecessary. The results were evaluated by simulated models and practical tests using a Pioneer-P3DX differential robot to show the system’s applicability.