Automatic Calibration of Ultra Wide Band Tracking Systems Using A Mobile Robot: A Person Localization Case-study

Ultra Wide Band (UWB) is an emerging technology in the field of indoor localization, mainly due to its high performances in indoor scenarios and relatively easy deployment. However, in complex indoor environments, its positioning accuracy may drastically decrease due to biases introduced when emitters and receivers operate in Non Line-of-Sight (NLOS) conditions. This undesired phenomenon can be attenuated by creating, a priori, a map of the measurement error in the environment, that can be exploited at a later stage by a localization algorithm. In this paper, the error map is the result of a calibration process, which consists of collecting several measurements of the localization system at different locations in the environment. This work proposes the leveraging of mobile robots in order to automatize the calibration process with the ultimate purpose of improving UWB-based people localization in a realistic indoor environment. The whole process exploits existing algorithms in the field of robot localization conveniently adapted in order to address our use case and technology. Experiments in real environments of incrementally increasing complexity show how the average localization accuracy can be improved up to 50% by adopting this method

Market-based Coordination in Dynamic Environments Based on Hoplites Framework

This work focuses on multi-robot coordination based on the Hoplites framework for solving the multi-robot task allocation (MRTA) problem. Three variations of increasing complexity for the MRTA problem, spatial task allocation based on distance, spatial task allocation based on time and distance and persistent coverage have been studied in this work. The Fast Marching Method (FMM) has been used for robot path planning and providing estimates of the plans that robots bid on, in the context of the market. The use of this framework for solving the persistent coverage problem provides interesting insights by taking a high-level approach that is different from the commonly used solutions to this problem such as computing robot trajectories to keep the desired coverage level. A high fidelity simulation tool, Webots, along with the Robotic Operating System (ROS) have been utilized to provide our simulations with similar complexity to the real world tests. Results confirm that this pipeline is a very effective tool for our evaluations given that our simulations closely follow the results in reality. By modifying the replanning to prevent having costly or invalid plans by means of priority planning and turn taking, and basing the coordination on maximum plan length as opposed to time, we have been able to make improvements and adapt the Hoplites framework to our applications. The proposed approach is able to solve the spatial task allocation and persistent coverage problems in general. However, there exist some limitations. Particularly, in the case of persistent coverage, this method is suitable for applications where moderate spatial resolutions are sufficient such as patrolling

A Comparison of PSO and Reinforcement Learning for Multi-Robot Obstacle Avoidance

The design of high-performing robotic controllers constitutes an example of expensive optimization in uncertain environments due to the often large parameter space and noisy performance metrics. There are several evaluative techniques that can be employed for on-line controller design. Adequate benchmarks help in the choice of the right algorithm in terms of final performance and evaluation time. In this paper, we use multi-robot obstacle avoidance as a benchmark to compare two different evaluative learning techniques: Particle Swarm Optimization and Q-learning. For Q-learning, we implement two different approaches: one with discrete states and discrete actions, and another one with discrete actions but a continuous state space. We show that continuous PSO has the highest fitness overall, and Q-learning with continuous states performs significantly better than Q-learning with discrete states. We also show that in the single robot case, PSO and Q-learning with discrete states require a similar amount of total learning time to converge, while the time required with Q-learning with continuous states is significantly larger. In the multi-robot case, both Q-learning approaches require a similar amount of time as in the single robot case, but the time required by PSO can be significantly reduced due to the distributed nature of the algorithm

On-Board Human-Aware Navigation for Indoor Resource-Constrained Robots: A Case-Study with the Ranger

Introducing simple robotic platforms into domes- tic environments is faced with the challenge of social accept- ability. Therefore human-aware navigation is a must for robots operating in environments shared with human users. In this work, we focus on the human-aware navigation problem in a structured environment for a robot with limited sensing and constrained maneuvering called Ranger. The Ranger is a simple domestic robotic platform designed for interacting with children. The system combines person detection and tracking —which is the result of fusing laser-scan and depth-image based detectors provided by an RGB-D camera—, basic autonomous navigation and the concept of personal space. We rely only on the on-board sensors for mapping, localization, human tracking, and navigation. Systematic experiments are carried out with a real robot in the presence of a human in order to compare our human-aware navigation with a non human-aware simple approach. The results show that human-aware navigation is able to achieve trajectories which are respecting the personal spaces of the human and are thus more acceptable for the users

Budgeted Knowledge Transfer for State-wise Heterogeneous RL Agents

In this paper we introduce a budgeted knowledge transfer algorithm for non-homogeneous reinforcement learning agents. Here the source and the target agents are completely identical except in their state representations. The algorithm uses functional space (Q-value space) as the transfer-learning media. In this method, the target agent’s functional points (Q-values) are estimated in an automatically selected lower-dimension subspace in order to accelerate knowledge transfer. The target agent searches that subspace using an exploration policy and selects actions accordingly during the period of its knowledge transfer in order to facilitate gaining an appropriate estimate of its Q-table. We show both analytically and empirically that this method decreases the required learning budget for the target agent

