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Data visualization has become an increasingly important means of effective data communication and has played a vital role in broadcasting the progression of COVID-19. Accessible data representations, on the other hand, have lagged behind, leaving areas of information out of reach for many blind and visually impaired (BVI) users. In this work, we sought to understand (1) the accessibility of current implementations of visualizations on the web; (2) BVI users’ preferences and current experiences when accessing data-driven media; (3) how accessible data representations on the web address these users’ access needs and help them navigate, interpret, and gain insights from the data; and (4) the practical challenges that limit BVI users’ access and use of data representations. To answer these questions, we conducted a mixed-methods study consisting of an accessibility audit of 87 data visualizations on the web to identify accessibility issues, an online survey of 127 screen reader users to understand lived experiences and preferences, and a remote contextual inquiry with 12 of the survey respondents to observe how they navigate, interpret and gain insights from accessible data representations. Our observations during this critical period of time provide an understanding of the widespread accessibility issues encountered across online datamore »visualizations, the impact that data accessibility inequities have on the BVI community, the ways screen reader users sought access to data-driven information and made use of online visualizations to form insights, and the pressing need to make larger strides towards improving data literacy, building confidence, and enriching methods of access. Based on our findings, we provide recommendations for researchers and practitioners to broaden data accessibility on the web.« less

Online data visualizations play an important role in informing public opinion but are often inaccessible to screen reader users. To address the need for accessible data representations on the web that provide direct, multimodal, and up-to-date access to the data, we investigate audio data narratives –which combine textual descriptions and sonification (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to define design principles that guide the structure, content, and duration of a data narrative. Based on these principles and relevant auditory processing characteristics, we propose a dynamic programming approach to automatically generate an audio data narrative from a given dataset. We evaluate our approach with 16 screen reader users. Findings show with audio narratives, users gain significantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.

We increasingly rely on up-to-date, data-driven graphs to understand our environments and make informed decisions. However, many of the methods blind and visually impaired users (BVI) rely on to access data-driven information do not convey important shape-characteristics of graphs, are not refreshable, or are prohibitively expensive. To address these limitations, we introduce two refreshable, 1-DOF audio-haptic interfaces based on haptic cues fundamental to object shape perception. Slide-tone uses finger position with sonification, and Tilt-tone uses fingerpad contact inclination with sonification to provide shape feedback to users. Through formative design workshops (n = 3) and controlled evaluations (n = 8), we found that BVI participants appreciated the additional shape information, versatility, and reinforced understanding these interfaces provide; and that task accuracy was comparable to using interactive tactile graphics or sonification alone. Our research offers insight into the benefits, limitations, and considerations for adopting these haptic cues into a data visualization context.

Soft isoperimetric truss robots have demonstrated an ability to grasp and manipulate objects using the members of their structure. The compliance of the members affords large contact areas with even force distribution, allowing for successful grasping even with imprecise open-loop control. In this work we present methods of analyzing and controlling isoperimetric truss robots in the context of grasping and manipulating objects. We use a direct stiffness model to characterize the structural properties of the robot and its interactions with external objects. With this approach we can estimate grasp forces and stiffnesses with limited computation compared to higher fidelity finite elements methods, which, given the many degrees-of-freedom of truss robots, are prohibitively expensive to run on-board. In conjunction with the structural model, we build upon a literature of differential kinematics for truss robots and apply it to the task of manipulating an object within the robot’s workspace.

Modular soft robots combine the strengths of two traditionally separate areas of robotics. As modular robots, they can show robustness to individual failure and reconfigurability; as soft robots, they can deform and undergo large shape changes in order to adapt to their environment, and have inherent human safety. However, for sensing and communication these robots also combine the challenges of both: they require solutions that are scalable (low cost and complexity) and efficient (low power) to enable collectives of large numbers of robots, and these solutions must also be able to interface with the high extension ratio elastic bodies of soft robots. In this work, we seek to address these challenges using acoustic signals produced by piezoelectric surface transducers that are cheap, simple, and low power, and that not only integrate with but also leverage the elastic robot skins for signal transmission. Importantly, to further increase scalability, the transducers exhibit multi-functionality made possible by a relatively flat frequency response across the audible and ultrasonic ranges. With minimal hardware, they enable directional contact-based communication, audible-range communication at a distance, and exteroceptive sensing. We demonstrate a subset of the decentralized collective behaviors that these functions make possible with multi-robot hardware implementations. Themore »use of acoustic waves in this domain is shown to provide distinct advantages over existing solutions.« less

This paper introduces a new class of soft reconfigurable robot: balloon animal robots. The balloon animal robot consists of a closed volume inflatable tube which can be reconfigured into structures of varying topology by a collective of simple sub-unit robots. The robotic sub-units can (1) drive along the length of the tube to localize a joint, (2) create pinch points that locally reduce the bending stiffness of the tube to form a joint, and (3) selectively mechanically couple to one another through cable driven actuators to create nodes of the structure. In this work we introduce the hardware necessary to construct the robot, present experiments to guide the hardware design, and formulate an algorithm using graph theory to calculate the number of nodes and node connections needed to form different 2D shapes. Finally, we demonstrate the system with two active nodes and four passive nodes forming multiple 2D shapes from the same hardware.

For robots to be useful for real-world applications, they must be safe around humans, be adaptable to their environment, and operate in an untethered manner. Soft robots could potentially meet these requirements; however, existing soft robotic architectures are limited by their ability to scale to human sizes and operate at these scales without a tether to transmit power or pressurized air from an external source. Here, we report an untethered, inflated robotic truss, composed of thin-walled inflatable tubes, capable of shape change by continuously relocating its joints, while its total edge length remains constant. Specifically, a set of identical roller modules each pinch the tube to create an effective joint that separates two edges, and modules can be connected to form complex structures. Driving a roller module along a tube changes the overall shape, lengthening one edge and shortening another, while the total edge length and hence fluid volume remain constant. This isoperimetric behavior allows the robot to operate without compressing air or requiring a tether. Our concept brings together advantages from three distinct types of robots—soft, collective, and truss-based—while overcoming certain limitations of each. Our robots are robust and safe, like soft robots, but not limited by a tether;more »are modular, like collective robots, but not limited by complex subunits; and are shape-changing, like truss robots, but not limited by rigid linear actuators. We demonstrate two-dimensional (2D) robots capable of shape change and a human-scale 3D robot capable of punctuated rolling locomotion and manipulation, all constructed with the same modular rollers and operating without a tether.« less

We consider a class of robotic systems composed of high elongation linear actuators connected at universal joints. We derive the differential kinematics of such robots, and formalize concepts of controllability based on graph rigidity. Control methods are then developed for two separate applications: locomotion and shape morphing. The control algorithm in both cases solves a series of linearly constrained quadratic programs at each time step to minimize an objective function while ensuring physical feasibility. We present simulation results for locomotion along a prescribed path, and morphing to a target shape.