IROS 2015 Papers

The paper describes a passivity based approach to the generation of virtual fixtures for robotic teleoperation schemes involving multiple masters and multiple slaves. Virtual fixtures considered in the paper aim to guide the user towards a geometric path, which is assumed to be collision-free by design, describing a desired execution of a given task. To preserve safe distance from obstacles and at the same time suggest the user a preferred direction to progress along the path, the virtual fixtures are generated by arbitrarily redirecting assistive forces obtained by summation of attractive and repulsive potential fields. The main result of the paper is the definition of a passivity preserving condition for this redirection, so that the behavior of the teleoperated systems remains safe and stable. The proposed assisted mode of teleoperation has been tested on a surgical robot prototype with dual arms configuration, since robotic surgery represents a suitable application domain for such control schemes.

This paper proposes a semi-autonomous bilateral control architecture for unmanned aerial vehicles. During autonomous navigation, a human operator is allowed to assist the autonomous controller of the vehicle by actively changing its navigation parameters to assist it in critical situations, such as navigating through narrow paths. The overall goal of the controller is to combine the stability and precision of an autonomous control with the cognitive abilities of a human operator, only when strictly required for the accomplishment of a task. The control architecture has been validated through simulations and experiments.

The Standard Platform League is one of the main competitions of the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2014, the league added a new sub-competition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i. e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this paper, we provide an overview of this competition, including its motivation and rules. We then present and analyze the results of the 2014 competition, which gathered robots from 23 teams, involved at least 50 human participants, and consisted of fifteen 20-minute games for a total playing time of 300 minutes. We also suggest improvements for future iterations, many of which will be evaluated at RoboCup 2015.

This paper reports on the inclusion of a probabilistic channel model within a cooperative localization planning framework. Underwater cooperative localization reduces positioning errors by sharing sensor data across a team of underwater vehicles. Relative range constraints between vehicles are measured by the one-way-travel-time of successfully received acoustic communication broadcasts. The quality of the navigation solution is intimately linked to the geometry of the network and, therefore, can benefit from planning informative relative trajectories. We cast this planning problem as an instance of belief space planning. In order to weight packet loss over the acoustic channel, we introduce a probabilistic channel model into the planning framework. We propose an optimization algorithm that allows us to plan open-loop control actions and, by extension, closed-loop parameterized trajectories.

Contextual cues can provide a rich source of information for robots that operate in the presence of other agents such as people, animals, vehicles and fellow robots. We are interested in context, in the form of the behavioural intent of an agent, for enhanced trajectory prediction. We present a Bayesian framework that estimates both the intended goal destination and future trajectory of a mobile agent moving among multiple static obstacles. Our method is based on multi-modal hypotheses of the intended goal, and is focused primarily on the long-term trajectory of the agent. We propose a computationally efficient solution and demonstrate its behaviour in a pedestrian scenario with a real-world data set. Results show the benefits of our method in comparison to traditional trajectory prediction methods and illustrate the feasibility of integration with higher-level planning algorithms.

This paper presents a trajectory tracking control strategy based on the subspace stabilization approach to accurately manipulate an under-actuated flying robot from a known initial state to the desired terminal state. To facilitate the development of this tracking strategy, the dynamical model of the quadrotor is firstly proposed. Subsequently, an optimal trajectory generation algorithm is adopted to generate dynamically consistent trajectories regarding the initial and terminal state constraints in specific missions. Then, a trajectory tracking control strategy based on the subspace stabilization approach is developed considering the lumped disturbances and time delays. The developed control strategy is applied for ball juggling of a highly under-actuated quadrotor, which is a popular flying robot in recent years. Real-time experimental results show that the quadrotor can be accurately manipulated from a known initial state to the desired terminal state within a given time horizon. In the consecutive juggling tasks, the quadrotor with a racket of radius 0.065 m can consecutively juggle the ball for averagely 4 hits in each rally, and a longest rally achieved by the developed control strategy is 14 hits. The feasibility of the developed control strategy is also preliminarily verified through the cooperative juggling between two quadrotors. All of these results demonstrate the effectiveness of the developed control strategy.

In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments.

In this paper, we introduce the very first ‘binary’ 3D feature descriptor, B-SHOT, for fast and efficient keypoint matching on 3D point clouds. We propose a binary quantization method that converts a real valued vector to a binary vector. We apply this method on a state-of-the-art 3D feature descriptor, SHOT [1], and create a new binary 3D feature descriptor. B-SHOT requires 32 times lesser memory for its representation while being 6 times faster in feature descriptor matching, when compared to the SHOT feature descriptor. Experimental evaluation shows that B-SHOT offers comparable keypoint matching performance to that of the state-of-the-art 3D feature descriptors on a standard benchmark dataset.

