IROS 2017 论文列表

This paper develops a custom motion capture system that uses vision-based methods to rapidly and accurately track the body and leg position/orientation of a 1.43g legged microrobot, the Harvard Ambulatory MicroRobot (HAMR). Two new generalized metrics for quantifying locomotion performance are defined: amplitude-normalized stride correlation, and percent ineffective stance. Six different gaits are run on HAMR to validate the experimental setup and establish baseline performance. Furthermore, HAMR is compared with the cockroach, Blaberus Discoidalis, and with other legged robots. Future studies can leverage the experimental setup to study gait selection and transitions for small legged systems.

We present a hierarchical control approach that can be used to fulfill autonomous flight, including vertical takeoff, landing, hovering, transition, and level flight, of a quadrotor tail-sitter vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). A unified attitude controller, together with a moment allocation scheme between elevons and motor differential thrust, is developed for all flight modes. A comparison study via real flight tests is performed to verify the effectiveness of using elevons in addition to motor differential thrust. With the well-designed switch scheme proposed in this paper, the aircraft can transit between different flight modes with negligible altitude drop or gain. Intensive flight tests have been performed to verify the effectiveness of the proposed control approach in both manual and fully autonomous flight mode.

We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a sensory glove that is capable of jointly collecting data of finger poses, hand poses, as well as forces on palm and each phalanx. Specifically, the sensory glove employs a network of 15 IMUs to measure the rotations between individual phalanxes. Hand pose is then reconstructed using forward kinematics. Contact forces on the palm and each phalanx are measured by 6 customized force sensors made from Velostat, a piezoresistive material whose force-voltage relation is investigated. We further develop an open-source software pipeline consisting of drivers and processing code and a system for visualizing hand actions that is compatible with the popular Raspberry Pi architecture. In our experiment, we conduct a series of evaluations that quantitatively characterize both individual sensors and the overall system, proving the effectiveness of the proposed design.

Performing remote manipulation tasks by tele-operation with limited bandwidth, communication delays and environmental differences is a challenging problem. In this paper, we learn a task-parameterized generative model from the teleoperator demonstrations using a hidden semi-Markov model that provides assistance in performing remote manipulation tasks. We present a probabilistic formulation to capture the intention of the teleoperator, and subsequently assist the teleoperator by time-independent shared control and/or time-dependent autonomous control formulations of the model. In the shared control mode, the model corrects the remote arm movement based on the current state of the teleoperator; whereas in the autonomous control mode, the model generates the movement of the remote arm for autonomous task execution. We show the formulation of the model with virtual fixtures and provide comparisons to benchmark our approach. Teleoperation experiments with the Baxter robot for reaching a movable target and opening a valve reveal that the proposed methodology improves the performance of the teleoperator and caters for environmental differences in performing remote manipulation tasks.

Frogs are capable of multiple locomotion modes including jumping and swimming, which enables them to adapt to various environmental conditions. This paper demonstrates a frog-inspired robot, which can mimic the swimming motion of a natural frog. The robot is developed based on dielectric elastomer actuators, which exhibits muscle-like behavior such as large voltage-induced deformation, high energy density, fast response and low weight. Inspired by the webbed feet of a frog, the foot actuator of the swimming robot is able to increase its projected area by 66% when subject to high voltage. Actuation of the foot actuators can significantly improve the averaged peak thrust by 34.5%. The total mass of the two dielectric elastomer actuators is 14g which only accounts for 13% of its total mass of 108g. The measured average swimming speed for a square wave voltage of 5kV and 0.25Hz is 19mm/s for the swimming robot. Future work of the project includes optimal design and control of this soft robot.

In this paper, a comprehensive friction model for collaborative industrial robot joints is proposed which takes into account the velocity, temperature and load torque effects. The model indicates that the velocity and temperature have a strong influence on viscous friction nonlinearly, whereas load torque significantly influences the Coulomb friction linearly and causes a slight Stribeck effect. The friction characteristics were investigated on the collaborative robot joints which are equipped with temperature sensor and harmonic reducer. The proposed model has been verified successfully under wide velocity, temperature and load range. In addition, the permillage of the rated current of the motor is used to scale the value of joint torque, so that the model can be applied in common industrial robot.

