Search Our Scientific Publications & Authors

Auton Robots 2022 19;46(1):307-320. Epub 2021 Oct 19.

Dep. of Informatics, Dep. of Neuroinformatics, University of Zurich and ETH, Zurich, Switzerland.

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the . Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. Read More

Auton Robots 2021 22;45(4):457-472. Epub 2021 Mar 22.

Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain.

We propose a new meta-module design for two important classes of modular robots. The new meta-modules are three-dimensional, robust and compact, improving on the previously proposed ones. One of them applies to so-called modular robot units, such as M-TRAN, SuperBot, SMORES, UBot, PolyBot and CKBot, while the other one applies to so-called modular robot units, which include Molecubes and Roombots. Read More

Auton Robots 2022 4;46(1):115-147. Epub 2021 Aug 4.

Computer Science Department, Stanford University, Stanford, USA.

Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and ; however, the interfaces people must use to control their robots are . Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. Read More

Auton Robots 2020 31;44(8):1451-1467. Epub 2020 Jul 31.

Division of Decision and Control, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.

This paper presents a scalable procedure for time-constrained planning of a class of uncertain nonlinear multi-robot systems. In particular, we consider robotic agents operating in a workspace which contains regions of interest (RoI), in which atomic propositions for each robot are assigned. The main goal is to design decentralized and robust control laws so that each robot meets an individual high-level specification given as a metric interval temporal logic (MITL), while using only local information based on a limited sensing radius. Read More

Auton Robots 2019 Feb 29;43(2):345-357. Epub 2018 Jun 29.

University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,

In highly constrained settings, e.g., a tentaclelike medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obstacles. Read More

Auton Robots 2019 Feb 11;43(2):435-448. Epub 2018 Jun 11.

Pneubotics, an Otherlab Company, San Francisco, CA.

In this work we present a novel, inductance-based system to measure and control the motion of bellows-driven continuum joints in soft robots. The sensing system relies on coils of wire wrapped around the minor diameters of each bellows on the joint. As the bellows extend, these coils of wire become more distant, decreasing their mutual inductance. Read More

Auton Robots 2018 01 12;42(1):83-97. Epub 2017 Jun 12.

Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, USA.

This paper describes a framework of algorithms for the active localization and tracking of flexible needles and targets during image-guided percutaneous interventions. The needle and target configurations are tracked by Bayesian filters employing models of the needle and target motions and measurements of the current system state obtained from an intra-operative imaging system which is controlled by an entropy-minimizing active localization algorithm. Versions of the system were built using particle and unscented Kalman filters and their performance was measured using both simulations and hardware experiments with real magnetic resonance imaging data of needle insertions into gel phantoms. Read More

Auton Robots 2018 15;42(2):177-196. Epub 2017 Feb 15.

3Department of Electrical Engineering and Computer Science, York University, Toronto, ON Canada.

Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. Read More

Auton Robots 2018 2;42(8):1787-1805. Epub 2018 May 2.

1Department of Control and Simulation (Micro Air Vehicle Laboratory), Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands.

To avoid collisions, Micro Air Vehicles (MAVs) flying in teams require estimates of their relative locations, preferably with minimal mass and processing burden. We present a relative localization method where MAVs need only to communicate with each other using their wireless transceiver. The MAVs exchange on-board states (velocity, height, orientation) while the signal strength indicates range. Read More

Auton Robots 2018 25;42(7):1477-1495. Epub 2018 Apr 25.

1School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB UK.

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a , which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. Read More

Auton Robots 2018 30;42(7):1355-1367. Epub 2018 Apr 30.

3Department of Computer Science, Brown University, Providence, USA.

We present a framework for representing scenarios with complex object interactions, where a robot cannot directly interact with the object it wishes to control and must instead influence it via intermediate objects. For instance, a robot learning to drive a car can only change the car's pose indirectly via the steering wheel, and must represent and reason about the relationship between its own grippers and the steering wheel, and the relationship between the steering wheel and the car. We formalize these interactions as chains and graphs of Markov decision processes (MDPs) and show how such models can be learned from data. Read More

Auton Robots 2017 28;41(1):181-203. Epub 2015 Dec 28.

2Department of Informatics, King's College London, London, WC2R 2LS UK.

It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. Read More

Auton Robots 2015;38(1):65-88. Epub 2014 Jul 31.

Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC), 08028  Barcelona, Spain.

The correct grasp of objects is a key aspect for the right fulfillment of a given task. Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures. This article reviews the quality measures proposed in the literature to evaluate grasp quality. Read More

Auton Robots 2009 Nov 9;27(4). Epub 2009 Sep 9.

University of Wisconsin, Madison, WI, USA.

In this paper we present a system to enhance the performance of feature correspondence based alignment algorithms for laser scan data. We show how this system can be utilized as a new approach for evaluation of mapping algorithms. Assuming a certain a priori knowledge, our system augments the sensor data with hypotheses ('Virtual Scans') about ideal models of objects in the robot's environment. Read More

Auton Robots 2001;11(3):305-310

Division of Biology 156-29, California Institute of Technology, Pasadena, CA, 91125, USA.

The brain is perhaps the most advanced and robust computation system known. We are creating a method to study how information is processed and encoded in living cultured neuronal networks by interfacing them to a computer-generated animal, the Neurally-Controlled Animat, within a virtual world. Cortical neurons from rats are dissociated and cultured on a surface containing a grid of electrodes (multi-electrode arrays, or MEAs) capable of both recording and stimulating neural activity. Read More

Auton Robots 2003 Mar-May;14(2-3):179-97

Hernandez Engineering, Inc., Houston, TX 77062, USA.

The Robotics Technology Branch at the NASA Johnson Space Center is developing robotic systems to assist astronauts in space. One such system, Robonaut, is a humanoid robot with the dexterity approaching that of a suited astronaut. Robonaut currently has two dexterous arms and hands, a three degree-of-freedom articulating waist, and a two degree-of-freedom neck used as a camera and sensor platform. Read More

Auton Robots 2003 Mar-May;14(2-3):165-78

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA.

One of the key issues in space exploration is that of deciding what space tasks are best done with humans, with robots, or a suitable combination of each. In general, human and robot skills are complementary. Humans provide as yet unmatched capabilities to perceive, think, and act when faced with anomalies and unforeseen events, but there can be huge potential risks to human safety in getting these benefits. Read More

Auton Robots 2003 Mar-May;14(2-3):127-45

DLR (German Aerospace Center), Institute of Robotics and Mechatronics, Oberpfaffenhofen, Wessling, Germany.

The paper briefly outlines DLR's experience with real space robot missions (ROTEX and ETS VII). It then discusses forthcoming projects, e.g. Read More

Auton Robots 2001 Nov;11(3):279-84

Department of Zoology and Centre for Neuroscience, University of Otago, New Zealand.

The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. Read More