IROS 2016 Papers

We present a reactive multi-agent push system for a large set of objects. The behavior for the pushing agents consists of: 1) selecting and updating an object set to push, 2) reaching positions near the objects to start influencing, 3) pushing the objects along a path to the goal region, and 4) regrouping when needed to ensure the group is packed tightly enough. The emergent properties of the behavior allow us to test how effectively a group of agents can push a set of objects through the environment with different strategies.

Motor channelling is a concept to provide navigation and sensory feedback to operators in master-slave surgical setups. It is beneficial since the introduction of robotic surgery creates a physical separation between the surgeon and patient anatomy. Active Constraints/Virtual Fixtures are proposed which integrate Guidance and Forbidden Region Constraints into a unified control framework. The developed approach provides guidance and safe manipulation to improve precision and reduce the risk of inadvertent tissue damage. Online three-degree-of-freedom motion prediction and compensation of the target anatomy is performed to complement the master constraints. The presented Active Constraints concept is applied to two clinical scenarios; surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery. The proposed motor channelling control strategy is implemented on the da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its effectiveness is demonstrated through a detailed user study.

Many robotic applications require repeated, on-demand motion planning in mapped environments. In addition, the presence of other dynamic agents, such as people, often induces frequent, dynamic changes in the environment. Having a potential function that encodes pairwise cost-to-go can be useful for improving the computational speed of finding feasible paths, and for guiding local searches around dynamic obstacles. However, since storing pairwise potential can be impractical given the O(|V|2) memory requirement, existing work often needs to compute a potential function for each query to a new goal, which would require a substantial online computation. This work addresses the problem by using diffusion maps, a machine learning algorithm, to learn the map's geometry and develop a memory-efficient parametrization (O(|V|)) of pairwise potentials. Specially, each state in the map is transformed to a diffusion coordinate, in which pairwise Euclidean distance is shown to be a meaningful similarity metric. We develop diffusion-based motion planning algorithms and, through extensive numerical evaluation, show that the proposed algorithms find feasible paths of similar quality with orders of magnitude improvement in computational speed compared with single-query methods. The proposed algorithms are implemented on hardware to enable real-time autonomous navigation in an indoor environment with frequent interactions with pedestrians.

Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles based on the currently needed level of detail. The hierarchy enables sampling to be performed using the simplest and most conservative representation of the environment possible in that region. Our results show that this scheme improves planner performance irrespective of the underlying sampling method and input problem. In many cases, improvement is significant, with running times often less than 60% of the original planning time.

This paper examines a mission planning problem for a solar-powered unmanned ground vehicle (UGV) which requires the vehicle to visit a series of objective points in minimal time subject to a strict net-energy change constraint. Though related to the Traveling Salesperson Problem, the mission planning problem discussed herein imposes further complexity through additional coupled mixed-variable sets and the strict energy constraint. A scalar field representing the solar radiation of the mission environment is first characterized from a visual-spectrum image. A cascaded particle swarm optimization algorithm, coupled with the integer linear programming technique, is used to generate a time-optimized motion plan and power schedules for the UGV, which guides it to visit the assigned objective points with optimized sequence and paths, and then return to its starting location and orientation while guaranteeing compliance with the net energy gain constraint.

We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of the trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically inspired approximations and system identification. Our optimization approach can incorporate various other constraints such as collision avoidance with obstacles, kinematic and dynamics constraints of the manipulator, and fluid dynamics characteristics. We demonstrate the performance of our planner on different benchmarks corresponding to various obstacles and container shapes. We also evaluate its accuracy by validating the motion plan using an accurate but computationally costly Navier-Stokes fluid simulation.

In this paper, we present a motion planning algorithm for autonomous navigation in highly constrained urban environments. Since common approaches to on-road trajectory planning turned out to be unsuitable for this task, we instead extended an A*-based planner originally designed for navigation in unstructured environments. Two novel node expansion methods were added to obtain smooth and accurate trajectories that consider the structure of the environment. The first one attempts to find a trajectory connecting the current node directly to the goal by solving a boundary value problem using numerical optimization. The second method leverages a simulated pure-pursuit controller to generate edges (i.e. short motion primitives) that guide the vehicle toward or along the global reference path. As a result, the planner is able to produce smooth paths while retaining the explorative power of A* that is needed to deal with challenging situations in urban driving (e.g., reversing in order to pass a vehicle that stopped unexpectedly). Its practical usefulness was demonstrated during extensive tests on an electric vehicle navigating a mock urban environment as well as on our own autonomous vehicle MuCAR-3.

