Yerba mate: From waste to activated carbon for supercapacitors

Developing technological solutions that use yerba mate waste as precursors is key to reducing the environmental impact caused by the lack of treatment and its accumulation in landfills. Due to their physicochemical properties, these residues can be used to develop activated carbons. Activated carbon is a versatile material with a high surface area that can be used for energy storage. In this work, yerba mate residues were valued by producing chemically activated carbon to be used as electrode material in supercapacitors. Activated carbons were developed through chemical activation in two steps with KOH. Variables such as impregnation ratio and activation temperature are studied. The developed carbons were characterized by physicochemical and electrochemical techniques. They were found to have high surface areas, up to 1800 m² g^-1, with a hierarchical porous distribution. A maximum specific capacitance of 644 F g^-1 at 0.1 A g^-1, and power values of ca 32,000 W kg^-1, at 33 A g^-1 were found. All the synthesized carbons have excellent electrochemical properties and are suitable for use as active material in supercapacitors.

ECOPAMPA: A new tool for automatic fish schools detection and assessment from echo data

Accurate identification of aquatic organisms and their numerical abundance calculation using echo detection techniques remains a great challenge for marine researchers. A software architecture for echo data processing is presented in this article. Within it, it is discussed how to obtain energetic, morphometric and bathymetric fish school descriptors to accurately identify different fish-species. To accomplish this task it was necessary to have a development platform that allowed reading echo data from a particular echosounder, to detect fish aggregations and then to calculate fish school descriptors that would be used for fish-species identification, in an automatic way. This article also describes thoroughly the digital processing algorithms for this automatic detection and classification, as well as the automatic process required for surface and bottom line detection, which is necessary to determine the exploration range. These algorithms are implemented within the ECOPAMPA software, which is the first Argentinean system for marine species identification. Finally, a comparative result over experimental data of ECOPAMPA against Echoview^TM Software Pty Ltd (formerly Myriax Software Pty Ltd), is carefully examined.

An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots

Intelligent control systems are being developed for the control of plants with complex dynamics. However, the simplicity of the PID (proportional-integrative-derivative) controller makes it still widely used in industrial applications and robotics. This paper proposes an intelligent control system based on a deep reinforcement learning approach for self-adaptive multiple PID controllers for mobile robots. The proposed hybrid control strategy uses an actor-critic structure and it only receives low-level dynamic information as input and simultaneously estimates the multiple parameters or gains of the PID controllers. The proposed approach was tested in several simulated environments and in a real time robotic platform showing the feasibility of the approach for the low-level control of mobile robots. From the simulation and experimental results, our proposed approach demonstrated that it can be of aid by providing with behavior that can compensate or even adapt to changes in the uncertain environments providing a model free unsupervised solution. Also, a comparative study against other adaptive methods for multiple PIDs tuning is presented, showing a successful performance of the approach.

How simple autonomous decisions evolve into robust behaviours? A review from neurorobotics, cognitive, self-organized and artificial immune systems fields

Researchers in diverse fields, such as in neuroscience, systems biology and autonomous robotics, have been intrigued by the origin and mechanisms for biological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organisms and physical robots to exemplify how the process of natural selection can lead to the evolution of robustness by means of adaptive behaviors.

Evaluation of an Efficient Approach for Target Tracking from Acoustic Imagery for the Perception System of an Autonomous Underwater Vehicle

This article describes the core algorithms of the perception system to be included within an autonomous underwater vehicle (AUV). This perception system is based on the acoustic data acquired from side scan sonar (SSS). These data should be processed in an efficient time, so that the perception system is able to detect and recognize a predefined target. This detection and recognition outcome is therefore an important piece of knowledge for the AUVs dynamic mission planner (DMP). Effectively, the DMP should propose different trajectories, navigation depths and other parameters that will change the robot's behaviour according to the perception system output. Hence, the time in which to make a decision is critical in order to assure safe robot operation and to acquire good quality data; consequently, the efficiency of the on-line image processing from acoustic data is a key issue.

Current techniques for acoustic data processing are time and computationally intensive. Hence, it was decided to process data coming from a SSS using a technique that is used for radars, due to its efficiency and its amenability to on-line processing. The engineering problem to solve in this case was underwater pipeline tracking for routine inspections in the off-shore industry. Then, an automatic oil pipeline detection system was developed borrowing techniques from the processing of radar measurements. The radar technique is known as Cell Average – Constant False Alarm Rate (CA – CFAR). With a slight variation of the algorithms underlying this radar technique, which consisted of the previous accumulation of partial sums, a great improvement in computing time and effort was achieved. Finally, a comparison with previous approaches over images acquired with a SSS from a vessel in the Salvador de Bahia bay in Brazil showed the feasibility of using this on-board technique for AUV perception.