Abstract Malawi is located within the southern branch of the active East African Rift System, where earthquakes of moment magnitude (Mw) 7.0 or greater can occur along major faults. The majority of dwellings in the country are non-engineered unreinforced masonry constructions, built by local artisans with little input from engineers. These constructions are highly vulnerable to seismic events due to poor-quality materials and lack of construction detailing. This study presents a new methodology to assess the seismic fragility curves of typical dwellings located in the Central and Southern Malawi. On-site inspections of buildings are carried out to assess geometrical and structural features of 646 façades, and an experimental campaign is performed to characterise the mechanical properties of local construction materials. The collected data allow the identification of different building typologies in terms of quality of materials and construction techniques. The critical failure modes for each of the inspected façade at their ultimate limit state are evaluated analytically. Damage limit states are defined and adopted to derive simplified Static Push-Over (SPO) curves, transformed into incremental dynamic analysis (IDA) curves by using SPO2IDA. The IDA curves are then used to obtain fragility curves for the specific damage limit states. The fragility curves presented herein are the first to be calculated for these building typologies, based on local data, and unfortunately, they show that buildings in Malawi are far more vulnerable to earthquakes than estimated from previously available international reference data. The fragility curves developed in this study may prove useful for assessing the seismic risk of these building typologies in Malawi and other East African countries.

State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence regarding the user experience and the robot behavior; field studies may suffer from the noise imposed by uncontrollable factors from the environment; controlled lab experiments often fail to properly enforce challenging interaction settings. This paper contributes a first step towards addressing the outlined gaps in the literature. We present an original experiment, designed to test the implicit interaction between a mobile robot and a group of navigating human participants, under challenging settings in a controlled lab environment. We conducted a large-scale, within-subjects design study with 105 participants, exposed to three different conditions, corresponding to three distinct navigation strategies, executed by a telepresence robot (two autonomous, one teleoperated). We analyzed observed human and robot trajectories, under close interaction settings and participants' impressions regarding the robot's behavior. Key findings, extracted from a comparative statistical analysis include: (1) evidence that human acceleration is lower when navigating around an autonomous robot compared to a teleoperated one; (2) the lack of evidence to support the conventional expectation that teleoperation would be humans' preferred strategy. To the best of our knowledge, our study is unique in terms of goals, settings, thoroughness of evaluation and sample size.

This paper discusses the progress made on developing a multi-rotor helicopter equipped with a vision-based ability to navigate through an a priori unknown, GPS-denied environment. We highlight the backbone of our system, the relative estimation and control. We depart from the common practice of using a globally referenced map, preferring instead to keep the position and yaw states in the EKF relative to the current map node. This relative navigation approach allows simple application of sensor updates, natural characterization of the transformation between map nodes, and the potential to generate a globally consistent map when desired. The EKF fuses view matching data from a Microsoft Kinect with more frequent IMU data to provide state estimates at rates high enough to control the vehicles fast dynamics. Although an EKF is used, a nodes and edges graph represents the map. Hardware results showing the quality of the estimates and flights with estimates in the loop are provided.

Multirotor aircraft have become a popular platform for indoor flight. To navigate these vehicles indoors through an unknown environment requires the use of a SLAM algorithm, which can be processing intensive. However, their size, weight, and power capacity limit the processing capabilities available onboard. In this paper, we describe an approach to state estimation that helps to alleviate this problem. By using an improved dynamic model we show how to more accurately estimate the aircraft states than can be done with the traditional approach of integrating IMU measurements. The estimation is done with relatively infrequent corrections from accelerometers (40Hz) and even less frequent updates from a vision-based SLAM algorithm (2–5 Hz). This benefit of requiring less frequent updates from processing intensive sources comes without significant increase in the estimator's complexity.