IROS 2017 论文列表

The requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using solely extended Behavior Trees (eBT), a model formalized and discussed in this paper. At run-time, the robot can use the more abstract skills to plan a sequence using a PDDL planner, expand the sequence into a hierarchical tree, and re-organize it to optimize the time of execution and the use of resources. The optimization is demonstrated on a kitting operation in both simulation and lab environment, showing up to 20% save in the final execution time.

Accurate online estimation of the environment structure simultaneously with the robot pose is a key capability for autonomous robotic vehicles. Classical simultaneous localization and mapping (SLAM) algorithms make no assumptions about the configuration of the points in the environment, however, real world scenes have significant structure (ground planes, buildings, walls, ceilings, etc.) that can be exploited. In this paper, we introduce meta-structural information associated with geometric primitives into the estimation problem and analyze their effect on the global structural consistency of the resulting map. Although we only consider the effect of adding planar and orthogonality information for the estimation of 3D points in a Manhattan-like world, this framework can be extended to any type of geometric, kinematic, dynamic or even semantic information. We evaluate our approach on a city-like simulated environment. We highlight the advantages of the proposed solution over SLAM formulation considering no prior knowledge about the configuration of 3D points in the environment.

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.

While solving inverse kinematics on serial kinematic chains is well researched, many methods still seem rather limited in jointly handling more complex geometries, including dexterous multi-finger hands or humanoid robots. In particular, object manipulation and motion tasks would benefit from the ability to define intermediate goals along the kinematic chains, such as an elbow position or wrist orientation. In this paper, we propose a fast hybrid evolutionary approach that is capable of solving inverse kinematics for multiple end effectors simultaneously, leaving high flexibility for specifying full-body postures with different objectives. Accurate solutions can be found in real-time and suboptimal extrema are robustly avoided. Our experimental results on the NASA Valkyrie and Shadow Dexterous Hand demonstrate that the algorithm is fast and can be efficiently applied for different robotic tasks which require flexible control of fully-constrained geometries.

Commercially available robots cannot always be adapted to arbitrary tasks or environments, particularly when the task would exceed the kinematic or dynamic limits of the robot. Modular robots offer a solution to this problem, since they can be reconfigured in various ways from a set of modules. The challenge of choosing the optimal composition for a given task, however, is hard since the search space of compositions is vast. Our approach addresses this problem: instead of finding the cost-optimal solution over all possible compositions individually, we propose a time-efficient composition synthesis method which uses evolutionary algorithms by taking task-related objectives into account. Simulations show that our algorithm finds the cost-optimal module composition with less computation time than other methods in the literature.

We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that acquire data samples from off-the-shelf tactile sensors based on capacitive transducers and generate and transmit an event-driven readout. This architecture has been specifically implemented for integration on robots with a large number of tactile sensors, to reduce communication bandwidth, power and processing requirements. An asynchronous serial address-event representation protocol further optimises effective data transmission rate (efficiency of 94.1%) and latency (340 ns) with respect to more common transmission protocols (e.g., Ethernet, CAN). We propose two complementary algorithms for the translation of raw-data into events, optimising data rate and bandwidth, or exploiting the asynchronous nature of the event-driven encoding and the temporal information within the sensory signal. Data reduction capability can reach up to 20 % of the correspondent clock-based encoding, with limited information loss due to the compression.

In this paper we investigate a series of candidate regular planar mesh geometries in terms of their suitability for utilization as mesh primitives for constructing compliant robotic structures. In prior work, we established a framework of compliant, articulate robotic meshes, termed Modular Active Cell Robots (MACROs), created from contractile Shape Memory Alloy linear-actuators (Active Cells). In this paper, we examine how to utilize these MACROs and other mesh-like robots in large regular or semi-regular topologies. We evaluate axial strains and stiffness characteristics for a range of MACRO meshes created using different mesh-primitives, which are drawn from known regular space-filling lattice geometries. We then describe the implications of these results on the design of MACRO structures, including the tradeoffs between different primitives for various structural performance properties.

