Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots

We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $40\%$ longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73\% to 0.5\%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, \airl enables a broad class of deep RL research on UAVs. The source code is available at:~\texttt{\url{http://bit.ly/2JNAVb6}}.Comment: To Appear in Springer Machine Learning Journal (Special Issue on Reinforcement Learning for Real Life

Smart electric vehicle charging system

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience, weather information based on data mining, and simulation approaches. In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart Electric Vehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.Fundação para a Ciência e a Tecnologia (FCT

Special Topics in Information Technology

This open access book presents outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the best theses defended in 2021-22 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Towards a collective knowledge for a smart electric vehicle charging strategy

In this work is proposed the design of a system to create and handle Electric Vehicles (EV) charging procedures, based on intelligent process. Due to the electrical power distribution network limitation and absence of smart meter devices, Electric Vehicles charging should be performed in a balanced way, taking into account past experience (spread in a social network). In order to allow information exchange and to help user mobility, it was also created a mobile application to assist the EV driver on these processes. This proposed Smart Electric Vehicle Charging System uses Vehicle-to-Grid (V2G) technology, in order to connect Electric Vehicles and also renewable energy sources to Smart Grids (SG). This system also explores the new paradigm of Electrical Markets (EM), with deregulation of electricity production and use, in order to obtain the best conditions for commercializing electrical energy.The authors are grateful to the FCT (Fundação para a Ciência e a Tecnologia) and to the MIT-Portugal Program, for funding the Project MIT-PTIEDAM-SMS/00301200

Special Topics in Information Technology

This open access book presents outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the best theses defended in 2021-22 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

A modular robot architecture capable of learning to move and be automatically reconfigured

Tackling the problem of making a modular robot automatically learn the movements necessary to locomote in different environments is not an easy task. The ability of modular robots to have an arbitrary morphology provides an advantage over usual monolithic robots when moving in different environments. However, being able to reconfigure also has its problems. Movement control for reconfigurable robots is difficult to design and implement. Morphology can also influence the sensing capabilities of a modular robot. Only a few studies include sensor information when adjusting or optimizing controllers for modular robots. The main contribution of this work is the development of an architecture that includes a locomotion training framework that enables a modular robot to move in different environments taking into account sensor information. The framework is composed of four main parts: a control strategy, a configurable environment approach, an adaptation mechanism and a new modular robot platform: the EMERGE modular robot. The EMERGE modular robot platform is designed to be easy to be assembled and can be quickly reconfigured thanks to the magnetic connectors present in its modules. This in turn enables an external agent, like a robot manipulator to reconfigure the robot. Results show that well coordinated movements turn out to be very important for controllers using sensors to improve when being adapted. The mechanisms inside the controller, for example, decision structures, also play a major part in allowing a robot to adapt to move in different environments and be improved. Evaluating robots in reality is a very expensive task and differences between simulation and reality also make robots behave very differently. The magnetic connector makes the assembly of an EMERGE morphology easier but hinders the disassembly process.Resumen: Resolver el problema de hacer, de forma automática, que un robot modular se mueva en diferentes ambientes no es tarea fácil. La habilidad de los robots modulares de tener morfología arbitraria provee una ventaja sobre robots monolíticos normales al moverse en diferentes ambientes. Sin embargo, ser capaz de auto reconfigurarse tiene sus propios problemas. El control de movimiento para robots modulares es difícil de diseñar e implementar. La morfología de los robots también influencia la capacidad de percibir de los robots modulares. Solo contados estudios incluyen información sensorial al ajustar u optimizar controladores para este tipo de robots. La mayor contribución de este trabajo es el desarrollo de una arquitectura de robot modular que hace que este pueda moverse en diferentes ambientes teniendo en cuenta información sensorial. Esta arquitectura está compuesta por cuatro partes principales: una estrategia de control, un modelo de ambiente configurable, un mecanismo de adaptación y una plataforma de robot modular nueva: el robot EMERGE. El robot modular EMERGE, es diseñado para ser fácil de construir y de reconfigurar gracias a sus conectores magnéticos. Esto también posibilita a un agente externo, como un manipulador robótico, a reconfigurar el robot. Los resultados de los experimentos muestran que la buena coordinación del robot es muy importante para que los controles que usan sensores puedan mejorar. Los mecanismos internos del controlador, por ejemplo, las estructuras de decisión también tienen un rol importante al adaptar el robot a diferentes ambientes. Evaluar robots en la realidad es una tarea muy costosa y las diferencias entre la simulación y la realidad hacen que los robots se comporten muy diferente. Los conectores magnéticos hacen que armar las morfologías de módulos de EMERGE sean fáciles de armar, mas no de desarmar.Doctorad

Distributed Control of Microscopic Robots in Biomedical Applications

Current developments in molecular electronics, motors and chemical sensors could enable constructing large numbers of devices able to sense, compute and act in micron-scale environments. Such microscopic machines, of sizes comparable to bacteria, could simultaneously monitor entire populations of cells individually in vivo. This paper reviews plausible capabilities for microscopic robots and the physical constraints due to operation in fluids at low Reynolds number, diffusion-limited sensing and thermal noise from Brownian motion. Simple distributed controls are then presented in the context of prototypical biomedical tasks, which require control decisions on millisecond time scales. The resulting behaviors illustrate trade-offs among speed, accuracy and resource use. A specific example is monitoring for patterns of chemicals in a flowing fluid released at chemically distinctive sites. Information collected from a large number of such devices allows estimating properties of cell-sized chemical sources in a macroscopic volume. The microscopic devices moving with the fluid flow in small blood vessels can detect chemicals released by tissues in response to localized injury or infection. We find the devices can readily discriminate a single cell-sized chemical source from the background chemical concentration, providing high-resolution sensing in both time and space. By contrast, such a source would be difficult to distinguish from background when diluted throughout the blood volume as obtained with a blood sample

Dronevision: An Experimental 3D Testbed for Flying Light Specks

Today's robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one

Muscleless Motor synergies and actions without movements : From Motor neuroscience to cognitive robotics

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to ‘action’ that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a ‘plastic, configurable’ internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both “real and imagined” actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action ‘execution, imagination and understanding’), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly “act, interact, anticipate and understand” in unstructured natural living spaces