Reversing with a dolly steered trailer configuration is a hard task for any driver without extensive training. In this work we present a motion planning and control framework that can be used to automatically plan and execute complicated manoeuvres. The unstable dynamics of the reversing general 2-trailer configuration with off-axle hitching is first stabilised by an LQ-controller and then a pure pursuit path tracker is used on a higher level giving a cascaded controller that can track piecewise linear reference paths. This controller together with a kinematic model of the trailer configuration is then used for forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate motion plans that are not only kinematically feasible but also include the limitations of the controller's tracking performance when reversing. The approach is evaluated over a series of Monte Carlo simulations on three different scenarios and impressive success rates are achieved. Finally the approach is successfully tested on a small scale test platform where the motion plan is calculated and then sent to the platform for execution.

In this paper we present a wearable Haptic Feedback Device to convey intuitive motion direction to the user through haptic feedback based on vibrotactile illusions. Vibrotactile illusions occur on the skin when two or more vibrotactile actuators in proximity are actuated in coordinated sequence, causing the user to feel combined sensations, instead of separate ones. By combining these illusions we can produce various sensation patterns that are discernible by the user, thus allowing to convey different information with each pattern. A method to provide information about direction through vibrotactile illusions is introduced on this paper. This method uses a grid of vibrotactile actuators around the arm actuated in coordination. The sensation felt on the skin is consistent with the desired direction of motion, so the desired motion can be intuitively understood. We show that the users can recognize the conveyed direction, and implemented a proof of concept of the proposed method to guide users' elbow flexion/extension motion.

We are dealing with the problem of generating natural human-like motions during speech in android robots, which have human-like appearances. So far, automatic generation methods have been proposed for lip and head motions of tele-presence robots, based on the speech signal of the tele-operator. In the present study, we aim for extending the speech-driven motion generation methods for laughing speech, since laughter often occurs in natural dialogue interactions and may cause miscommunication if there is mismatch between audio and visual modalities. Based on analysis results of human behaviors during laughing speech, we proposed a motion generation method given the speech signal and the laughing speech intervals. Subjective experiments were conducted using our android robot by generating five different motion types, considering several modalities. Evaluation results show the effectiveness of controlling different parts of the face, head and upper body (eyelid narrowing, lip corner/cheek raising, eye blinking, head motion and upper body motion control).

In large industrial plants, the inspection of production lines is a heavy and costly task that puts human inspectors at high risk. In order to overcome these challenges, we have developed an autonomous plant inspection system using a mobile tracked vehicle. In this paper, we propose an autonomous navigation method for tracked vehicles based on a contact force model that enables the robot to compensate for collisions with obstacles. The model considers the influence of the contact force on the linear and angular motion of the robot. Using the model, the controllable velocity range is derived during collisions. The experimental results show that the robot is safely controlled by complying with velocity constraints. In addition, our method can generate motions such as leaving wall, L-shaped curve and crosswise locomotion in straight passage while navigation alongside the walls. The method allows the robot to smoothly follow a target path, despite colliding with obstacles.

This paper demonstrates a novel motion control approach for biomimetic underwater vehicles with pitching fins. Even though these vehicles are highly maneuverable, the actuation of their different degrees of freedom (DOFs) is strongly coupled. To address this problem, we propose to use smooth DOF prioritization depending on which maneuver the vehicle is about to do. DOF prioritization has allowed us to develop a modular, easily applicable and extendable motion control architecture for U-CAT vehicle, which is meant for archaeological shipwreck penetration. We demonstrate the benefits of this architecture by developing an remotely operated vehicle autopilot for depth and yaw using a nonlinear state feedback controller. We also show the extensibility of the approach by controlling 3 DOFs of a fully autonomous U-CAT. The real-time experimental results show high position tracking precision (depth RMS error: 1.9 cm; yaw RMS error: 2.5°) Comparative experiments justify the use of DOF prioritization.

From the viewpoint of evolution, vertebrates first accomplished locomotion via motion of the spine. Legs evolved later, to enhance mobility, but the spine remains central. Contrary to this, most robots have rigid torsos and rely primarily on movement of the legs for mobility. The force distributing properties of tensegrity structures presents a potential means of developing compliant spines for legged robots, with the goal of driving motion from the robots core. We present an initial exploration of the morphological design of a tensegrity quadruped robot, the first to the authors' knowledge, which we call MountainGoat, and its impact on controllable locomotion. All parts of the robot, including legs and spine, are compliant. Locomotion is aided by the use of central pattern generators, feedback control via a neural network, and machine learning techniques involving the Monte Carlo method as well as genetic evolution for parameter optimization. Control is demonstrated with three variations of MountainGoat, focusing on actuation of the spine as central to the locomotion process.

Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particular when physical interaction is involved. It seems reasonable then to wish for robots to understand how pushed objects move.

Localizing a camera in a given map is essential for vision-based navigation. In contrast to common methods for visual localization that use maps acquired with cameras, we propose a novel approach, which tracks the pose of monocular camera with respect to a given 3D LiDAR map. We employ a visual odometry system based on local bundle adjustment to reconstruct a sparse set of 3D points from image features. These points are continuously matched against the map to track the camera pose in an online fashion. Our approach to visual localization has several advantages. Since it only relies on matching geometry, it is robust to changes in the photometric appearance of the environment. Utilizing panoramic LiDAR maps additionally provides viewpoint invariance. Yet low-cost and lightweight camera sensors are used for tracking. We present real-world experiments demonstrating that our method accurately estimates the 6-DoF camera pose over long trajectories and under varying conditions.

This article studies algorithms used by a learner to explore high-dimensional structured sensorimotor spaces such as in tool use discovery. In particular, we consider goal babbling architectures that were designed to explore and learn solutions to fields of sensorimotor problems, i.e. to acquire inverse models mapping a space of parameterized sensorimotor problems/effects to a corresponding space of parameterized motor primitives. However, so far these architectures have not been used in high-dimensional spaces of effects. Here, we show the limits of existing goal babbling architectures for efficient exploration in such spaces, and introduce a novel exploration architecture called Model Babbling (MB). MB exploits efficiently a modular representation of the space of parameterized problems/effects. We also study an active version of Model Babbling (the MACOB architecture). These architectures are compared in a simulated experimental setup with an arm that can discover and learn how to move objects using two tools with different properties, embedding structured high-dimensional continuous motor and sensory spaces.

This paper studies modelling and dynamic analysis of underactuated capsule systems exhibiting friction-induced hysteresis. The motion mechanism is novel in utilizing internal centripetal torques generated by a vibration micro-motor mounted on the platform. Up to now, most investigations in frictional interactions towards capsule systems were confined into static or quasi-dynamic circumstance, where it is difficult to facilitate online use and control. It is the first time the dynamic frictional characteristics (non-reversible drooping and hysteresis) are studied towards these systems. An analytical study is primarily conducted to reveal the non-reversible characteristic for the static friction, the pre-sliding regime as well as the pure sliding regime, and the frictional limit boundaries are identified. Subsequently, the studies are mainly focused on dynamic analysis, including friction-driven vibrational responses and qualitative changes induced by control parameter (mass ratio) in capsule dynamics. It is found that the models predict periodic responses for the parameters considered and the average capsule velocity can be controlled through proper tuning of the control parameter around identified control points. The results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements.

In this paper we present the design and characterization of a novel Passive Variable Stiffness Joint (pVSJ). pVSJ is the proof of concept of a passive revolute joint with controllable variable stiffness. The current design is intended to be a bench-test for future development towards applications in haptic teleoperation purposed exoskeletons. The main feature of the pVSJ is its capability of varying the stiffness with infinite range based on a simple mechanical system. Moreover, the joint can rotate freely at the zero stiffness case without any limitation. The stiffness varying mechanism consists of two torsional springs, mounted with an offset from the pVSJ rotation center and coupled with the joint shaft by an idle roller. The position of the roller between the pVSJ rotation center and the spring's center is controlled by a linear sliding actuator fitted on the chassis of the joint. The variation of the output stiffness is obtained by changing the distance from the roller-springs contact point to the joint rotation center (effective arm). If this effective arm is null, the stiffness of the joint will be zero. The stiffness increases to reach high stiffness values when the effective arm approaches its maximum value, bringing the roller close to the torsional springs' center. The experimental results matched with the physical-based modeling of the pVSJ in terms of stiffness variation curve, stiffness dependency upon the springs' elasticity, joint deflection and the spring's deflection.

The paper presents an analysis of human-like reaching movements in manipulation of parallel flexible objects. To predict the trajectory of human hand, a minimum hand jerk model, based on the minimization of integral of squared hand jerk over the movement duration is established. It is shown that within this model the optimal hand trajectory is composed of a fifth order polynomial and two trigonometric terms depending on the natural frequencies of the system and the movement time. A virtual reality-based experimental setup with a haptic simulator is designed, and the prediction by the minimum hand jerk model is verified against experimental data. The theoretical prediction matches the collected data with a reasonable accuracy. The initial experimental results confirm the applicability of the minimum hand jerk model for modeling of human-like reaching movements in dynamic environments.