This paper presents findings from two deployments of an autonomous mobile robot in older adult low income Supportive Apartment Living (SAL) facilities. Design guidelines for the robot hardware and software were based on query of clinicians, caregivers and older adults through focus groups, member checks and surveys, to identify what each group believed to be the most important daily activities for older adults to accomplish physically, mentally and socially. After data analysis, hydration and walking encouragement were found to be critical daily activities, becoming the focus of our deployments. The aim of the deployments was to understand the efficacy of human-robot interaction and identify ways to enhance the robot design and programming. Through observation of older adults interacting with the robot and post-interaction surveys filled out by the older adults, conclusions were drawn for further advancement of the robot development to be tested in future deployments. Results overall indicated high perceived usefulness and growing acceptance of the robot by older adults with increased interactions.

This paper presents a new numeric estimation for dynamic performance of fully-actuated UAV platforms with nonparallel actuation mechanisms. Multi-rotor UAV designs with tilted rotors can achieve fully-actuated performance (net thrust that spans the 6-DoF force-torque space) by tilting the rotors in both cant and dihedral. These orthogonal rotations have different implications on the intrinsic stability of the platform. A numeric estimation of the multi-rotor performances is proposed with an optimization approach to configure the nonparallel thrusters with respect to desired tasks. The new model is applied to Purdues Dexterous Hexrotor UAV and CyPhy Works LVL 1 platform, to illustrate the multi-rotor estimation and optimization. Estimation of these fully-actuated UAVs include precise physical interaction with the environment (Dexterous Hexrotor), hovering efficiency, and influence operating altitude. Experimental results are presented to validate the optimized result with a contact based force control task in preparation for a demo at the DOE Gaseous Diffusion Plant.

Financial and social elements of modern societies are closely connected to the cultivation of corn. Due to the massive production of corn, deficiencies during the cultivation process directly translate to major financial losses. The early detection and treatment of crops deficiencies is thus a task of great significance. Towards an automated health condition assessment, this study introduces a scheme for the computation of plant health indices. Based on the 3D reconstruction of small batches of corn plants, an alternative to existing cumbersome Leaf Area Index (LAI) estimation methodologies is presented. The use of 3D models provides an elevated information content, when compared to planar methods, mainly due to the reduced loss attributed to leaf occlusions. High resolution images of corn stalks are collected and used to obtain 3D models of plants of interest. Based on the extracted 3D point clouds, an accurate calculation of the Leaf Area Index (LAI) of the plants is performed. An experimental validation (using artificially made corn plants used as ground truth of the LAI estimation), emulating real world scenarios, supports the efficacy of the proposed methodology. The conclusions of this work, suggest a fully automated scheme for information gathering in modern farms capable of replacing current labor intensive procedures, thus greatly impacting the timely detection of crop deficiencies.

Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independent of force measurements that may not be available to a robotic framework lacking force/torque sensors. In this work, we design a method to estimate deformability based on certain properties, such as volume conservation. Using the finite element method (FEM) we create the ground truth deformability for various settings to evaluate our method. The experimental evaluation shows that our approach is able to accurately identify the deformability of test objects, supporting the value of MSM for robotic applications.

Sensing contact forces can be a key enabler for higher order dexterous manipulation in robotic hands. To sense the full range of contact pressure distribution would provide the best solution, but it is in practice unfeasible when considering very deformable and adaptable hands. This paper proposes an approach to estimate the contact forces acting on an under-actuated adaptable hand by combining the compliance model of the hand with the geometric configuration of the hand itself. This is done by introducing reasonable assumptions about the net contact force on each phalanx. The proposed method is introduced and experimentally validated on two fingers of the Pisa/IIT SoftHand.

In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.

