IROS 2015 Papers

This paper introduces a novel concept for a (self-)adaptive, a.k.a. underactuated, finger which presents the particularity of exploiting the traditional motion generated by standard robotic grippers for actuation. The kinematic structure of this finger allows for seizing objects of various shapes in both enveloping and precision grasps without having any actuator in its joints. Following a kinetostatic analysis, the forces generated by the finger will be obtained and analyzed. The performance of a practical implementation of the design will be subsequently evaluated and compared with another architecture inspired by a commercially available product. Finally, collision modes for safe human interaction will be presented and followed by the demonstration of a prototype.

The overall performance of sampling-based motion planning algorithms strongly depends on the use of suitable sampling and connection strategies, as well as on the accuracy of the distance metric considered to select neighbor states. Defining appropriate strategies and metrics is particularly hard when considering robot dynamics, which is required to treat constrained motion planning problems for quadrotors. This paper presents an accurate but computationally fast quasi-metric to determine the proximity of dynamic states of a quadrotor, and an incremental state-space sampling technique to avoid generating local trajectories that violate kinodynamic constraints. Results show that the integration of the proposed techniques in RRT-based and PRM-based algorithms can drastically decrease computing time, up to two orders of magnitude.

The design of a stable and robust force controller is one of the most important and difficult tasks in rehabilitation robotics. In previous works, the Universal Haptic Pantograph (UHP) was presented as an alternative to conventional arm rehabilitation after a stroke. This robot is composed by a Series Elastic Actuator (SEA) and a Pantograph. In this work an enhanced force control for the UHP is presented. The proposed controller uses the robot model to estimate the contact force without direct measurement and to compensate nonlinearities in the actuators. In order to prove the effectiveness of the approach, several tests are carried out in simulation and experimentally. Results reveal that mean of tracking errors between desired and actual force is smaller than 0.1 N, which is significantly improved compare to that (around 2.5 N) shown in previous results of UHP, indicating that the proposed force control is likely to enhance haptic performance of the UHP.

Often times in multi-target, Unmanned Aerial System (UAS) applications, minimizing energy consumption is a common goal. In simplified cases, this problem can be solved by minimizing the path length between targets using the Traveling Salesman Problem with Neighborhoods (TSPN). However, this method does not take into account the attitude of the UAS. In addition, minimum path length does not always correlate to minimum energy consumption. This research proposes an approach to both problems. A method is proposed to more accurately model multi-target, six-rotor aircraft missions using the Generalized TSPN (GTSPN), which can account for the non-convex characteristics of the neighborhoods created by accounting for variable attitude. To address the energy consumption problem, a heuristic procedure is proposed to solve GTSPN instances using a genetic algorithm approach that incorporates a fine-tuned, dynamic model of a six-rotor to simulate energy consumption. Numerical simulations are performed on randomly generated instances with up to 50 targets to show that the proposed procedure determines tours for a given instance within a 1% standard deviation error from the best-known tour and on average improves the results obtained with the TSPN formulation up to 11.4%.

The shortage of physicians afflicting developed countries encourages engineers and doctors to collaborate towards the development of telemedicine. In particular, robotic systems have the potential for helping doctors making examination. A very common examination that can be the goal of a robotic system is palpation. Most of the robotics systems that have been developed for palpation present interesting features such as integrating augmented reality environments or allowing for hand free interaction. In this paper we present a novel palpation system that allows us to perform virtual palpation of real objects by means of a haptic and an augmented reality feedback. This system features an encountered-type haptic interface in which the haptic feedback is calculated by a collision detection algorithm that is based on online recording of the surface to be touched. The system allows the users to remove their hand from the haptic interface end-effector that follows the user's hand thanks to the tracking performed by a Leap Motion. We show that the system provides a natural interaction during the contact-non contact switch, a suitable force during indentation, and it allows to discriminate objects within the body through the haptic channel.

This work is based on the idea that, like in biological organisms, basic motivated behavior can be represented in terms of approach and avoidance. We propose a model for emotional modulation of the robot peripersonal space. That is to say, an area both reachable and secure; the space where the robot can act. The contribution of this paper is a generic model that integrates various stimuli to build a representation of reachable and comfort areas used to control robot behavior. Such an architecture is tested is three experiments using real robot and simulations. It is compared with two altered architecture versions. We show how our model allow the robot to solve various tasks, display emotionally colored behaviors and account for results from psychological studies.

This paper provides an overview of the embedded joint-space control approach developed for THOR, a new series elastic humanoid. The 60 kg robot features electromechanical linear series elastic actuators (SEAs), enabling low-impedance control of each joint in the lower body via linear to rotary and parallel mechanisms. We present a distributed joint impedance control framework that leverages a custom dual-axis motor controller to track position, velocity, and torque setpoints for each pair of joints. The required actuator forces are tracked using an inner force control loop combining feedforward and PID control with a model-based disturbance observer (DOB). Unlike previous approaches, we utilize an inverse plant model based on the open-loop actuator dynamics to simplify tuning of the cascaded controller by decoupling DOB estimates from the inner loop gains. The effectiveness of the proposed approach is verified through trajectory tracking and dynamic walking experiments conducted on the THOR humanoid utilizing a complementary optimization-based whole-body controller.