Manipulation tasks involving sequential pick-and-place actions in human environments remains an open problem for robotics. Central to this problem is the inability for robots to perceive in cluttered environments, where objects are physically touching, stacked, or occluded from the view. Such physical interactions currently prevent robots from distinguishing individual objects such that goal-directed reasoning over sequences of pick-and-place actions can be performed. Addressing this problem, we introduce the Axiomatic Particle Filter (APF) as a method for axiomatic state estimation to simultaneously perceive objects in clutter and perform sequential reasoning for manipulation. The APF estimates state as a scene graph, consisting of symbolic spatial relations between objects in the robot's world. Assuming known object geometries, the APF is able to infer a distribution over possible scene graphs of the robot's world and produce the maximally likely state estimate of each object's pose and spatial relationships between objects. We present experimental results using the APF to infer scene graphs from depth images of scenes with objects that are touching, stacked, and occluded.

Identification of landmarks for outdoor navigation is often performed using computationally expensive computer vision methods or via heavy and expensive multi-spectral and range sensors. Both choices are forbidden on Micro Aerial Vehicles (MAV) due to limited payload and computational power. However, an appropriate choice of the hardware sensor equipment allows the employment of mixed multi-spectral analysis and computer vision techniques to identify natural landmarks. In this work, we propose a low-cost low-weight camera array with appropriate optical filters to be exploited both as stereo camera and multi-spectral sensor. Through stereo vision and the Normalized Difference Vegetation Index (NDVI), we are able to classify the observed materials in the scene among several different classes, identify vegetation and water bodies and provide measurements of their relative bearing and distance from the robot. A handheld prototype of this camera array is tested in outdoor environment.

In an effort to improve sailor safety during underway replenishment on the open sea, a robotic refueling system has been developed to autonomously refuel unmanned surface vehicles (USVs). The Rapid Autonomous Fuel Transfer (RAFT) project has demonstrated a methodology that could be used on the open water to autonomously refuel Navy vessels at significant sea states. The prototype refueling system is made up of two robotic arms: a rigid and precise industrial robotic manipulator to pinpoint the location of the target fuel tank and a novel soft pneumatic arm (Octarm) to provide compliant and safe contact with the USV. At the end of the Octarm, a magnetic end effector was designed (patent pending) to transfer a refueling “puck” from the robotic system to the target fuel tank. Acting under manual control or autonomously through visual tracking techniques, the robotic refueling system was shown to effectively transfer fuel to the target US Navy Sea Fox vessel under sea state 3.25 conditions at the US Army Aberdeen Test Center. The results demonstrate the feasibility of using a robotic solution to allow autonomous shore-to-ship or ship-to-USV refueling. It also illustrates the benefits and challenges of future robotic ship-to-USV refueling operations. This represents the first demonstrated use of a robotic system for fluid transfer to vessels in active sea states. This paper describes the design, development, and demonstration of the prototype autonomous refueling system.

We detail the design of autonomous golf cars which were used in public trials in Singapore's Chinese and Japanese Gardens, for the purpose of raising public awareness and gaining user acceptance of autonomous vehicles. The golf cars were designed to be robust, reliable, and safe, while operating under prolonged durations. Considerations that went in to the overall system design included the fact that any member of the public had to not only be able to easily use the system, but to also not have the option to use the system in an unintended manner. This paper details the hardware and software components of the golf cars with these considerations, and also how the booking system and mission planner facilitated users to book for a golf car from any of ten stations within the gardens. We show that the vehicles performed robustly throughout the prolonged operations with a small localization variance, and that users were very receptive from the user survey results.

The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer infrequent and costly information to the farmers about the state of their fields. The proposed methodology promotes the use of small-scale Unmanned Aerial Vehicles (UAVs) and Computer Vision algorithms that operate with information in the visual (RGB) spectrum. Through this implementation, a lower cost solution for identifying N deficiencies is promoted. We provide extensive results on the use of commercial RGB sensors for delivering the essential information to farmers regarding the condition of their field, targeting the reduction of N fertilizers and the increase of the crop performance. Data is first collected by a UAV that hovers over a stressed area and collects high resolution RGB images at a low altitude. A recommendation algorithm identifies potential segments of the images that are candidates exhibiting N deficiency. Based on the feedback from experts in the area a training set is constructed utilizing the initial suggestions of the recommendation algorithm. Supervised learning methods are then used to characterize crop leaves that exhibit signs of N deficiency. The performance of 84.2% strongly supports the potential of this scheme to identify N-deficient leaves even in the case of images where the unhealthy leaves are heavily occluded by other healthy or stressed leaves.

Wheelchairs play an important role for people living with locomotor impairments. However, power wheelchair users frequently report both minor and major accidents. The goal of this paper is to advocate for the use of robotic technology, in particular sensor-based detection and automatic classification of activities, to track and characterize activities onboard smart wheelchairs. Experiments were conducted in a clinical setting, in which experienced wheelchair users were asked to conduct a set of typical wheelchair activities. This paper presents an end-to-end pipeline for accurately classifying these activities from accelerometer data using signal processing and machine learning methods. Our classifier achieved an overall accuracy of around 50% in a more than 25 classes classification problem, compared to less than 4% with a random classifier. We also explored the possibility of discovering hidden patterns of activities using unsupervised topic modeling methods. We demonstrated the power of the inferred patterns with two use cases, namely story telling and hazard discovery. Altogether, this work provides new tools for characterizing the usage of smart wheelchairs with typical users.