Incorporating Perception Uncertainty in Human-Aware Navigation: A Comparative Study

In this work, we present a novel approach to human-aware navigation by probabilistically modelling the uncertainty of perception for a social robotic system and investigating its effect on the overall social navigation performance. The model of the social costmap around a person has been extended to consider this new uncertainty factor, which has been widely neglected despite playing an important role in situations with noisy perception. A social path planner based on the fast marching method has been augmented to account for the uncertainty in the positions of people. The effectiveness of the proposed approach has been tested in extensive experiments carried out with real robots and in simulation. Real experiments have been conducted, given noisy perception, in the presence of single/multiple, static/dynamic humans. Results show how this approach has been able to achieve trajectories that are able to keep a more appropriate social distance to the people, compared to those of the basic navigation approach, and the human-aware navigation approach which relies solely on perfect perception, when the complexity of the environment increases. Accounting for uncertainty of perception is shown to result in smoother trajectories with lower jerk that are more natural from the point of view of humans

Harnessing the Power of Smart and Connected Health to Tackle COVID-19:IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) — including Machine Learning (ML) and Big Data analytics — as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19

A Framework for Cooperative Human-Aware Navigation and Coordination of Multi-Robot Systems in Social Environments

Continual developments in robotic technology have enabled the use of robots in everyday applications in domestic, office and public spaces. Although single robot problems have been the main focus of social robotics research, applications of robots in social environments will not be limited to a single robot due to the increasing demand for robotic assistants and multi-robot operations. Multi-robot systems can achieve performances exceeding the sum of the individual robot contributions by exploiting the full potential of the team through information sharing, coordination, and joint decision-making. Robots operating in human-populated environments either directly interact with people or have to share the space with the humans. It is of utmost importance that people co-existing with robots feel safe and comfortable around them. This makes human-awareness essential for long-term sustainable deployment of robots in such environments. Furthermore, for cooperative robots, the presence of humans and their actions can directly affect the robot and team plans, making human-awareness more essential for ensuring high performance as well as social acceptability. Research in the area of socially-aware navigation has received substantial attention in recent years. However, despite their great potential, human-aware teams of robots considering social factors at both individual navigation and collective coordination and planning levels, are currently largely unexplored. In this thesis, we address the problem of human-aware cooperative navigation and coordination for multi-robot systems in realistic social environments. We focus on a class of multi-robot coordination problems known as multi-robot task allocation using a market-based approach. We explicitly consider the challenges of noisy, dynamic and stochastic human-populated environments by means of accounting for perception and prediction limitations and uncertainties in social cost modeling, bid estimation, coordination, and replanning. We construct an end-to-end framework comprising three main components of (i) human-aware navigation, (ii) human-aware coordination and planning for multi-robot systems, and (iii) human-robot interaction in the presence of multiple cooperative robots. We opt for an incremental approach to this problem starting from single robot human-aware navigation with expectation-based social costmaps. Subsequently, we move to multi-robot cooperative navigation in highly stochastic social environments. We propose human-aware coordination strategies based on social costs and social risks. The concept of risk introduced in this thesis incorporates perception and prediction uncertainties as well as social costs for estimating the stochastic costs of tasks that the robots should bid on in the market. Additionally, we introduce an adaptive risk-based replanning method for dealing with the limitations of local perception and unpredicted human behavior in the social environment. Finally, we demonstrate the interactive potential of the team of robots for social multi-robot task allocation by integrating an interaction that actively requests human collaboration and assistance in socially costly and blocking situations, into our adaptive replanning strategy. Extensive experiments with up to four robots and 12 humans in simulation, and up to two robots and two humans in reality have been carried out for evaluating the performance of the proposed methods in this thesis

Multi-Robot Coordination in Dynamic Environments Shared with Humans

This work addresses multi-robot coordination in social human-populated environments based on a Hoplites-based framework for solving the multi-robot task allocation problem. Humans are considered in the proposed coordination mechanism by means of accounting for social costs in bid evaluations and requesting collaboration in socially blocking situations. Initially, the effect of a realistic dynamic noisy environment with varying number of static/moving humans on the behavior and performance of our method is studied through an extensive suite of experiments in a realistic high-fidelity simulator (Webots). Results show that the total traveled distance and time are increased when humans are present in the environments. Localization noise is also increased particularly for the case of static people. In the second part of the experiments, a number of problematic cases resulting in longer modified plans, blocked passages, and long waits have been investigated. A comparative study targeting human-agnostic navigation and planning, human-aware navigation without considering humans in the planning phase, and human-aware navigation and planning has been conducted. Both simulated and real robot experiments confirm the effectiveness of accounting for humans on both team and individual levels for respecting social constraints as well as achieving a better performance based on MRTA metrics

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

In this letter, we propose an adaptive risk-based replanning strategy in the context of multirobot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and humanmotion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a nonadaptive replanning strategy in all cases with respect to the chosen social metrics. First, the overall performance of the team depends on its replanning strategy, and second on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the nonadaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans