For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this paper, we build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory with a history of observations to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. Stochastic reachability analysis is proposed to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass systems is proposed to enable safe proactive motion planning. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of multiple humans, demonstrating improved performance and effectiveness of online learning when compared to reactive obstacle avoidance approaches.

We present a method for calibration-free, infrastructure-free localization in sensor networks. Our strategy is to estimate node positions and noise distributions of all links in the network simultaneously - a strategy that has not been attempted thus far. In particular, we account for biased, non-line-of-sight (NLOS) range measurements from ultra-wideband (UWB) devices that lead to multi-modal noise distributions, for which few solutions exist to date. Our approach circumvents cumbersome a-priori calibration, allows for rapid deployment in unknown environments, and facilitates adaptation to changing conditions. Our first contribution is a generalization of the classical multidimensional scaling algorithm to account for measurements that have multi-modal error distributions. Our second contribution is an online approach that iterates between node localization and noise parameter estimation. We validate our method in 3-dimensional networks, (i) through simulation to test the sensitivity of the algorithm on its design parameters, and (ii) through physical experimentation in a NLOS environment. Our setup uses UWB devices that provide time-of-flight measurements, which can lead to positively biased distance measurements in NLOS conditions. We show that our algorithm converges to accurate position estimates, even when initial position estimates are very uncertain, initial error models are unknown, and a significant proportion of the network links are in NLOS.

A significant number of real-time control applications include computational activities where the results have to be delivered at precise instants, rather than within a deadline. The performance of such systems significantly degrades if outputs are generated before or after the desired target time. This work presents a general methodology that can be used to design and analyze target-sensitive applications in which the timing parameters of the computational activities are tightly coupled with the physical characteristics of the system to be controlled. For the sake of clarity, the proposed methodology is illustrated through a sample case study used to show how to derive and verify real-time constraints from the mission requirements. Software implementation issues necessary to map the computational activities into tasks running on a real-time kernel are also discussed to identify the kernel mechanisms necessary to enforce timing constraints and analyze the feasibility of the application. A set of experiments are finally presented with the purpose of validating the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.

Several real-time applications include tasks in which the output must be produced at precise time instants, rather than “within” a deadline, and the overall system performance significantly degrades when the task is executed too late or too early with respect to the desired time. This paper illustrates one of such applications and takes it as a reference case study to propose a general approach to show how to derive the timing constraints from the application requirements, how to implement the application on top of a realtime kernel, identifying the operating system features necessary to enforce such constraints, and how to analyze the schedulability of the task set. A set of experimental results are also presented to validate the proposed approach.