IROS 2015 Papers

The exploitation of new fields of application in addition to traditional industrial production for robot manipulators (e.g. agriculture, human areas) requires extensions to the sensor as well as to the planning capabilities. Motion planning solely based on visual information performs poorly in cluttered environments since contacts with obstacles might be inevitable and thus a distinction between hard and soft objects has to be made. In our contribution we present a novel intrinsic tactile sensing module mounted on a multipurpose 9 DOF agricultural manipulator. With its innovative sensor arrangement we consider it to be a low-cost, easily manageable and efficient solution with a reasonable abstraction layer in comparison to complex torque sensing or tactile skins. The sensor provides information about the resulting force and torque. In the second part of our paper, the tactile information is used for minimizing contact forces while pursuing the end-effector tasks as long as reasonable. Hence, we present robust and efficient extensions to Resolved Motion Rate Control for real-time application. We introduce a general formulation providing control inputs in task-space, joint-space and nullspace. Thus, we design a suitable controller by feedback linearization and feed-forward terms. Results from real-world experiments show the potential of our approach. A discussion of the different control schemes completes the paper.

Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios.

This paper deals with motion planning problems for spherical rolling robots driven by a two degree of freedom pendulum. A full dynamic model for this system is first introduced. Then, assuming that the contact path is specified and the sphere moves in pure rolling mode, the full dynamic model is reduced by imposing virtual constraints. A timing control law, based on the Beta function, for tracing the contact curves in rest-to-rest motion is constructed. The constructed control law is verified under simulation for tracing Viviani's curve and the Loxodrome on the sphere.

The paper describes a motion planning and control software architecture developed for the automation of a surgical robot. The considered surgical robot is a dual-arm prototype developed with a redundant and modular mechanical structure, designed to be reconfigured for different surgical tasks, and with a hybrid parallel/serial kinematics. The motion planning solution proposed in the paper includes both an online collision-free path planner, based on the RRT-Connect algorithm, and a generator of predefined motion primitives. This solution allows the multi-arm robot to autonomously execute the complex motion patterns required for a suturing task. Since such motion patterns are specified in the Cartesian space, an efficient and univocal solution of the inverse kinematics of the robot, which is a challenging problem due to its hybrid structure, is another crucial issue addressed in the paper.

In clinics, customized archwires are demanded for lingual orthodontic treatment. However, only very experienced orthodontists can handle the manual appliance preparation. This pattern not only occupies lots of the orthodontist's labor time, but also can not ensure the accuracy of the appliances. Therefore, a robotic system was developed for automatic and accurate orthodontic archwire bending in our study. First, a method for customized archwire parameterization was developed. Second, an adaptive sampling-based bending planner with collision checker in time-varying environment was designed. Finally, a bending control strategy was used to eliminate the springback effect of the archwires and bending point shift during the bending process. A self-developed simulation platform based on Robot Operating System with MoveIt was used for preliminary validation of the proposed method. Physical experiments for multi-functional orthodontic bends on the robotic system were conducted as well. The results have shown that the developed robotic system using the proposed planning and control method was able to accomplish the automatic and accurate orthodontic archwire bending.

Helical microswimmers capable of propulsion at low Reynolds numbers have been proposed for numerous applications. Several different kinds of helical swimmers inspired by E. coli bacteria have been proposed by researchers, and most of them have rigid helical tails. However, high softness could make swimmers more adaptive in confined environments for biomedical applications. This paper aims to study the morphologies and the swimming characteristics of magnetically actuated swimmers with belt-like soft tails initially straight at low Reynolds numbers. We found that a swimmer with a soft tail during rotations shows different morphologies: a twisted shape until the input frequency increases to a threshold frequency, and a helical shape until a step-out frequency. Beyond the stepout frequency, the shape of the soft tail becomes irregular. The soft tail swimmers with different lengths show similar swimming velocities at same rotational frequencies. However, their maximal swimming velocities are different because of the varied step-out frequencies. The interactions between the soft tails reduce the swimming velocity, and this influence increases with the rotational frequency. Thus, the swimming performance is not improved by doubling the number of soft tails in our experiments.

Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.

In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought, we design sensors and actuators into the robot at the onset, both reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic actuators embedded into a silicone robotic gripper. The robot's body is formed by curing the silicone in complex 3D printed molds. We show that the liquid metal strain gauges provide a repeatable resistance response during robotic actuation. We further show that, given sufficient control over other time-dependent variables, it is possible to determine when the robot begins gripping an object during actuation.

This paper investigates a simple swimming mechanism, which comprises two concentric bodies and two passive flaps. The mechanism propels itself forward by oscillating its inner body in a symmetric fashion using a single actuator. The paper starts by using a simple model to investigate the dynamics of the robot and to simulate its motion. Next, we simulate various design parameters and conclude that some parameters can improve the performance of the swimming robot. We conclude with a proof-of-concept prototype that is capable of swimming similar to the predicted model. Moreover, by using an asymmetric input, the mechanism is able to rotate in various curvatures. The combination of these forward and rotational gaits can be used to stabilize the robot onto a desired trajectory.

The execution of long-horizon tasks under uncertainty is a fundamental challenge in robotics. Recent approaches have made headway on these tasks with an integration of task and motion planning. In this paper, we present Interfaced Belief Space Planning (IBSP): a modular approach to task and motion planning in belief space. We use a task-independent interface layer to combine an off-the-shelf classical planner with motion planning and inference. We determinize the problem under the maximum likelihood observation assumption to obtain a deterministic representation where successful plans generate goal-directed observations. We leverage properties of maximum likelihood observation determinizations to obtain a simple representation of (optimistic) belief space dynamics that is well-suited to planning. Our interface is implemented with standard belief state queries, requiring only the ability to sample, compute unnormalized likelihoods, and compute maximum likelihood states. Our contribution is a novel algorithm for task and motion planning in belief space that has minimal dependence on the details of the inference engine used. IBSP can work with a broad class of black box state estimators, with zero changes to the algorithm. We validate our approach in simulated tasks for the PR2 that account for continuous state, different types of initial state distributions, and negative observations.

In robot-assisted surgery, manipulation tasks can be achieved through collaboration among robotic and human agents. Collaboration models can potentially include multiple agents working towards a shared objective - a scenario referred to as multilateral manipulation. In this work, we examine multilateral manipulation in the task of debridement: removing dead tissue or damaged tissue fragments to allow remaining healthy tissue to heal. We extended our previously developed multilateral manipulation software framework to the task of debridement and implemented four different collaboration models: (1) fully autonomous debridement, (2) shared control between a human and robotic agent, (3) supervised control where the operator identifies foreign bodies to be excised, and (4) teleoperation. We demonstrate these collaboration models with the RAVEN-II robot, an open-architecture surgical robot with two cable-driven 7-DOF arms. Each collaboration model included methods to remove foreign bodies from the field of view of a stereoscopic camera setup. Results indicate tradeoffs in experiment duration, hardware requirements, and safety implications among the four collaboration models.

Safety is important to a robot that is used in a way that can impact on the wellbeing of people. However, understanding and managing the safety information of a robot is not a trivial task. The quantity of this information is large, and it is usually stored in text-based formats. These factors mean that maintaining safety information when it changes is error prone, and understanding it requires manually searching through lengthy reports. In this paper, we demonstrate that a robot's safety information can be stored in a model-based format. We use the SafeML modelling language to model the results of a safety analysis performed on a semi-autonomous wheelchair. We then illustrate and discuss the benefits of storing and managing safety information using a model, both to maintainability of the information and to understanding the safety of the robot.

Soft pneumatic artificial muscles (SPAMs) are a type of pneumatic actuator that provide customizable motion trajectories in three dimensional space without the need for rigid links or a transmission mechanism. This paper presents a novel design for producing SPAMs, named wrapped SPAMs (WSPAMs). Unlike previous SPAM designs, the production process of WSPAM is highly repeatable, while the motion trajectory can be easily modified. A model for predicting the steady-state angular displacement of a WSPAM actuator based on its geometrical parameters and the elasticity of the materials used in its production is presented and experimentally validated.

