IROS 2018 论文列表

In unknown, cluttered environments, robots require online real-time mapping and collision checking in order to navigate robustly. Discrete map representations are inefficient for collision checking as they are expensive in terms of memory and computation. This paper takes a probabilistic approach to local mapping by representing the environment as a Gaussian Mixture Model (GMM) and leverages its geometric properties to enable efficient collision checking given a time-parameterized trajectory. In contrast to current discretization-based methods, a GMM preserves geometric coverage of the environment without losing representation accuracy with varying map resolutions. We introduce a novel GMM local mapping algorithm that can be used with a single depth camera processed on a single CPU, and provide algorithms for collision avoidance given arbitrary trajectory representations. Finally, we provide experimentation results demonstrating safety, efficiency, and data coverage for real-time collision avoidance with a quadrotor navigating in a cluttered environment.

Model-based design of robotic systems has many advantages, among them faster development cycles and reduced costs due to early detections of design flaws. Approximate models are sufficient for many classical robotic applications; however, they no longer suffice for safety-critical applications. For instance, a dangerous situation which has not been detected by model-based testing might occur in a human-robot co-existence scenario since models do not exactly replicate behaviors of real systems-this problem arises no matter how accurate a model is, since even disturbances and sensor noise can cause a mismatch. We address this issue by adding non-determinism to robotic models and by computing the whole set of possible behaviors using reachability analysis. By using reachset conformance, we automatically adjust the required non-determinism so that all recorded behaviors are captured. For the first time this approach is demonstrated for a real robot.

Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid problems involves making trade-offs between generality of system dynamics, generality of problem setups, optimality of solutions, and computational complexity. In this paper, we combine sum-of-squares optimization and dynamic programming to address the reach-avoid problem, and provide a conservative solution that maintains reaching and avoidance guarantees. Our method is applicable to polynomial system dynamics and to general problem setups, and is more computationally scalable than previous related methods. Through a numerical example involving two single integrators, we validate our proposed theory and compare our method to Hamilton-Jacobi reachability. Having validated our theory, we demonstrate the computational scalability of our method by computing the reach-avoid set of a system involving two kinematic cars.

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies.

Virtual reality (VR)systems let users intuitively interact with 3D environments and have been used extensively for robotic teleoperation tasks. While more immersive than their 2D counterparts, early VR systems were expensive and required specialized hardware. Fortunately, there has been a recent proliferation of consumer-grade VR systems at affordable price points. These systems are inexpensive, relatively portable, and can be integrated into existing robotic frameworks. Our group has designed a VR teleoperation package for the Robot Operating System (ROS), ROS Reality, that can be easily integrated into such frameworks. ROS Reality is an open-source, over-the-Internet teleoperation interface between any ROS-enabled robot and any Unity-compatible VR headset. We completed a pilot study to test the efficacy of our system, with expert human users controlling a Baxter robot via ROS Reality to complete 24 dexterous manipulation tasks, compared to the same users controlling the robot via direct kinesthetic handling. This study provides insight into the feasibility of robotic teleoperation tasks in VR with current consumer-grade resources and exposes issues that need to be addressed in these VR systems. In addition, this paper presents a description of ROS Reality, its components, and architecture. We hope this system will be adopted by other research groups to allow for easy integration of VR teleoperated robots into future experiments.

Sampling based methods resulted in feasible and effective motion planning algorithms for high dimensional configuration spaces and complex environments. A vast majority of such algorithms as well as their application rely on generating a set of open-loop trajectories first, which are then tracked by feedback control policies. However, controlling a dynamic robot to follow the planned path, while respecting the spatial constraints originating from the obstacles is still a challenging problem. There are some studies which combine statistical sampling techniques and feedback control methods which address this challenge using different approaches. From the feedback control theory perspective, Reference Governors proved to be a useful framework for constraint enforcement. Very recently, Arslan and Koditschek (2017) introduced a feedback motion planner that utilizes Reference Governors that provably solves the motion planning problem in simplified spherical worlds. In this context, here we propose a “trajectory-free” novel feedback motion planning algorithm which combines the two ideas: random trees and reference governors. Random tree part of the algorithm generates a collision-free region as a set of connected simple polygonal regions. Then, reference governor part navigates the dynamic robot from one region to the adjacent region in the tree structure, ensuring it stays inside the current region and asymptotically reaches to the connected region. Eventually, our algorithm robustly routes the robot from the start location to the goal location without collision. We demonstrate the validity and feasibility of the algorithm on simulation studies.

