The International Journal of Robotics Research

The speed and maneuverability at which legged animals can travel across rough and cluttered landscapes has provided inspiration for the development of legged robots with similar capabilities. Researchers have developed a number of robots that can run over rough terrain, but there is currently no universally accepted measure for stability in an unstructured environment. This paper considers the effectiveness of a number of stability metrics in predicting the disturbance-rejection behavior of three spring-loaded inverted pendulum (SLIP)-like systems ranging from simple reduced-order models to a one-legged mechanical hopping robot. We show that the gait stability norm utilizing leg-centric indicators and a two-step decay ratio utilizing body-centric indicators provide the best correlation between step response behavior and how the systems perform when running over unknown, uneven terrain. By providing a cross-platform stability comparison, this paper facilitates the development and evaluation of robots and control schemes, such as the active energy removal approach utilized in this paper, that aim to improve the stability of running systems.

We have designed, developed, and evaluated the performance of a multi-degree-of-freedom discretely actuated steerable cannula with shape-memory alloy (SMA) actuators. This will enable us to deliver diagnostic as well as therapeutic devices to the target location through the hollow inner core of the cannula. We propose to use SMAs to generate bending forces due to its small size and high power density. We annealed the SMA wires through a customized training process in an arc shape and mounted them at discrete locations on the outer surface of the cannula to enable joint motion. A pulse-width modulation (PWM)-based control scheme was implemented to control all SMA actuators simultaneously to enable multiple joint motion using a single power supply. The proposed controller was validated through an experiment inside gelatin to mimic the motion of the cannula inside a medium which requires a significant amount of force to move the joints of the cannula. Trajectory planning using a suitable metric and trajectory execution were successfully implemented. To demonstrate the delivery of a diagnostic tool through our cannula, we demonstrate that we can pass an optical coherence tomography probe through the cannula and perform in situ microscale imaging.

A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a thorough evaluation of our algorithm for localizing and mapping the mobile and stationary nodes in sparsely connected sensor networks using range-only measurements and odometry from the mobile node. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the non-linearity within the range-only measurements using Gaussian distributions. Utilizing the motion information from a mobile node, we show additional improvements to the static network localization solution. In addition to this centralized filtering technique, an asynchronous and decentralized approach is investigated and experimentally proven. This decentralized filtering technique distributes the computation across all nodes in the network, leveraging their numbers for improved efficiency. We demonstrate the effectiveness of our approach using simulated and real-world experiments in challenging environments with limited network connectivity. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available. Additionally, it is shown that by initializing the network map with a static network solution, the network mapping with a mobile node can be further improved.

We extend existing oceanographic sampling methodologies to sample an advecting feature of interest using autonomous robotic platforms. GPS-tracked Lagrangian drifters are used to tag and track a water patch of interest with position updates provided periodically to an autonomous underwater vehicle (AUV) for surveys around the drifter as it moves with ocean currents. Autonomous sampling methods currently rely on geographic waypoint track-line surveys that are suitable for static or slowly changing features. When studying dynamic, rapidly evolving oceanographic features, such methods at best introduce error through insufficient spatial and temporal resolution, and at worst, completely miss the spatial and temporal domain of interest. We demonstrate two approaches for tracking and sampling of advecting oceanographic features. The first relies on extending static-plan AUV surveys (the current state-of-the-art) to sample advecting features. The second approach involves planning of surveys in the drifter or patch frame of reference. We derive a quantitative envelope on patch speeds that can be tracked autonomously by AUVs and drifters and show results from a multi-day off-shore field trial. The results from the trial demonstrate the applicability of our approach to long-term tracking and sampling of advecting features. Additionally, we analyze the data from the trial to identify the sources of error that affect the quality of the surveys carried out. Our work presents the first set of experiments to autonomously observe advecting oceanographic features in the open ocean.

RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop-closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.

We study the problem of designing dynamically feasible trajectories and controllers that drive a quadrotor to a desired state in state space. We focus on the development of a family of trajectories defined as a sequence of segments, each with a controller parameterized by a goal state or region in state space. Each controller is developed from the dynamic model of the robot and then iteratively refined through successive experimental trials in an automated fashion to account for errors in the dynamic model and noise in the actuators and sensors. We show that this approach permits the development of trajectories and controllers enabling such aggressive maneuvers as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.

In this paper we describe a method for the automatic self-calibration of a 3D laser sensor. We wish to acquire crisp point clouds and so we adopt a measure of crispness to capture point cloud quality. We then pose the calibration problem as the task of maximizing point cloud quality. Concretely, we use Rényi Quadratic Entropy to measure the degree of organization of a point cloud. By expressing this quantity as a function of key unknown system parameters, we are able to deduce a full calibration of the sensor via an online optimization. Beyond details on the sensor design itself, we fully describe the end-to-end intrinsic parameter calibration process and the estimation of the clock skews between the constituent microprocessors. We analyse performance using real and simulated data and demonstrate robust performance over 30 test sites.

While complex hands seem to offer generality, simple hands are often more practical. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands, it considers several familiar examples, and it proposes an approach for autonomous manipulation using a general-purpose but simple hand. We explore the approach in the context of a bin-picking task, focused on grasping, recognition, and localization. The central idea is to use learned knowledge of stable grasp poses as a cue for object recognition and localization. This leads to some novel design criteria, such as minimizing the number of stable grasp poses. Finally, we describe experiments with two prototype hands to perform bin-picking of highlighter markers.