ICRA 2015 Papers

Moball is a wind-driven spherical robot equipped with sensors for in-situ observation of scientifically important and windy environments, e.g., the Earth's polar regions, Mars, and Saturn's moon Titan. More importantly, Moball incorporates an internal triaxial set of linear electromagnetic generators which can be used to harvest wind energy for long-duration self-sustained operation, or to bias its' wind-driven motions as a form of steering. This paper describes our process to optimize the design of a coreless tubular linear generator for Moball so as to improve energy generation and motion control capabilities with the minimal moving generator mass. The performance of three different types of movers was analyzed with the help of finite element analysis. We determined a final optimized structure and its' dimensions involving a single dipole PM and novel slope-shaped back-irons. A prototype of a single-axis linear generator with a length of 0.8 m was fabricated and assembled. Drop and rotating tests were performed to measure the generated power with this machine. The maximum generated power in the rotating test was 1.05 W at 19 rpm when the load resistance was 40 Ω. The experimental results agreed well with our model predictions. The paper concludes with an overview of the current Moball prototype and ongoing work. The design process developed in this paper can serve as a guideline for future design of energy scavenging systems for robots.

Independent mobility is an important aspect of an individual's life and must sometimes be augmented by use of an assistive device such as a wheeled walker or cane following a fall, injury, or functional decline. Physical therapists perform functional gait assessments to gauge the probability of an individual experiencing a fall and often recommend use of a walker, cane, or walking stick to decrease fall risk. Our team has developed a clinical assessment tool centered on a standard walking cane embedded system that can enhance a therapist's observation-based gait assessment with use of additional objective and quantitative data. This system can be utilized to detect timing and speed of cane placement, angular acceleration of the cane, and amounts of weight borne on the cane. This system is designed to assist physical therapists at the basic level in collection of objective data during gait analysis, to facilitate appropriate assistive gait device prescription, to provide patients and therapists feedback during gait training, and to reduce wrist and shoulder injuries with cane usage. However, more importantly, using the plethora of objective data that can be obtained from this cane, automated gait analysis and gait pattern classification can be performed to understand a patient's walking performance.

Robotic systems, such as prosthetics and exoskeletons, offer people suffering from motor impairments a chance to regain lost physical functionality. However, the neural control that individuals are able to exert over these robots is currently limited. This is due to both lack of control authority in many degrees of freedom and insufficient sensory feedback through the human-robot interface. We propose that haptic feedback is paramount for accurate and efficient control of robots via brain-computer interfaces (BCIs). Skin stretch at the fingertip is a novel form of haptic feedback for improving BCI-based robot control. In this paper, we describe the design of a BCI-driven skin-stretch device, assess several control paradigms for this device, and evaluate its effectiveness in a small user study. We show that BCI-based movement-intent classification improved in the presence of skin-stretch feedback for 3 of 4 healthy individuals controlling a computer cursor via an inexpensive, commercial electroencephalography-based (EEG) BCI.

Parallel elastic actuators (PEAs) have shown the ability to reduce the energy consumption of robots. The problem with regular PEAs is that it is not possible to freely choose at which instant or configuration to store or release energy. This paper introduces the concept and the design of the Bi-directional Clutched Parallel Elastic Actuator (BIC-PEA*), which reduces the energy consumption of robots by loading and unloading the parallel spring in a controlled manner. The concept of the BIC-PEA consists of a spring that is mounted between the two outgoing axes of a differential mechanism. Those axes can also be locked to the ground by two locking mechanisms. At any position, the BIC-PEA can store the kinetic energy of a joint in the spring such that the joint is decelerated to zero velocity. The spring energy can then be released, accelerating the joint in any desired direction. In our prototype of 202 g, the energy that can be stored in the spring is 0.77 J. When disengaged, the friction that the mechanism adds is negligible. The current maximum over-all efficiency is 62 %, which is about 55% more than what generally can be achieved by recapturing the energy electrically. Its relatively high efficiency and controllability make the BIC-PEA a promising concept for reducing the energy consumption of robots.