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot was developed as an integrated ensemble of advanced H/W and S/W components, realizing the robot skills of perception, cognition, safe navigation, grasping, manipulation, and human-robot communication, ample to operate in real, rather challenging domestic environments. The RAMCIP use-cases include proactive assistance provision to user's cooking, eating and medication activities, through discreet user monitoring and robot interventions by reminders and robotic manipulations., RAMCIP can bring the medicine, recognize fallen objects and electric appliance that has been forgotten turned on. It also recognizes the user walking in low-light conditions and turns on the light, as well as detects cases of emergency such as a fall. The robot provides also the user with cognitive training games and stimulates the user to contact with relatives through video-calls. Pilot trials of the RAMCIP robot have been performed in real homes of more than ten different users, in Barcelona, Spain; the video at hand exhibits the robot performing the target use cases.

A motion planning algorithm computes the motion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotient-space decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).

This paper presents a dexterous three degree-of-freedom (3-DOF) wrist mechanism with a large range of motion and uniform manipulability without singular points throughout the entire range of motion. It has a 2-DOF spherical pure rolling joint surrounded by two pairs of actuating wires, the motions of which directly represent the quaternion values of the joint; this joint is therefore named the quaternion joint. Based on this property, it has simple and clear forward and inverse kinematics and high manipulability. By adding a 1-DOF rotation joint at the distal end of the quaternion joint, it can be extended to a 3-DOF joint mechanism. To precisely approximate the spherical pure rolling motion in a confined central space, a novel parallel mechanism composed of three identical supporting linkages was introduced. Unlike conventional parallel mechanisms, it has a compact and simple structure with no interference among the supporting linkages. Because the wrist mechanism is a tendon-driven mechanism, and is thus suitable for lightweight manipulators, it is mounted to a low-inertia manipulator with high stiffness and strength, namely, LIMS2-AMBIDEX, which is an improved version of the authors' previous research. The basic concept and thorough theoretical analysis of the wrist mechanism are described herein, and the simulations and experiments conducted for a quantitative validation are presented.

In this paper, we propose a novel mapping method for robotic navigation. High-quality dense depth maps are estimated and fused into 3D reconstructions in real-time using a single localized moving camera. The quadtree structure of the intensity image is used to reduce the computation burden by estimating the depth map in multiple resolutions. Both the quadtree-based pixel selection and the dynamic belief propagation are proposed to speed up the mapping process: pixels are selected and optimized with the computation resource according to their levels in the quadtree. Solved depth estimations are further interpolated and fused temporally into full resolution depth maps and fused into dense 3D maps using truncated signed distance function (TSDF). We compare our method with other state-of-the-art methods using the public datasets. Onboard UAV autonomous flight is also used to further prove the usability and efficiency of our method on portable devices. For the benefit of the community, the implementation is also released as open source at https://github.com/HKUST-Aerial-Robotics/open_quadtree_mapping.

The present work addresses the generation of a walking gait with automatic footstep placement for a quadrupedal robot, within a Linear Model Predictive Control framework. Existing work has shown how this is only possible within a non-convex programming framework, finding a solution of which is well-known to be very hard. We propose a way to formulate the joint optimization problem as an approximate QP with linear constraints, whose global optimum can be quickly found with off-the-shelf solvers. More specifically, this is done by introducing auxiliary states and control inputs, each of which is subject to linear constraints that are inspired from the literature on bipedal locomotion. Finally, we validate our method on the CENTAURO robot, a hybrid wheeled-legged quadruped with a humanoid upper-body.

A novel gait planning and control framework was developed for quadruped locomotion of a robot. It modeled the quadruped robot as two bipeds carrying the body from the front and rear ends. We first mapped the relationship between the joint torques of support legs and the torso forces of the bipedal sub-robots. Then the equations describing the relationship between the quadruped body forces and the bipedal torso forces under various operating modes of the robot were deduced and solved. Virtual forces were generated on the quadruped body to manipulate its velocity and orientation. Then these virtual forces were distributed to the front and hind sub-robots to generate support leg torques. The state machines and gait generators for the two bipedal sub-robots were designed individually, resulting in the decoupling of the gait parameters in the front legs and hind legs. The effectiveness of the controller was validated through dynamic simulations.

