IROS 2015 Papers

Monte Carlo localization (MCL) is a sample-based approach for representing probability density for the pose of a robot. MCL is widely used for mobile robots because of its robustness with respect to sensor noise. On the other hand, MCL can fail to estimate a pose of a robot if objects block measuring range of a laser sensor of the robot. Even though MCL fails to estimate the pose of the robot, MCL does not stop to estimate the pose because MCL does not have a self-diagnostic function. Therefore, detection of localization failure is essential for a mobile robot application. In the present paper, we propose a novel approach that detects the localization failure of MCL through logistic regression. The proposed approach has two advantages. First, it can detect whether position errors is larger than 0.15 m with a high accuracy. Second, the proposed method can detect localization failure by means of a statistically modeled equation. Moreover, as an example of an application using the probability of localization failure, we have proposed a hybrid localization scheme with MCL and laser odometry. The practical effectiveness of the proposed scheme is verified through experiments.

We have developed a method to visualize search and rescue (SAR) dogs' activities from sensor data recorded by the SAR dogs' sensor vests. This paper proposes two methods for detecting continuous barking actions of SAR dogs, which locate victims by smell and then bark continuously to tell handlers where victims are located. Continuous barking action is detected from audio information and a dog's body motions. This detection method is based on dynamic time warping (DTW), which has been used successfully to analyze human audio information. Cyclic body motion was observed during dogs' barking motions. This cyclic motion can be detected by an inertial measurement unit (IMU) attached to the vest. A fast Fourier transform (FFT) is used to analyze a dog's barking motion. The proposed detection methods were evaluated using audio and IMU data recorded during actual SAR dog training sessions. The F-scores of the audio and motion-based barking detection methods were 0.95 and 0.90, respectively. As a trial, we marked victim locations on a map based on the body motion.

Environment perception is an important task in computer vision for many applications in robotics. Especially for robots navigating through different levels of a building, stair detection constitutes an important perception task. In this paper, we propose a stair detection algorithm using range data. Firstly, we introduce a parameter, which describes local surface orientations w.r.t. a global reference. Secondly, a matched filter is used to detect relevant edges in the orientation data. Afterwards, line segments are determined using these edge data which are further used to estimate stairs. The proposed method is invariant against rotations of the sensor. We show that the system can handle typical outdoor stair types and outperforms the accuracy of state-of-the-art stair detection methods. Moreover, the method is used in real time to assist visually impaired people who wear the camera system on a helmet.

Despite their ubiquitous presence, texture-less objects present significant challenges to contemporary visual object detection and localization algorithms. This paper proposes a practical method for the detection and accurate 3D localization of multiple texture-less and rigid objects depicted in RGB-D images. The detection procedure adopts the sliding window paradigm, with an efficient cascade-style evaluation of each window location. A simple pre-filtering is performed first, rapidly rejecting most locations. For each remaining location, a set of candidate templates (i.e. trained object views) is identified with a voting procedure based on hashing, which makes the method's computational complexity largely unaffected by the total number of known objects. The candidate templates are then verified by matching feature points in different modalities. Finally, the approximate object pose associated with each detected template is used as a starting point for a stochastic optimization procedure that estimates accurate 3D pose. Experimental evaluation shows that the proposed method yields a recognition rate comparable to the state of the art, while its complexity is sub-linear in the number of templates.

In this paper, we present a novel approach in combining visual and inertial measurements in non-static environments for first order characterization of the metric motion of non-static objects in the scene. Our approach leverages online estimated ego motion states and uses a novel inertial-optical flow (IOF) measurement analysis to identify moving objects and to characterize them in their angular and linear velocities. The novelty of our algorithm lies in the identification and segmentation of consistent optical flow outliers in the so-called kinematic space. These consistent outliers in combination with the IOF information for ego-motion estimation yield a first order estimation of the moving object in full 3D and in metric units. The approach is highly efficient as it only requires matched features in two consecutive images. We evaluate and demonstrate our algorithm in simulations and in real world tests.

Advances in mobile robotics have enabled robots that can autonomously operate in human-populated environments. Although primary tasks for such robots might be fetching, delivery, or escorting, they present an untapped potential as information gathering agents that can answer questions for the community of co-inhabitants. In this paper, we seek to better understand requirements for such information gathering robots (InfoBots) from the perspective of the user requesting the information. We present findings from two studies: (i) a user survey conducted in two office buildings and (ii) a 4-day long deployment in one of the buildings, during which inhabitants of the building could ask questions to an InfoBot through a web-based interface. These studies allow us to characterize the types of information that InfoBots can provide for their users.

In this paper, we introduce the main components comprising the action-perception loop of an overarching framework implementing artificial attention, designed to fulfil the requirements of social interaction (i.e., reciprocity, and awareness), with strong inspiration on current theories in functional neuroscience. We demonstrate the potential of our framework, by showing how it exhibits coherent behaviour without any inbuilt prior expectations regarding the experimental scenario. Current research in cognitive systems for social robots has suggested that automatic attention mechanisms are essential to social interaction. In fact, we hypothesise that enabling artificial cognitive systems with middleware implementing these mechanisms will empower robots to perform adaptively and with a higher degree of autonomy in complex and social environments. However, this type of assumption is yet to be convincingly and systematically put to the test. The ultimate goal will be to test our working hypothesis and the role of attention in adaptive, social robotics.