The percutaneous coronary interventions (PCI) require complex operating skills of the interventional devices and make the surgeons being exposed to heavy X-ray radiation. Accurate delivery of the interventional devices and avoiding the radiation are especially important for the surgeons. This paper presents a novel dedicated dual-finger robotic hand (DRH) and a console to assist the surgeons to deliver the interventional devices in PCIs. The system is designed in the master-slave way which helps the surgeons to reduce the exposure to radiation. The mechanism of the DRH is bio-inspired and motions are decoupled in kinematics. In PCI procedures, the accuracy of the guidewire delivery and the catheter tip placement have significant effects on the surgical results. The performances of the DRH in delivering the guidewire and the balloon/stent catheter were evaluated by three surgical manipulations. The results show that the DRH has the ability to deliver the guidewire and the balloon/stent catheter precisely.

In this paper, a wearable hand exoskeleton with force-controllable and compact actuator modules is proposed. In order to apply force feedback accurately while allowing natural finger motions, the exoskeleton linkage structure with three degrees of freedom (DOFs) was designed, which was inspired by the muscular skeletal structure of the finger. As an actuating system, a series elastic actuator (SEA) mechanism, which consisted of a small linear motor, a manually designed motor driver, a spring and potentiometers, was applied. The friction of the motor was identified and compensated for obtaining a linearized model of the actuating system. Using a LQ (linear quadratic) tuned PD (proportional and derivative) controller and a disturbance observer (DOB), the proposed actuator module could generate the desired force accurately with actual finger movements. By integrating together the proposed exoskeleton structure, actuator modules and control algorithms, a wearable hand exoskeleton with force-controllable and compact actuator modules was developed to deliver accurate force to the fingertips for flexion/extension motions.

In this paper, we propose a novel design of compliant safe joint, which has flexibility when the work load exceeds a predefined threshold. The compliance is generated by a spring. We design a special transmission mechanism to convert axial motion into circumferential motion such that the linear compliance can be converted into circular one. When the end-effector of a surgical robot actuated by the compliant safe joints collides with patient's body, the compliance of the joints will protect the patient by absorbing part of the collision energy. Because of the system's special mechanical structure, the control methods should be different when it works under different states. We propose a simple algorithm to choose control methods so that the system can work both under rigid and flexible states with different controllers. We have built a prototype to validate the design and the controller.

This paper presents a novel approach to balance assistance and joint load reduction for human bipedal walking. We introduce a new type of wearable robot, called Supernumerary Robotic Limbs (SRL), that provides two additional legs for augmenting stability and reducing the loads on human leg joints. Unlike exoskeletons, the SRL is kinematically independent of the human skeletal structure, and can therefore take an arbitrary posture to provide optimal assistance in coordination with human motions. Furthermore, unlike crutches, canes, and other balance assistance equipment, the SRL can provide balancing support autonomously and thereby free the human arms from holding those tools. First, the new design concept and balance assistance strategy are described, followed by kinematic and static modeling. Two gate patterns of the combined human and SRL are discussed. Optimal gate synthesis that maximizes the area of support polygon is discussed. Finally, the gate control strategies are implemented on a prototype SRL, using body motion sensors to enable real-time, seamless coordination between the user and the robot.

Quadrupedal running is an efficient form of locomotion found in nature, which serves as an inspiration for robotics. We believe that a resonance-based approach is the path towards energy-efficient legged locomotion and running robots. The first step in working towards this goal is creating an energy-efficient one-legged hopper. Such a one-legged hopper was designed and constructed. The impact efficiency of the mechanism is calculated analytically, determined in simulation and measured with the prototype. The impact efficiency as calculated from the experiments is found to be in agreement with the analytical expectation and simulation results. Finally, using an electric motor to inject energy by creating a virtual spring with reverse hysteresis, hopping is achieved.