The subject of this paper is about the use of a suspended Cable-Driven Parallel Robot (CDPR) for pick-and-place operations of heavy and heterogeneous objects. The knowledge of the payload mass and its center of mass in realtime is an asset for robust control of the device, which is required to ensure a good stability, especially when the objects have different shapes, sizes and masses. Accordingly, this paper aims at experimentally evaluating the effects of (i) the pulleys modeling and (ii) the use of force sensors for the payload estimation. It turns out that the consideration of the pulleys into the geometric model of the robot improves the mass and center of mass estimations of the payload. A comparison is made between the estimation of cable tensions from force sensors and from motor currents. Finally, a torque controller with a feedforward term for real-time mass compensation is proposed and implemented on a CDPR prototype.

The public perception of android robots is a field of growing applied relevance. Currently, most androids are confined within controlled environments rendering interactions between potential end-users, and robots challenging. Even more challenging is for researchers to investigate end-users' perception of androids. We exploit pre-existing YouTube comments as artifacts for quantitative content analysis to gain an indication of social perception on androids. We perform a content analysis of 10301 YouTube comments from four different videos, and reflect on the textual reactions to video stimuli of four extremely human-like android robots. We use text mining and machine learning techniques to process and analyze our corpus. Our findings reveal three equally important topics that should be considered for paving the way towards a robotic society: human-robot relationships, technical specifications, and the science fiction valley. Considering people's attitudes, fears and wishes towards androids, researchers can increase citizen awareness, and engagement.

Intelligent service robots are used at a significant level to uplift the living standards of domestic users. These robots are expected to possess human-friendly interactive features. Service robots should be able to provide a variety of tasks to support independent living of users in domestic environments. Therefore, a service robot often needs to approach users to execute these services and the approach toward the users should be human friendly. In order to achieve this, proxemics planner of a service robot should be cable of deciding the approaching proxemics based on user behavior. This paper proposes a method to decide the approaching proxemics based on the behavior of the user. A fuzzy interference system has been designed to decide the proxemics based on the user behavior identified through body parameters. This leads to an effective interaction mechanism initiated by a robot in such a way that the approaching scenario looks more humanlike. The proposed concept has been implemented on MIRob platform and experiments were conducted in an artificially created domestic environment. The experimental results of the proposed system have been compared with results of a human study to evaluate the performance of the system.

Grasping unknown objects has been an active research topic for decades. Approaches range from using various sensors (e.g. vision, tactile) to gain information about the object, to building passively compliant hands that react appropriately to contacts. In this paper, we focus on grasping unknown objects using proprioception (the combination of joint position and torque sensing). Our hypothesis is that proprioception alone can be the basis for versatile performance, including multiple types of grasps for objects with multiple shapes and sizes, and transitions between grasps. Using a series-elastic-actuated gripper, we propose a method for performing stable fingertip grasps for unknown objects with unknown contacts, formulated as multi-input-multi-output (MIMO) control. We also show that the proprioceptive gripper can perform enveloping grasps, as well as the transition from fingertip grasps to enveloping grasps.

Recent advances in mixed-reality technologies have renewed interest in alternative modes of communication for human-robot interaction. However, most of the work in this direction has been confined to tasks such as teleoperation, simulation or explication of individual actions of a robot. In this paper, we will discuss how the capability to project intentions affect the task planning capabilities of a robot. Specifically, we will start with a discussion on how projection actions can be used to reveal information regarding the future intentions of the robot at the time of task execution. We will then pose a new planning paradigm - projection-aware planning - whereby a robot can trade off its plan cost with its ability to reveal its intentions using its projection actions. We will demonstrate each of these scenarios with the help of a joint human-robot activity using the HoloLens.

Fast inspection and light maintenance capability is already a clear demand to control the tokamak condition and improve the efficiency of the experimental campaigns. EAST remote maintenance system has been developed to implement inspection and grasping tasks during plasma. The paper presents design description of EAMA (EAST articulated maintenance arm) robot, the gripper and the CASK. The field commissioning was performed both in mockup and EAST tokamak to demonstrate the availability and functionalities of EAMA system. To be able to realize fully routine operation on EAST, improvement of EAMA control system was proposed with integration developed algorithm, such as the robot flexible model modeling, vision servo, motion planning, etc. Finally, thoughts for CFETR In-Vessel Inspection System (CIVIS) are given.

Security of robotics systems, as well as of the related middleware infrastructures, is a critical issue for industrial and domestic IoT, and it needs to be continuously assessed throughout the whole development lifecycle. The next generation open source robotic software stack, ROS2, is now targeting support for Secure DDS, providing the community with valuable tools for secure real world robotic deployments. In this work, we introduce a framework for procedural provisioning access control policies for robotic software, as well as for verifying the compliance of generated transport artifacts and decision point implementations.