This paper describes a novel amphibious robot, which adopts a dual-swing-legs propulsion mechanism, proposing a new locomotion mode. The robot is called FroBot, since its structure and locomotion are similar to frogs. Our inspiration comes from the frog scooter and breaststroke. Based on its swing leg mechanism, an unusual universal wheel structure is used to generate propulsion on land, while a pair of flexible caudal fins functions like the foot flippers of a frog to generate similar propulsion underwater. On the basis of the prototype design and the dynamic model of the robot, some locomotion control simulations and experiments were conducted for the purpose of adjusting the parameters that affect the propulsion of the robot. Finally, a series of underwater experiments were performed to verify the design feasibility of FroBot and the rationality of the control algorithm.

We present the design of simple, centimeter-scale modular actuation units (“Active Cells”) and passive compliant nodes that are electromechanically networked to create macroscopically deformable Modular Active Cell-based Structures (MACROs). Each Active Cell is a single degree-of-freedom linear actuator (a “muscle unit”), consisting of fiberglass end-pieces connecting two strands of Nitinol shape-memory alloy and a passive biasing spring. The Nitinol strands are coiled into a tight spring to increase deformations when activated through resistive heating. In-depth examination of the optimization of Nitinol coils with an antagonistic spring is presented, resulting in large repeatable axial cell strains of up to 25%. The design of these cellular muscle units to obtain maximal repeatable stroke is presented, allowing for the construction of larger networks of cells (MACRO modules, akin to a biological “tissue”) that can be customized to a target application. Finally, experimental demonstration of the construction and actuation of some simple MACRO modules is described.

This paper presents a novel manipulator for human-robot interaction that has low mass and inertia without losing stiffness and payload performance. A lightweight tension amplifying mechanism that increases the joint stiffness in quadratic order is proposed. High stiffness is essential for precise and rapid manipulation, and low mass and inertia are important factors for safety due to low stored kinetic energy. The proposed tension amplifying mechanism was applied to a 1-DOF elbow joint and then extended to a 3-DOF wrist joint. The developed manipulator was analyzed in terms of inertia, stiffness, and strength properties. Its moving part weighs 3.37 kg, and its inertia is 0.57 kg·m2, which is similar to that of a human arm. The stiffness of the developed elbow joint is 1440Nm/rad, which is comparable to that of the joints with rigid components in industrial manipulators. A detailed description of the design is provided, and thorough analysis verifies the performance of the proposed mechanism.

In this paper, a back-drivable joint mechanism for an in-pipe robot is presented. The joint mechanism consists of 2-pitch and 1-roll joint. The back-drivability of the joint mechanism is realized by using a ball-screw and a steel wire rope. When the in-pipe robot with the back-drivable joint mechanism passes through pipeline elements except the miter and the branch, the control of the joint mechanism is not required. In addition, a rescue mechanism is not needed in case of emergency such as the loss of power, communication and control. Besides, the 2-pitch joint of this joint module assists the in-pipe robot to pass through the miter and the branch safely and quickly. Finally, the performance of back-drivable joint module is experimentally validated.

Magnetic adhesion mechanism is commonly used in mobile robots design for inspection tasks of ferric structures. The magnetic adhesion force is generated either by Permanent Magnets (PMs) or Electro-Magnets (EMs). Both methods have intrinsic limitations: fixed magnetic force in PMs or a constant electric power supply in EMs. Work in this paper presents a novel active magnetic wheel design by implementing Electro- Permanent Magnet (EPM) technology in its adhesion mechanism. EPMs allow to control the magnetic force by simply applying a short electric pulse. Controlling the amplitude of the electrical pulse, the magnetic force can be set into a chosen value enabling a continuous variation of the magnetic adhesive force. Implementing EPMs in adhesion mechanism will impact significantly on the mobile robot when manoeuvring in complex structures, avoiding difficult obstacles and energy consumption, thus enhancing its performance. Simulations of the proposed wheel design are performed in order to demonstrate the behaviour of the active magnetic adhesion mechanism and its feasibility. An improved design of the actual wheel is presented based on results of initial simulations. Further designs are suggested by adapting the adhesion mechanism to existing wheel designs.

Autonomous underwater robots provide opportunities to perform missions in confined environments such as water pipe networks. They can carry sensors in these pipes and perform tasks such as mapping and inspection. Those robots must have a high level of maneuverability in order to navigate through complex networks of pipes with irregularities due to rust and calcite deposition. We propose a fully integrated, untethered robot capable of carrying sensors and maneuver into water pipe networks. The objective of this paper is to present (i) the optimal shape design and (ii) a propulsion system selection and sizing procedure for such robots. A prototype is built to demonstrate the basic elements of maneuverability, including following straight lines and making sharp turns.

