Modelling and Simulation of a Railgun powered by a Capacitor Bank

A railgun powered by a capacitor bank was developed to launch hypervelocity projectiles. The efficiency of the gun to a large extent will determine its feasibility for weapon applications. A simulation code was developed to predict the performance of the railgun. The railgun has been modelled as a time-varying impedance to determine the currents and the voltages from the power source. In the railgun circuit the currents and the voltages are of the order of hundreds of kiloamperes. Even very low impedances of the order of milli-ohm and micro-henry are substantial sources of energy losses. The measured and simulation currents at peak values agree with in 10%, validating the model. The simulation code accurately predicts the energy distribution in the system. Maximization of the projectile energy leads to improved and efficient designs. The simulation also leads to the optimized launcher pressure and payload velocity

Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Sensing, Communication, and Localization Constraints

Determining multi-robot motion policies for persistently monitoring a region with limited sensing, communication, and localization constraints in non-GPS environments is a challenging problem. To take the localization constraints into account, in this paper, we consider a heterogeneous robotic system consisting of two types of agents: anchor agents with accurate localization capability and auxiliary agents with low localization accuracy. To localize itself, the auxiliary agents must be within the communication range of an {anchor}, directly or indirectly. The robotic team's objective is to minimize environmental uncertainty through persistent monitoring. We propose a multi-agent deep reinforcement learning (MARL) based architecture with graph convolution called Graph Localized Proximal Policy Optimization (GALOPP), which incorporates the limited sensor field-of-view, communication, and localization constraints of the agents along with persistent monitoring objectives to determine motion policies for each agent. We evaluate the performance of GALOPP on open maps with obstacles having a different number of anchor and auxiliary agents. We further study (i) the effect of communication range, obstacle density, and sensing range on the performance and (ii) compare the performance of GALOPP with non-RL baselines, namely, greedy search, random search, and random search with communication constraint. For its generalization capability, we also evaluated GALOPP in two different environments -- 2-room and 4-room. The results show that GALOPP learns the policies and monitors the area well. As a proof-of-concept, we perform hardware experiments to demonstrate the performance of GALOPP

Agri.q: A Sustainable Rover for Precision Agriculture

5noIn this paper, an innovative mobile and sustainable robot for precision agriculture, named “Agri.q”, is presented. Characterized by a peculiar mechanical architecture and provided with specific sensors and tools, the Agri.q is able to operate in unstructured agricultural environments in order to fulfill several tasks as mapping, monitoring, and manipulating or collecting small soil and leaf samples. In addition, the rover is equipped with a top platform covered with solar panels, whose orientation can be exploited both to maximize the sunrays collection during the auto-charging phase and to permit a drone landing over a horizontal surface, regardless of the ground inclination. A particular attention to energy consumptions and sustainability has driven the mechanical design of the Agri.q powertrain: the weight reduction results into a limited number of small size locomotion motors, enhancing the importance of the harvested solar energy on the energy balance of the whole system. In this paper, all these characteristics are described and analyzed in detail. Moreover, some preliminary tests aimed at evaluating the energetic behaviour of the rover under different working and weather conditions are presented.reservedmixedGiuseppe Quaglia, Carmen Visconte, Luca Carbonari, Andrea Botta, Paride CavalloneQuaglia, Giuseppe; Visconte, Carmen; Carbonari, Luca; Botta, Andrea; Cavallone, Parid

JFR Special Issue on Agricultural Robotics

David Ball, Ben Upcroft, Eldert van Henten, Anton van den Hengel, Pratap Tokekar and Jnaneshwar Da