This paper presents the design and analysis of a quadrotor with a novel configuration, VOOPS-Vertically Offset Overlapped Propulsion System. The objective of this configuration is to increase the payload capacity of a quadrotor without increasing the overall dimension and without compromising endurance. This has been achieved by vertically offsetting the propellers and allowing propeller overlaps so that an increase in propeller size can be achieved without any increase in the overall dimensions. The design details and the dynamic model of the VOOPS configuration are presented. The constraints on the design parameters such as propeller offset and overlap are determined using propeller deflection model and geometry respectively. The effects on change in the design parameters of VOOPS configuration were studied using bench tests and performance simulation of quadrotor with VOOPS configuration were carried out. Practical implementation of VOOPS configuration is also discussed.

This paper presents the design concept of a weight-support lower-extremity-exoskeleton (LEE) with a compliant joint to relieve compressive load in the knee. Along with a leg dynamic model and a knee bio-joint model, a compliant exoskeleton knee-joint has been designed using topology optimization and experimentally evaluated. Results suggest that the gait-based design of a LEE can be divided into two parts in terms of knee angles; compliant coupling and body-weight support. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. Both simulation and experimental results agree with data in-vivo confirming the effectiveness of the LEE in supporting human body-weight during walking, and also provide a basis for computing the internal knee forces as a percentage of bodyweight.

This work integrates visual and physical constraints to perform real-time depth-only tracking of articulated objects, with a focus on tracking a robot's manipulators and manipulation targets in realistic scenarios. As such, we extend DART, an existing visual articulated object tracker, to additionally avoid interpenetration of multiple interacting objects, and to make use of contact information collected via torque sensors or touch sensors. To achieve greater stability, the tracker uses a switching model to detect when an object is stationary relative to the table or relative to the palm and then uses information from multiple frames to converge to an accurate and stable estimate. Deviation from stable states is detected in order to remain robust to failed grasps and dropped objects. The tracker is integrated into a shared autonomy system in which it provides state estimates used by a grasp planner and the controller of two anthropomorphic hands. We demonstrate the advantages and performance of the tracking system in simulation and on a real robot. Qualitative results are also provided for a number of challenging manipulations that are made possible by the speed, accuracy, and stability of the tracking system.

In this paper, we propose a gait tracking and analysis method using a depth image sensor installed on robotic walker. Nowadays, robotic walker not only can assist elders who have suffered deteriorating mobility but also help to provide rehabilitating function to people who have crippled walking ability. This approach is meant to be sufficiently accurate, non-intrusive, and low-cost. The goal of this research is to enable the robotic walker to become more active in terms of walker control and comfortable user experiences thorough gait analysis. In experiment, the accuracy of the proposed 3D leg pose tracking method was evaluated by a motion capture system and the result is quite promising. After actual trials, the proposed method has the potential to be used in a safer and more reliable application such as those assisting elders and specific group of patient subjects.

Real-time dense mapping and pose estimation is essential for a wide range of navigation tasks in mobile robotic applications. We propose an odometry and mapping system that leverages the full photometric information from a stereo-vision system as well as inertial measurements in a probabilistic framework while running in real-time on a single low-power Intel CPU core. Instead of performing mapping and localization on a set of sparse image features, we use the complete dense image intensity information in our navigation system. By incorporating a probabilistic model of the stereo sensor and the IMU, we can robustly estimate the ego-motion as well as a dense 3D model of the environment in real-time. The probabilistic formulation of the joint odometry estimation and mapping process enables to efficiently reject temporal outliers in ego-motion estimation as well as spatial outliers in the mapping process. To underline the versatility of the proposed navigation system, we evaluate it in a set of experiments on a multi-rotor system as well as on a quadrupedal walking robot. We tightly integrate our framework into the stabilization-loop of the UAV and the mapping framework of the walking robot. It is shown that the dense framework exhibits good tracking and mapping performance in terms of accuracy as well as robustness in scenarios with highly dynamic motion patterns while retaining a relatively small computational footprint. This makes it an ideal candidate for control and navigation tasks in unstructured GPS-denied environments, for a wide range of robotic platforms with power and weight constraints. The proposed framework is released as an open-source ROS package.