Modular Robot Manipulators are user-configurable manipulators which provide rapid design and inexpensive implementation. To be easy-use, smart actuators embedded with position input and position feedback controller are adopted, these local controllers render the manipulators position controlled, but also result in limited performance and precision. This paper targets the case that the built-in controller does not provide desirable precision for set-point regulation. Firstly a joint-level model is established, of which the nominal model can be identified with derivative observer based on the position feedback, then an auxiliary adaptive controller coping with parametric uncertainty is proposed which leads to an error close to zero, a switching control strategy is introduced considering the actuator saturation. The proposed control method is implemented on a 5-DOF modular manipulator, with comparison to classic integral controller.

This paper presents the development of an endoscope manipulator with passive and active structures for functional endoscopic sinus surgery (FESS). The 5-DoF passive structure has three translations and two rotations (T3R2) that allows the surgeon to manually place the endoscope near to the entry point during. The 4-DoF motorized structure (T2R2) actively controls the endoscope's position based on the surgeon's input commands. We analyze the reciprocal screw of the passive and active structures. The motion control system is based on a real-time Linux kernel that processes the commands from the surgeon and controls the manipulator's active joints. A user control interface based on an IMU fastened on the surgeon's foot is developed; this interface measures the foot's posture and through a series of gestures, it provides the desired pan/tilt/zoom motions of the camera. The developed endoscope manipulator allows the surgeon to conduct ‘two-hand’ operations while retaining direct control of the camera. We present an experimental study to validate the performance of the robotic prototype.

Robotics applications involve robots that perform tasks by interacting with specific worlds. Most applications are intertwined with and tied to fixed robots and worlds. Changes and evolution of a robot or world have an invasive and often unpredictable impact on the application software.

To gain high mobility on challenging terrains, a mobile robot based on eccentric paddle mechanism (ePaddle) with locomotion versatility has been proposed. In this paper, a paddle-aided stair-climbing motion is presented for this ePaddle-based robot. The robot can roll on the stair as a traditional wheeled vehicle and also can climb up the stair under the help of its paddles. Robot-stair interaction modes are presented and typical feasible postures of the robot in stair-climbing are discussed. Frictional requirements for the robot to hold a desired posture are evaluated by modelling statics of the robot. Analyzed results reveal that two critical scenarios in wheeled mode occur when the front-wheel is at the bottom of the riser, and when the rear-wheel is at the top of the riser, respectively. In contrast, frictional requirements of the paddle-aided stair-climbing postures confirm that the robot can climb up the stair with all feasible postures by touching the stair with the paddle, which verifies the effectiveness of the proposed paddle-aided stair-climbing.

In this work we present an obstacle detection and tracking solution applied to Automated Guided Vehicles (AGVs) in industrial environments. The proposed method relies on information provided by an omnidirectional stereo vision system enabling 360 degree perception around the AGV. The stereo data is transformed into a classified digital elevation map (DEM). Based on this intermediate representation we are able to generate a set of obstacle hypotheses, each represented by a 3D cuboid and a free-form polygonal model. The cuboidal model is used for the classification of each hypothesis as “Pedestrian”, “AGV”, “Large Obstacle” or “Small Obstacle”, while the free-form polylines are used for object motion estimation relying on an Iterative Closest Point (ICP) method. The obtained measurements are subjected to a Kalman filter based tracking approach, in which the data association takes into account also the classification results.

This paper investigates both positional dependence in systems with friction and the influence an increase in temperature has on the friction behavior. The positional dependence is modeled with a Radial Basis Function network and the temperature dependence is modeled as a first order system with the power loss due to friction as input, eliminating the need for temperature sensing. The proposed methods are evaluated in both simulations and experiments on two industrial robots with strong positional and temperature friction dependence.