Anqi Xu

Optimal complete terrain coverage using an Unmanned Aerial Vehicle

We present the adaptation of an optimal terrain coverage algorithm for the aerial robotics domain. The general strategy involves computing a trajectory through a known environment with obstacles that ensures complete coverage of the terrain while minimizing path repetition. We introduce a system that applies and extends this generic algorithm to achieve automated terrain coverage using an aerial vehicle. Extensive experimental results in simulation validate the presented system, along with data from over 100 kilometers of successful coverage flights using a fixed-wing aircraft.

Multi-domain monitoring of marine environments using a heterogeneous robot team

In this paper we describe a heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on real-time interaction with a remotely-located marine biologist.

A vision-based boundary following framework for aerial vehicles

We present an integration of classical computer vision techniques to achieve real-time autonomous steering of an unmanned aircraft along the boundary of different regions. Using an unified conceptual framework, we illustrate solutions for tracking coastlines and for following roads surrounded by forests. In particular, we exploit color and texture properties to differentiate between region types in the aforementioned domains. The performance of our system is evaluated using different experimental approaches, which includes a fully automated in-field flight over a 1km coastline trajectory.

OPTIMo: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations

We present OPTIMo: an Online Probabilistic Trust Inference Model for quantifying the degree of trust that a human supervisor has in an autonomous robot “worker”. Represented as a Dynamic Bayesian Network, OPTIMo infers beliefs over the human’s moment-to-moment latent trust states, based on the history of observed interaction experiences. A separate model instance is trained on each user’s experiences, leading to an interpretable and personalized characterization of that operator’s behaviors and attitudes. Using datasets collected from an interaction study with a large group of roboticists, we empirically assess OPTIMo’s performance under a broad range of configurations. These evaluation results highlight OPTIMo’s advances in both prediction accuracy and responsiveness over several existing trust models. This accurate and near real-time humanrobot trust measure makes possible the development of autonomous robots that can adapt their behaviors dynamically, to actively seek greater trust and greater efficiency within future human-robot collaborations. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems- software psychology General Terms Algorithms, Experimentation

Trust-driven interactive visual navigation for autonomous robots

We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a humans expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robots autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot.

A natural gesture interface for operating robotic systems

A gesture-based interaction framework is presented for controlling mobile robots. This natural interaction paradigm has few physical requirements, and thus can be deployed in many restrictive and challenging environments. We present an implementation of this scheme in the control of an underwater robot by an on-site human operator. The operator performs discrete gestures using engineered visual targets, which are interpreted by the robot as parametrized actionable commands. By combining the symbolic alphabets resulting from several visual cues, a large vocabulary of statements can be produced. An iterative closest point algorithm is used to detect these observed motions, by comparing them with an established database of gestures. Finally, we present quantitative data collected from human participants indicating accuracy and performance of our proposed scheme.

Learning legged swimming gaits from experience

We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-the-art policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.

MARE: Marine Autonomous Robotic Explorer

We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platforms particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations.

Fourier Tag: A Smoothly Degradable Fiducial Marker System with Configurable Payload Capacity

We describe the design and implementation of a fiducial marker system that encodes data in the frequency spectrum of a synthetic image. This distinctive approach to marker synthesis and data encoding allows for partial data extraction in adverse imaging conditions, and can significantly extend the detection range through graceful data degradation. Additional digital encoding and image construction techniques are used to increase the payload capacity, and also to store 3-D pose information in each fiducial marker. This fiducial marker scheme can be configured to match the needs of the target application. We present several experiments investigating the practical range of various parameters as well as the performance of a specific instance of the system.

Robust real-time underwater digital video streaming using optical communication

We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this systems performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.