We present a hybrid control framework for solving a motion planning problem among a collection of heterogenous agents. The proposed approach utilizes a finite set of low-level motion primitives, each based on a piecewise affine feedback control, to generate complex motions in a gridded workspace. The constraints on allowable sequences of successive motion primitives are formalized through a maneuver automaton. At the higher level, a control policy generated by a shortest path non-deterministic algorithm determines which motion primitive is executed in each box of the gridded workspace. The overall framework yields a highly robust control design on both the low and high levels. We experimentally demonstrate the efficacy and robustness of this framework for multiple quadrocopters maneuvering in a 2D or 3D workspace.

In this paper, we present a control strategy that enables intuitive physical human-robot collaboration with mobile manipulators equipped with an omnidirectional base. When interacting with a human operator, intuitiveness of operation is a major concern. To this end, we propose a redundancy solution that allows the mobile base to be fixed when working locally and moves it only when the robot approaches a set of constraints. These constraints include distance to singular poses, minimum of manipulability and distance to objects and angular deviation. Experimental results with a Kuka LWR4 arm mounted on a Neobotix MPO700 mobile base validate the proposed approach.

Automated assembly operations have traditionally been carried out using “hard automation”, where all parts to be handled during the operation are located at known positions with very high accuracy. Such automation solutions are often mechanically very expensive to implement and also the programming time can be substantial. Hence, hard automation is only suitable for large batch sizes. However, with the upcoming programming paradigms based on joining preprogrammed subtasks (skills) to a complete solution, the programming time can be shortened significantly. Skills are subtasks with well defined interfaces in terms of pre- and postconditions. However, skills still rely on high accuracy as there is no methodology for handling the propagation of inaccuracies when combining the skills. Therefore, skills are typically only useful for hard automation situations. In cases with smaller batch sizes, the high accuracy property will have to be loosened to lower the costs on the hardware side. In this paper, we extend the skill interfaces with a unified formalism for how the skill propagates inaccuracies. The formalism includes how the inaccuracy propagation for a skill can be learned and how the learned propagations can be used to join the skills while also predicting how inaccuracies will propagate through the entire skill chain. We also briefly discuss the perspectives using the formalism to offline search for the most robust combinations of skills for the entire task.

Accurate biological agent localization offers the opportunity for both researchers and institutions to gain new knowledge about individual and group behaviors of biosystems. This paper presents a sensor-fusion approach for tracking biological agents, combining the data from automated video logging with magnetic, angular rate, and gravity (MARG) and inertial measurement unit (IMU) data, with professionally managed dolphins as the representative example. Our method of video logging allows for accurate and automated dolphin location detection using a combination of Laplacian of Gaussian (LoG) and multi-orientation elliptical blob detection. These data are combined with MARG/IMU measurements to generate a localization estimate through a series of drift-correcting Kalman and gradient-descent filters, finalized with Incremental Smoothing and Mapping (iSAM2) pose-graph localization.

This paper describes a novel four-limbed robot having high mobility capability in extreme environments. At disaster sites, there are various types of environments where a robot must move such as rough terrain with possibility of collapse, narrow places, stairs, vertical ladders and so forth. In this paper, first we categorized extreme environments based on three indexes: unevenness, narrowness, and inclination. To move in such extreme environments, we proposed a four-limbed robot having various locomotion styles such as bipedal/quadrupedal walking, crawling and ladder climbing. The main contribution of this paper is the concept and hardware design of the four-limbed robot. We developed a prototype of the four-limbed robot having commonly structured limbs. The number of DoF for the whole body is 29, with 7-DoFs in each limb and 1-DoF in the trunk. The robot weight is 110 kg, and the height is 1,290 mm when standing on two legs. The end-effector has hook-like shape. Verification of the prototype robot is conducted through simulations and experiments.

This paper proposes a new concept called “a flexible exoskeleton” and presents the design of a wearable walking assistance device to provide physical gait assistance for the elderly. To overcome some limitations of previous wearable walking assistance devices, 3 novel mechanisms are proposed: 1) flexible thigh frames that withstand the designated load direction, 2) an adjustable flexible hip brace surrounding the wearer's body, and 3) slim and backdrivable actuators that minimize the maximum height and resistance. The proposed walking assistance device has reduced metabolic energy consumption by 13.2±4% compared with a free condition.

Children with severe motor impairments require the use of assistive devices to perform activities of daily living. Brain-machine interfaces are not suited for children due to various factors such as surgical risks. In this paper, we present a non-invasive body-machine interface where upper body movements are recorded by wireless inertial measurement units (IMUs) and used to control a robot arm. We develop a novel approach, called the virtual body model (VBM), which allows for control of high number of degrees-of-freedom (DOF). Our results show that participants could use the VBM to control up to five DOFs of a robot arm, and perform a pick-and-place task. Even with minimal training, trajectories of the end effector were smooth and positioning was accurate. These results show the potential of this safe, non-invasive approach to control high DOFs in children.

To improve the search efficiency of robotic grasping detection, this paper presents a novel search algorithm based on the image pyramid. It significantly reduces the search space for grasping position detection using the coarse-to-fine strategy. The proposed method searches the positions from the top layer of the pyramid, and initializes the search area at the next layer. The sparse automatic encoder is employed to construct the model which is used to evaluate the grasp quality. The experimental results demonstrate that the proposed search algorithm can improve efficiency of the robotic grasping detection with the comparative performance on the grasp quality.

This study proposes a novel facial wearable robot for supporting eyelid movements. The robot is designed for people with facial paralysis, especially on one side of the face. People with facial paralysis are not able to blink, which leads to dry eyes and could cause permanent damage to the cornea. They are also prone to developing synkinesis during recovery, and a biofeedback-enabled rehabilitation is considered to prevent it. Based on these, we developed a robot system to support eyelid movements on the paralyzed side, based on the eye closure on the healthy side. The robot has a novel mechanism for supporting the eyelid control, made from soft material, which is called Eyelid Gating Mechanism (ELGM). ELGM deforms by simple rotational or linear actuation inputs and its deformation is customized to eyelid movements. Therefore, this robot can provide non-invasive and gentle support for eyelid movements. In this paper, we present the development and performance evaluation of the developed facial wearable robot.

In this paper, we present a complete algorithm for exploration of unknown environments containing disjoint obstacles with multiple robots. We propose a distributed approach considering two main cases. In the first one, the obstacles are distinguishable, i.e., each obstacle is uniquely identifiable, which can be imagined as each obstacle having a different color, and in the second case, the obstacles are not distinguishable. Two possible applications of our algorithms are: 1) Search of a static object in an unknown environment. 2) Damage verification in unknown environments composed by multiple elements (e.g. buildings). The main contributions of this work are the following: 1) The algorithms guarantee exploring the whole environment in finite time even though each robot does not have full information about the part of the environment explored by other robots. 2) The method only requires limited communication between the robots. In both cases distinguishable and indistinguishable obstacles, the robots communicate only at rendezvous. 3) The algorithm scales well to hundreds of robots and obstacles. We tested in several simulations the performance of our algorithms for both cases, in terms of the distance traveled by the robots.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas it will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAV) to cover wildfire is promising because it can replace humans for fire tracking, reducing hazards and saving operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

Search and rescue missions and surveillance require finding targets in a large area. These tasks often use unmanned aerial vehicles (UAVs) with cameras to detect and move towards a target. However, common UAV approaches make two simplifying assumptions. First, they assume that observations made from different heights are deterministically correct. In practice, observations are noisy, with the noise increasing as the height used for observations increases. Second, they assume that a motion command executes correctly, which may not happen due to wind and other environmental factors. To address these, we propose a sequential algorithm that determines actions in real time based on observations, using partially observable Markov decision processes (POMDPs). Our formulation handles both observations and motion uncertainty and errors. We run offline simulations and learn a policy. This policy is run on a UAV to find the target efficiently. We employ a novel compact formulation to represent the coordinates of the drone relative to the target coordinates. Our POMDP policy finds the target up to 3.4 times faster when compared to a heuristic policy.

Recent work in the field of bio-inspired robotic systems has introduced designs for modular robots that are able to assemble into structures (e.g., bridges, landing platforms, fences) using their bodies as the building components. Yet, it remains an open question as to how to program large swarms of robotic modules so that the assembly task is performed as efficiently as possible. Moreover, the problem of designing assembly algorithms is compounded by the scale of these systems, and by the lack of centralized guidance in unstructured environments. The main contribution of this work is a decentralized algorithm to assemble structures with modular robots. Importantly, we coordinate the robots so that docking actions can be parallelized. We show the correctness of our algorithm, and we demonstrate its scalability and generality through multiple scenarios in simulation. Experiments on physical robots demonstrate the validity of our approach in real-world settings.