Robotic systems typically have numerous parameters, e.g. the choice of planning algorithm, real-valued parameters of motion and vision modules, and control parameters. We consider the problem of optimizing these parameters for best worst-case performance over a range of environments. To this end we first propose to evaluate system parameters by adversarially optimizing over environment parameters to find particularly hard environments. This is then nested in a game-theoretic minimax optimization setting, where an outerloop aims to find best worst-case system parameters. For both optimization levels we use Bayesian global optimization (GP-UCB) which provides the necessary confidence bounds to handle the stochasticity of the performance. We compare our method (Nested Minimax) with an existing relaxation method we adapted to become applicable in our setting. By construction our approach provides more robustness to performance stochasticity. We demonstrate the method for planning algorithm selection on a pick'n'place application and for control parameter optimization on a triple inverted pendulum for robustness to adversarial perturbations.

Underwater environments present a considerable challenge for computer vision, since water is a scattering medium with substantial light absorption characteristics which is made even more severe by turbidity. This poses significant problems for visual underwater navigation, object detection, tracking and recognition. Previous works tackle the problem by using unreliable priors or expensive and complex devices. This paper adopts a physical underwater light attenuation model which is used to enhance the quality of images and enable the applicability of traditional computer vision techniques images acquired from underwater scenes. The proposed method simultaneously estimates the attenuation parameter of the medium and the depth map of the scene to compute the image irradiance thus reducing the effect of the medium in the images. Our approach is based on a novel optical flow method, which is capable of dealing with scattering media, and a new technique that robustly estimates the medium parameters. Combined with structure-from-motion techniques, the depth map is estimated and a model-based restoration is performed. The method was tested both with simulated and real sequences of images. The experimental images were acquired with a camera mounted on a Remotely Operated Vehicle (ROV) navigating in a naturally lit, shallow seawater. The results show that the proposed technique allows for substantial restoration of the images, thereby improving the ability to identify and match features, which in turn is an essential step for other computer vision algorithms such as object detection and tracking, and autonomous navigation.

In recent years, several low-cost 3D laser scanners are being brought to the market and 3D laser scanning is becoming widely used in many fields. For example, 3D modeling of architectural structures or digital preservation of cultural heritages are typical applications for 3D laser scanning. Despite of the development of light-weight and high-speed laser scanners, however, the complicated measurement procedure and long measurement time are still a heavy burden for the widespread use of laser scanning. We have proposed a robotic 3D scanning system using multiple robots named CPS-SLAM, which consists of parent robots with a 3D laser scanner and child robots with target markers. In this system, a large-scale 3D model can be acquired by an on-board 3D laser scanner on a parent robot from several positions determined precisely by the localization technique using multiple robots named Cooperative Positioning System, CPS. Therefore, this system enables to build a 3D model without complicated post-processing procedures such as ICP. In addition, this system is an open-loop SLAM system and a quite precise 3D model can be obtained without closed loops. This paper proposes an automatic planning technique of a laser measurement for CPS-SLAM. By planning a proper scanning strategy depending on a target structure, it is possible to perform laser scanning efficiently and accurately even for a large-scale and complex environment. Proposed technique plans an efficient scanning strategy automatically by taking account of several criteria, such as visibility between robots, error accumulation, and efficient traveling. We conducted computer simulations and outdoor experiments to verify the performance of the proposed technique.

End effectors of laser ablation systems for minimally invasive surgery are crucial to the performance of integrated laser ablation systems. Due to the constraints imposed by millimeter-sized overall diameter, the designs of laser ablation end effectors are challenging. In this paper, a laser ablation end effector for minimally invasive surgery is designed and built. The proposed end effector is composed of a bending section, a ball joint mechanism and a micro imaging sensor. By actuating the bending section and the ball joint mechanism independently, the Nd:YAG laser beam with the wavelength of 1064nm is steered to desired directions within its workspace and irradiate target lesion points. The diameter of the laser ablating distal module is 5 millimeters and the maximal tilt angle of the ball joint mechanism is 26 degrees. Furthermore, based on the proposed end effector and auxiliary mechanical system, an autonomous laser ablating approach is established to coagulate lesion. This method is developed on a lesion's planar contour which is obtained by the small image sensor, and the coordinates of all planned ablating positions are calculated, satisfying that all lesion area are covered. The laser ablating method is verified by experiments.

Robotic assembly tasks are in general difficult to program and require a high degree of precision. As the complexity of the task increases it becomes increasingly unlikely that tasks can always be executed without errors. Preventing errors beyond a certain point is economically infeasible, in particular for small-batch productions. As an alternative, we propose a system for automatically handling certain classes of errors instead of preventing them. Specifically, we show that many operations can be automatically reversed. Errors can be handled through automatic reverse execution of the control program to a safe point, from which forward execution can be resumed. This paper describes the principles behind automatic reversal of robotic assembly operations, and experimentally demonstrates the use of a domain-specific language that supports automatic error handling through reverse execution. Our contribution represents the first experimental demonstration of reversible computing principles applied to industrial robotics.