Abstract
The automation of internal logistics and inventory-related tasks is one of the main challenges of modern-day manufacturing corporations since it allows a more effective application of their human resources. Nowadays, Autonomous Mobile Robots (AMR) are state of the art technologies for such applications due to their great adaptability in dynamic environments, replacing more traditional solutions such as Automated Guided Vehicles (AGV), which are quite limited in terms of flexibility and require expensive facility updates for their installation. The application of Artificial Intelligence (AI) to increase AMRs capabilities has been contributing for the development of more sophisticated and efficient robots. Nevertheless, multi-robot coordination and cooperation for solving complex tasks is still a hot research line with increasing interest. This work proposes a Multi-Agent System for coordinating multiple TIAGo robots in tasks related to the manufacturing ecosystem such as the transportation and dispatching of raw materials, finished products and tools. Furthermore, the system is showcased in a realistic simulation using both Gazebo and Robot Operating System (ROS).

Abstract
The electric vehicle (EV), when aggregated by an agent (Aggregator), is a suitable candidate for participating in demand response in power system operation. As the interface between distribution network and EV users, as well as an independent party at the same time, an optimal scheduling algorithm is necessary with consideration of benefits of three parties, which in return will affect aggregators' sustainable development. The benefits of distribution system from demand response, aggregator and EV users are defined in this paper. EV users' benefit is described by their satisfaction on SOCs reached after a given period of time and overall costs/revenues for charging/discharging and policy award/penalty, while the benefit of distribution network for the integration of large amount EV loads through aggregator is evaluated by aggregator's load shifting capability through a pricebased demand response (DR) program under real time electricity price. The optimal scheduling of the aggregator is with an objective of maximizing its own benefit under constraints of EV users' minimum satisfaction and minimum load-shifting capability required by distribution network. The optimization scheduling is tested by a test system, and further analysis is given on the effect of aggregator's facility level and technology (Vehicle to Vehicle) and the operation mode of aggregator group on the benefits of three parties.

Abstract
This paper presents a study on the complexity of cargo arrangements in the pallet loading problem. Due to the diversity of perspectives that have been presented in the literature, complexity is one of the least studied practical constraints. In this work, we aim to refine and propose a new set of metrics to measure the complexity of an arrangement of cargo in a pallet. The parameters are validated using statistical methods, such as principal component analysis and multiple linear regression, using data retrieved from the company logistics. Our tests show that the number of boxes was the main variable responsible for explaining complexity in the pallet loading problem.

Abstract
Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.

Abstract
Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution-for the automotive industry-to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.

Abstract
Operating isolated power systems with increasing shares of renewable energy sources requires the integration of battery energy storage systems in order to assure enhanced frequency regulation capabilities. The control mode of power converters interfacing battery energy storage systems to the grid can be based on grid-forming type structures given its superior performance with respect to the mitigation of network frequency disturbances. Nevertheless, in case of network faults, the interactions between existing synchronous units and the grid-forming type converters may adversely affect the global system behavior. Therefore, this paper addresses the study-case of a MW-scale isolated power system with large shares of converter-interfaced renewable generation, operating with both synchronous machines and a grid-forming type power converter. An optimal grid-forming control parameter tuning procedure considering different disturbances is presented, aiming to reduce the associated battery energy storage system power regulating effort following the disturbances. Moreover, it is proposed and discussed the need of a novel rule-based adaptive control solution to switch between different sets of control parameters used in grid-forming type converters depending on the network status following a fault-type disturbance. Extensive numerical simulations performed over different operating scenarios illustrate the performance of the proposed solution.