The population of disabled stroke survivors is increasing sharply over the world, and robot-assisted training has been proved to be effective to help motor relearning and alleviate the shortage of physical therapists. This paper presents the design details of a novel upper limb rehabilitation robot named CASIA-ARM, which can assist poststroke patients to perform shoulder and elbow rehabilitation training in the horizontal plane, and provide both force and visual feedback to the patient: 1) A five-bar closed-chain structure is designed to realize a stiff and low-inertial mechanism. Workspace and singularity analysis is given, which guarantees that the workspace is large enough for upper limb moving and no singularity in the workspace. 2) Cable transmission, motor current controller and impedance control method guarantee the transparent and compliant interaction between the human and the robot. 3) Implementation examples of passive training based on position control and active training based on impedance control are presented in detail. 4) In order to evaluate the robot's applicability and performance, examples of trajectory tracking and active reaching tasks are used in this study, and the preliminary test results are also given (tracking error: 0.86±0:42 mm, force control error: 1.41±0.79 N in the X direction, and 1.22±0.91 N in the Y direction).

Unmanned multirotor helicopters have been expected for various critical tasks such as rescue missions, and it is important to sense the environment to achieve those tasks. In order to realize such sensor systems, this paper considers acoustic information recognition from a hovering helicopter which a microphone array system. As the noise of rotors distorts the acoustic information, noise reduction is necessary. Since the rotor noise varies even at the hovering flight, an acoustic model of the rotor noise is derived taking the dynamics of the helicopter into account, and the model is utilized to evaluate the performance of the microphone array. The proposed approach was verified by evaluating the optimality of a real device that was empirically tuned in authors previous work, and the computed configuration of the microphone array and the developed one coincide well, which ensures the validity of the approach. As the validation through a practical application, the signal was processed by Delay-and-Sum Beam Former to localize the sound source. The system was able to find peaks of the power that corresponded to the sound source.

Flexible and stretchable electronics are expected to contribute to electric conductors, sensors and actuators for various applications, such as robotic systems and power-assisted systems. In this study, a stretchable strain sensor is proposed as a length sensor for a pneumatic artificial muscle. To measure the length of the artificial muscle, the stretchable strain sensor is required to have quick response characteristics. A capacitance measurement circuit based on a band-pass filter is analyzed, and is tuned by the parameters of its electric components. The circuit measures the capacitance of the strain sensor with accuracy equivalent to a commercially available LCR meter. We confirm that the combination of the capacitance meter and the strain sensor measures the length of the artificial muscle with a high sampling rate of 10 kHz.

Rendering forces to the user is one of the main goals of haptic technology. While most force-feedback interfaces are robotic manipulators, attached to a fixed frame and designed to exert forces on the users while being moved, more recent haptic research introduced two novel important ideas. On one side, cutaneous stimulation aims at rendering haptic stimuli at the level of the skin, with a distributed, rather than, concentrated approach. On the other side, wearable haptics focuses on highly portable and mobile devices, which can be carried and worn by the user as the haptic equivalent of an mp3 player. This paper presents a light and simple wearable device (CUFF) for the distributed mechano-tactile stimulation of the user's arm skin with pressure and stretch cues, related to normal and tangential forces, respectively. The working principle and the mechanical and control implementation of the CUFF device are presented. Then, after a basic functional validation, a first application of the device is shown, where it is used to render the grasping force of a robotic hand (the Pisa/IIT SoftHand). Preliminary results show that the device is capable to deliver in a reliable manner grasping force information, thus eliciting a good softness discrimination in users and enhancing the overall grasping experience.

Brain tumor, be it primary or metastatic, is usually life threatening for a person at any age. The risks involved in carrying out surgery within a brain can cause severe anxiety in patients. However, primary surgical resection which is one of the most effective ways of treating brain tumors can have a tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this work, we present the design and kinematic analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot (MINIR-II). It is comprised of an inter-connected inner spring and an outer spring to enable motion in three dimensions (3D). Our design provides improved dexterity with higher degrees of freedom (DoFs) and independent joint control because of the centrally routed tendon configuration. Since the robot itself is made of plastic (except the electrocautery probes and SMA spring actuators), it is MRI-compatible and allows surgeons to track the real-time location of the robot in the brain and to reach the brain tumor target. The inter-connected spring and the outer spring are manufactured individually in a single piece using rapid prototyping technology at low cost to make it disposable after single use. Our three-segment robot has two DoFs at each segment with both joints controlled by two pairs of MRI-compatible SMA spring actuators. We also present a detailed kinematic analysis of the robot, simulation of the robot motion, and experimental evaluation of the robot motion using vision feedback.

We present the design and implementation of a small Vertical-Take-Off-and-Landing (VTOL) aircraft. The vehicle requires minimal additional components to achieve the hover capability and is thus very efficient in forward flight. We improve over the state of the art by using a single controller in all flight modes without using blending between hover condition and fixed wing controllers or gain scheduling. We present a compact airflow estimation model for VTOL airframes which rely on the slipstream across control surfaces for hover attitude control. Furthermore we show attitude and position control results in simulation. Finally we show outdoor flight experiments validating our simulation results.