Constructing Radio Signal Strength Maps with Multiple Robots

Communication is essential for coordination in most cooperative control and sensing paradigms. In this paper, we investigate the construction of a map of radio signal strength that can be used to plan multirobot tasks and also serve as useful perceptual information. We show how nominal models of an urban environment, such as those obtained by aerial surveillance, can be used to generate strategies for exploration and present preliminary experimental results with our multi-robot testbed

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

Maintaining network connectivity and performance in robot teams

In this paper, we present an experimental study of strategies for maintaining end-to-end communication links for tasks such as surveillance, reconnaissance, and target search and identification, where team connectivity is required for situational awareness. Our main contributions are three fold: (a) We present the construction of a radio signal strength map that can be used to plan multi-robot tasks and also serve as useful perceptual information. We show how a nominal model of an urban environment obtained by aerial surveillance is used to generate strategies for exploration. (b) We present reactive controllers for communication link maintenance; and (c) we consider the differences between monitoring signal strength versus data throughput. Experimental results, obtained using our multi-robot testbed in three representative urban environments, are presented with each of our main contributions

Generalized Model to Enable Zero-shot Imitation Learning for Versatile Robots

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete. In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic tasks. The RFID inventory task is a typical long-horizontal robotic task that can be formulated as a Partially Observable Markov Decision Process (POMDP); the robot should be able to recall previous actions and reason from current environmental observations to optimize its strategy. To this end, our CMRM has been designed with a two-stream flow structure to extract abstract information concealed in environmental observations and subsequently generate robot actions by reasoning structural and temporal features from historical and current observations. Extensive experiments in a virtual platform and mockup real store are conducted to evaluate the proposed CMRM. Experimental results demonstrate that CMRM is capable of performing RFID inventory tasks in unstructured environments with complex layouts and provides competitive accuracy that surpasses previous methods and manual inventory. To facilitate the training and assessment of CMRM, we constructed a Unity3D-based virtual platform that can be configured into various environments, like an apparel store. This platform is capable of offering photo-realistic objects and precise physical features (gravities, appearance, and more) to provide close to real environments for training and testing robots. Subsequently, the robot, once trained, was deployed in an actual retail environment to perform RFID inventory tasks. This approach effectively bridges the ``reality gap , enabling the robot to perform the RFID inventory task seamlessly in both virtual and real-world settings, thereby demonstrating zero-shot generalization capabilities

Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Construcción de mapas de cobertura para comunicaciones inalámbricas

Conocer ciertas características sobre cómo es la propagación de la señal en determinados entornos es de vital importancia para el uso efectivo de una red de comunicaciones inalámbrica. Dependiendo de la complejidad del medio podemos utilizar como guía uno o varios modelos de propagación, pudiéndose llegar a buenas aproximaciones sobre el comportamiento de la señal. Bien sea para desarrollar modelos (empíricos o deterministas) o validarlos, se requieren mediciones experimentales. En otros casos no se dispone de un modelo de propagación, por lo que la única opción radica en tomar mediciones prácticas. Cualquiera sea el caso, a través de la representación de estas mediciones en función de la posición obtenemos lo que se suele llamar un mapa de comunicaciones o mapa de cobertura. Situados en este contexto, en este trabajo se desarrollaron herramientas para la construcción de mapas de comunicaciones a gran escala y a pequeña escala. Pensando en una solución modular, se desarrollaron diversos módulos para el meta sistema operativo ROS y se implementaron en un vehículo real todoterreno, y en un robot Pioneer P3AT. Se realizaron pruebas en un ambiente de especial interés para el grupo RoPeRT (Robotics, Perception and Real Time) de la Universidad de Zaragoza: el túnel ferroviario de Somport, que conecta Francia con España. Se obtuvo un mapa de cobertura a gran escala de una sección de especial interés, de unos 2.5 km de largo con cambio de pendiente, y uno más detallado a menor escala de una sección de 1 Km, donde aparecen atenuaciones importantes. Se compararon los resultados con un modelo de propagación basado en “Ray Tracing” (trazado de rayos), desarrollado por Valenzuela (1993). Se obtuvieron similitudes como la existencia de un notable fading, pero a la vez diferencias que dan importancia a las mediciones realizadas, como la ubicación de este fading y diversas atenuaciones que no aparecen en las simulaciones. Se verificó la repetibilidad de estos fenómenos realizando diversos experimentos, inclusive en días diferentes, cuestión que no se ha sido tratada con importante énfasis en la literatura. También se encontró que, debido a variaciones transversales, aplicando una diversidad espacial muy superior a la de las tarjetas comerciales, podemos mejorar la calidad de señal en la mayoría del trayecto estudiado. Los resultados obtenidos pueden ser utilizados tanto para el despliegue óptimo de redes inalámbricas, hasta inclusive para el desarrollo de técnicas de navegación para equipos multi-robot manteniendo la comunicación