In this study we propose a new method to enhance the performance of iterative learning control (ILC). We focus on robotic tasks dealing with adaptation to the unknown or partially known environment, where the robot has to learn the environment geometry in order to perform the desired task with the given reference forces and torques. The initial motion trajectories are obtained by kinesthetic teaching, whereas the required forces and torques are prescribed by the task. We are interested in incremental learning, which assures smooth and safe operation, aiming at handling of delicate, fragile objects, such as objects made of glass. In order to achieve these goals we propose a new adaptive ILC scheme, where the adaptation is supervised by reinforcement learning. We also show how to apply ILC to orientational motion, taking into account the curved geometry of SO(3). The performance of the proposed algorithm is verified on a bi-manual glass wiping task.

This paper presents a method for trajectory generation with Bézier curves, by considering the wind field and dynamic constraints for small Unmanned Aerial Systems (UAS). Atmospheric phenomena affect UAS trajectories, so the wind presence should be considered in order efficiently perform a mission. These missions require precise area decomposition, efficient waypoint sequencing, and a smooth trajectory generation to fulfill the required level of safety and reliability. In this context, a path planning algorithm is presented which incorporates the use of an area decomposition scheme which considers complex shapes and restrictions, in order to provide high levels of coverage. A novel trajectory generation algorithm is proposed, which aims to harvest energy from the atmosphere by taking advantage of the previously estimated wind field and the identified wind features, such as shear wind and discrete gusts, by taking into account flight envelope restrictions. Different test cases and scenarios, including Software-In-The-Loop (SITL) simulations and real telemetry data analysis, are presented to assess the energy gain for the implemented algorithms. The results indicate promising energy gains of almost 15% in voltage saving if the mentioned factors are considered to re-plan the path with the energy-efficient trajectories.

Given the wide variety of flight conditions typically encountered by fixed-wing aerial vehicles, the flight performance of a solar-powered unmanned aerial vehicle (SUAV) depends on many factors. Predicting the performance for a given application requires characterization of both system and environmental components. Furthermore, a SUAV with a transformable airframe increases the number of characterizable states, where each state features a unique set of capabilities. This results in significant differences with respect to power consumption, solar panel orientation, and increases the design limitations on system components. This paper characterizes the energy collection, propulsion, and power electronics subsystems of a transformable SUAV developed at the University of Minnesota. Clear-sky and solar panel models are used to predict the power available for a given location and time. Propulsion system design is validated with flight data, and a proposed model correlates propulsion limits with available energy. Power electronics are modeled and simulated to determine hardware and tracking algorithm efficiencies. Finally, a pulsed battery charging methodology is implemented in hardware and evaluated against conventional charging techniques.

In this paper, a tracking control approach for robot assisted landing of UAV rotorcraft, such as multicopters and helicopters, is presented. The aim is to safely land these type of flying vehicles under side wind conditions and on moving surfaces using a robot manipulator arm mounted on the ground. In our previous work, we have considered light UAVs compared to the robot's payload capabilities. In this work, we present a method that enables to realize robot assisted landing for arbitrary combinations of aerial vehicles and robot arms. We show that the combined system is overactuated. This allows to distribute the workload and enables to assist heavy rotorcraft, whose weight would otherwise exceed the nominal payload of the robot arm. The performance of the controller is evaluated in numerical simulations as well as in experiments using a custom-built hexacopter and a KUKA/DLR light-weight robot.

The large dynamic deflections of continuum robots, soft robots, and slender elastic objects can be accurately modeled with classical rod theories in nonlinear elasticity. In this paper, we propose a real-time computational approach for solving the partial differential equations of a dynamic Kirchhoff rod. Our approach is based on implicit time discretization of the Kirchhoff equations and subsequent solution of the resulting continuous spatial boundary value problem at each time step. This modular approach can exhibit low numerical damping, handle arbitrarily large time steps, and provide an accurate, high-order representation of the rod shape in steady-state. We experimentally validated the method by capturing footage of a dynamic rod with a high speed camera and comparing this experimental data with simulations using the proposed approach. Soft-real-time performance is achieved, and the relationship between time step and real-time performance is explored in a plot.