In this study, cell-laden alginate-poly-L-lysine (PLL) hydrogel structures with arbitrary shapes were constructed based on electrodeposition method. Electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned anode electrode, and cell-laden alginate gel structures were formed. The several different shapes of gel were generated at one time by predefined micro-patterns. The micro-patterned electrode was fabricated by coating photoresist on Fluorine-doped tin oxide (FTO) glass slide. Alginate-PLL encapsulation technique was introduced to this platform forming alginate-PLL hydrogel structures. This proposed method can lead to more accurate and creative studies of fabricating cell-laden scaffold for tissue engineering.

This paper proposes a finger-mounted walk controller for a complete paraplegic patient wearing a powered exoskeleton. The wearable controller mounted on both hands of the patient compensates impaired efferent and afferent nerves of the patient through healthy index fingers. The user controls his hip joint angle through force sensors of the controller as if he controls a position of his swing leg voluntarily. Simultaneously, he perceives hip joint angles through an electric stimulation device of the controller. This paper introduces a walking robot which simulates the patient body with lofstrand crutches on his arms for preliminary experiments before a clinical trial. The finger-mounted walk controller is used to control a position of the robot's swing leg. A 5-meter walk experiment is conducted to evaluate feasibility of the controller. We confirmed that the user controlled a stride based only on the electric stimulation feedback and no visual feedback, and that the robot finished walking 5-meter in three minutes.

A collective actuator is a self-reconfigurable modular-robotic structure which produces useful mechanical work through simultaneous reconfiguration of its constituent units. An actuator is additionally called scalable if its force-to-weight ratio does not depend on the number of its member modules. In this work, we consider scalable collective actuators built from spherical catoms with two connection types: strong but fixed and weak but mobile. We investigate how to construct these actuators in such a way, as to maximize their force-to-weight ratio. We present a number of designs of high strength, whose force capacities significantly exceed those of similar actuators reported previously.

A Next Best View estimate may guide processes of 3D reconstruction and exploration to completeness within reasonable time. For the evaluation to be useful, the Next Best View computation itself must be effective in terms of time and accuracy. It needs to be model-free to hold for any geometry of the 3D scene. In this work, we compare the effectiveness of different approaches to Next Best View evaluation. A 3D occupancy grid map, allowing for fast lookup and ray casting, serves as a foundation for our evaluation. We tested naive, state-of-the-art and novel algorithms on data acquired from both indoor and outdoor environments. We demonstrate that the most effective volumetric algorithm is a novel one that exploits spatial hierarchy, utilizes frontiers, and avoids redundant ray casting.

In retinal microsurgery, membrane peeling is a standard procedure requiring the delamination of a thin fibrous membrane adherent to the retina surface by applying very small forces. Robotic devices with combined force-sensing instruments have significant potential to assist this procedure by facilitating membrane delamination through induced micro-vibrations. However, defining the optimal frequency and amplitude for generating such vibrations, and updating these parameters during the procedure is not trivial. Automatic adjustment of these parameters via an adaptive control scheme is possible only if the individual parameter effects on delamination behavior are known. This study presents an experimental exploration of how micro-vibration amplitude and frequency affect membrane peeling forces alone. Combining a micromanipulator and a force-sensing micro-forceps, several peeling experiments were done on artificial phantoms (bandages) and inner shell membrane of raw chicken eggs. In the tested range of micro-vibration frequencies (10–50 Hz) the average delamination force was minimized mostly at 30 Hz for the bandages and at 50 Hz for the egg membranes. Increasing the micro-vibration amplitude from 50 µm up to 150 µm provided further reduction in average force, thus facilitated membrane delamination.

In this work, we present an algorithm for improving collaborator performance on sequential manipulation tasks. Our agent-decoupled, optimization-based, task and motion planning approach merges considerations derived from both symbolic and geometric planning domains. This results in the generation of supportive behaviors enabling a teammate to reduce cognitive and kinematic burdens during task completion. We describe our algorithm alongside representative use cases, with an evaluation based on solving complex circuit building problems. We conclude with a discussion of applications and extensions to human-robot teaming scenarios.

We propose a method for realizing effective dynamic motion learning in a flexible-joint robot using motor babbling. Flexible-joint robots have recently attracted attention because of their adaptiveness, safety, and, in particular, dynamic motions. It is difficult to control robots that require dynamic motion. In past studies, attractors and oscillators were designed as motion primitives of an assumed task in advance. However, it is difficult to adapt to unintended environmental changes using such methods. To overcome this problem, we use a recurrent neural network (RNN) that does not require predetermined parameters. In this research, we propose a method for facilitating effective learning. First, a robot learns simple motions via motor babbling, acquiring body dynamics using a recurrent neural network (RNN). Motor babbling is the process of movement that infants use to acquire their own body dynamics during their early days. Next, the robot learns additional motions required for a target task using the acquired body dynamics. For acquiring these body dynamics, the robot uses motor babbling with its redundant flexible joints to learn motion primitives. This redundancy implies that there are numerous possible motion patterns. In comparison to a basic learning task, the motion primitives are simply modified to adjust to the task. Next, we focus on the types of motions used in motor babbling. We classify the motions into two motion types, passive motion and active motion. Passive motion involves inertia without any torque input, whereas active motion involves a torque input. The robot acquires body dynamics from the passive motion and a means of torque generation from the active motion. As a result, we demonstrate the importance of performing prior learning via motor babbling before learning a task. In addition, task learning is made more efficient by dividing the motion into two types of motor babbling patterns.