This paper is about dense depthmap estimation using a monocular camera in workspaces with extensive textureless surfaces. Current state of the art techniques have been shown to work in real time with an admirable performance in desktop-size environments. Unfortunately, as we show in this paper, when applied to larger indoor environments, performance often degrades. A common cause is the presence of large affine texture-less areas like by walls, floors, ceilings and drab objects such as chairs and tables. These produce noisy and worse still, grossly erroneous initial seeds for the depthmap that greatly impede successful optimisation. We solve this problem via the introduction of a new non-local higher-order regularisation term that enforces piecewise affine constraints between image pixels that are far apart in the image. This property leverages the observation that the depth at the edges of bland regions are often well estimated whereas their inner pixels are deeply problematic. A welcome by-product of our proposed technique is an estimate of the surface normals at each pixel. We will show that in terms of implementation, our algorithm is a natural extension of the often used variational approaches. We evaluate the proposed technique using real datasets for which we have ground truth models.

We consider the problem of system identification of helicopter dynamics. Helicopters are complex systems, coupling rigid body dynamics with aerodynamics, engine dynamics, vibration, and other phenomena. Resultantly, they pose a challenging system identification problem, especially when considering non-stationary flight regimes. We pose the dynamics modeling problem as direct high-dimensional regression, and take inspiration from recent results in Deep Learning to represent the helicopter dynamics with a Rectified Linear Unit (ReLU) Network Model, a hierarchical neural network model. We provide a simple method for initializing the parameters of the model, and optimization details for training. We describe three baseline models and show that they are significantly outperformed by the ReLU Network Model in experiments on real data, indicating the power of the model to capture useful structure in system dynamics across a rich array of aerobatic maneuvers. Specifically, the ReLU Network Model improves 58% overall in RMS acceleration prediction over state-of-the-art methods. Predicting acceleration along the helicopter's up-down axis is empirically found to be the most difficult, and the ReLU Network Model improves by 60% over the prior state-of-the-art. We discuss explanations of these performance gains, and also investigate the impact of hyperparameters in the novel model.

This paper presents a multiagent path planning algorithm based on sequential convex programming (SCP) that finds locally optimal trajectories. Previous work using SCP efficiently computes motion plans in convex spaces with no static obstacles. In many scenarios where the spaces are non-convex, previous SCP-based algorithms failed to find feasible solutions because the convex approximation of collision constraints leads to forming a sequence of infeasible optimization problems. This paper addresses this problem by tightening collision constraints incrementally, thus forming a sequence of more relaxed, feasible intermediate optimization problems.We show that the proposed algorithm increases the probability of finding feasible trajectories by 33% for teams of more than three vehicles in non-convex environments. Further, we show that decoupling the multiagent optimization problem to a number of single-agent optimization problems leads to significant improvement in computational tractability. We develop a decoupled implementation of the proposed algorithm, abbreviated dec-iSCP. We show that dec-iSCP runs 14% faster and finds feasible trajectories with higher probability than a decoupled implementation of previous SCP-based algorithms. The proposed algorithm is real-time implementable and is validated through hardware experiments on a team of quadrotors.

Making decisions based on visual input is challenging because determining how the scene should be split into individual objects is often very difficult. While previous work mainly considers decision making and visual processing as two separate tasks, we argue that the inherent uncertainty in object segmentation requires an integrated approach that chooses the best decision over all possible segmentations. Our approach over-segments the visual input and combines the segments into possible objects to get a probability distribution over object compositions, represented as particles. We introduce a Markov chain Monte Carlo procedure that aims to produce exact, independent samples. In experiments, where a 6-DOF robot arm moves object hypotheses captured by an RGB-D visual sensor, our approach of probability distribution based decision making outperforms an approach which utilises the traditional most likely object composition.

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.