Simultaneous motion control of multiple joints has many potential applications such as Tai Chi, Yoga etc. The capability of vibrotactile cues to assist this kind of motor task has not been well explored. In this paper, we studied the effect of vibrotactile cues for guiding procedural motion of two joints on human's upper limbs. By mounting eight vibrotactile motors on two arms, we performed two experiments to measure human's perception and motor performance in response to the vibrotactile commands. In the first experiment, we measured perceptual performance of correctly identifying the location of two active vibrotactile cues. The difference between sustained, pulsed and hybrid vibration conditions was compared. To explore the possible reasons leading to the wrong perception results, the correct rate was ranked among different combinations of cues. In the second experiment, the correct rate of procedural motion control of two joints was measured, while vibrotactile cues were used as guidance signals to produce the motion command. The results showed that average correct rate of two cues localization on upper limbs was as high as 98%, while the average correct rate of procedural motion control of two joints was only 86%. Further analysis revealed that low correct rate of procedural motion control was caused by the unnatural motion pattern, i.e. two joints on a same arm and rotate in opposite directions.

EMY (Enhancing MobilitY) is an exoskeleton dedicated to the evaluation of Brain Machine Interface during clinical trials. This paper presents the first version of EMY restricted to upper limbs with four actuated joints per arm. Since an evaluation of a BMI controlled exoskeleton by a disabled person requires clinical trials, a risk management process should be conducted with medical standards as references. More than an exhaustive architecture description of EMY, this paper details relationships between risk management and the proposed technical solutions in order to justify design options. EMY's design is upgradable and future versions of EMY will concern the addition of lower limbs and prehension capability to ultimately obtain a full-body exoskeleton suitable for testing brain controlled locomotion and manipulation tasks.

Modern simulations feature crowds of AI-controlled agents moving through dynamic environments, with obstacles appearing or disappearing at run-time. A dynamic navigation mesh can represent the traversable space of such environments. The A* algorithm computes optimal paths through the dual graph of this mesh. When an obstacle is inserted or deleted, the mesh changes and agents should re-plan their paths. Many existing re-planning algorithms are too memory-intensive for crowds, or they cannot easily be used on graphs where vertices and edges are added or removed. In this paper, we present Dynamically Pruned A* (DPA*), an extension of A* for re-planning optimal paths in dynamic navigation meshes. DPA* has similarities to adaptive algorithms that make the A* heuristic more informed based on previous queries. However, DPA* prunes the search using only the previous path and its relation to the dynamic event. We describe the four re-planning scenarios that can occur; DPA* uses different rules in each scenario. Our algorithm is memory-friendly and robust against structural changes in the graph, which makes it suitable for crowds in dynamic navigation meshes. Experiments show that DPA* performs particularly well in complex environments and when the dynamic event is visible to the agent. We integrate the algorithm into crowd simulation software to model large crowds in dynamic environments in real-time.

Quadrupedal locomotion on sloped terrains poses different challenges than walking in a mostly flat environment. The robot's configuration needs to be explicitly controlled in order to avoid slipping and kinematic limits. To this end, information about the terrain's inclination is required for carefully planning footholds, the pose of the main body, and modulation of the ground reaction forces. This is even more important for dynamic trotting, as only two support legs are available to compensate for gravity and drive a desired motion. We propose a reliable method for estimating the parameters of the terrain quadrupedal robots move on, in the face of limited perception capabilities and drifting robot pose estimates. By fusing inertial measurements, kinematic data from joint encoders and contact information from force sensors, the local inclination can be robustly estimated and used to optimize the contact forces to reduce slippage. The estimated terrain information, namely the pitch and roll angles of the ground plane, is exploited in an extended version of our previous model-based control approach. Our improved control framework enabled StarlETH, a medium-sized, fully autonomous, torque-controllable quadrupedal robot, to trot on slopes of up to 21°.

Robots are often used to perform similar tasks on vastly different scales. When utilizing traditional design approaches, this typically requires a full redesign and optimization at each specified size, a costly and time intensive process. In this work, we propose an alternative approach based on the principle of dynamic similarity. With this method, an initial design may be conceived at any size and scaled to any other without needing to re-optimize either physical or control variables. In the following study, we derive a generalized scaling method for this application that not only preserves system performance, but also affords flexibility in the scaling process. The preservation of dynamic similarity is validated in simulation. We further explore the application for robot-to-robot scaling, flexibility afforded by an additional free parameter, and the implications of scaling on both power requirements and structural loading. With these insights, we provide an improved methodology for the efficient and cost-effective development of scaled robotic systems.