IROS 2015 Papers

As the application of probabilistic models in robotic applications increases, the necessity of a systematic robot-control method that considers the effects of multiple uncertainty sources becomes more evident. Motivated by human sensorimotor findings, in this work we study the stochastic locally optimal feedback control problem with high-order cost statistics where dynamics have multiple additive noise sources and cost variability produced by each uncertainty source is evaluated marginally. We present risk-sensitive and cost-cumulant solutions for this problem for non-linear dynamics and non-quadratic costs. Locally optimal solutions are found by iteratively performing a linear quadratic approximation around a nominal trajectory, solving the local problem and updating the trajectory until convergence. Simulation results of a point mass robot and a two-link manipulator validate the applicability of the proposed approach and illustrate its peculiarities.

In the way of biomimetic research, we have developed a painless needle mimicking mosquito's proboscis. A real three-dimensional (3D) sharp and jagged needle shape elaborately like mosquito, however, was difficult to achieve as far as using conventional fabrication methods. For this purpose, a 3D laser lithography with ultrafine resolution of 200 nm (0.2 μm) is employed in this article. Unlike other reported sole needles, presented microneedle is divided into two half parts, each is shaped like eaves-trough and dependently moved. The combined halves act as one hollow microneedle, which can draw up human blood by capillary action. The resistive force during insertion can be reduced when the two halves are alternately advanced, which mimics the motion of mosquito's proboscis.

We present the first demonstration of establishing mutual attention between an outdoor UAV in autonomous normal flight and an uninstrumented human user. We use the familiar periodic waving gesture as a signal to attract the UAV's attention. The UAV can discriminate this gesture from human walking and running that appears similarly periodic. Once a signaling person is observed and tracked, the UAV acknowledges that the user has its attention by hovering and performing a “wobble” behavior. Both parties are now ready for further interaction. The system works on-board the UAV using a single camera for input and is demonstrated working reliably in real-robot trials.

The most accurate and dependable approach to the in-vivo identification of human limb stiffness is by position perturbation. Moving the limb over a small distance and measuring the effective force gives, when states are steady, direct information about said stiffness. However, existing manipulandi are comparatively slow and/or not very stiff, such that a lumped stiffness is measured. This lumped stiffness includes the limb response during or after reflexes influenced by both, the passive musculotendon and active neuronal component. As this approach usually leads to inconsistencies between the data and the stiffness model, we argue in favour of fast, pre-reflex impedance measurements-i.e., completing the perturbation movement and collecting the data before effects of spinal reflexes or even from the motor cortex can influence the measurements. To obtain such fast planar movements, we constructed a dedicated orthoglide robot while focusing on a lightweight and stiff design. Our subject study of a force task with this device lead to very clean data with always positive definite Cartesian stiffness matrices. By representing them as ellipses, we found them to be substantially bigger in comparison to standard literature which we address to a larger number of recruited motor units. While ellipses orientation and the length of their main axis increased, the shape decreased with the exerted force. The device will be used to derive design criteria for variable-stiffness robots, and to investigate the relation between muscular activity and areflexive joint stiffness for teleoperational approaches.

This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and propose a computationally efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main contributions of this paper are: • A computationally efficient decoupled space-time trajectory planning structure. • The formulation of optimization-free elastic-band-based path planning and speed-constraint-based temporal planning routines with pre-determined runtime. • Identification of continuity problems with previous cost-based planners that cause tunability and stability issues.

The objective of this work is to achieve disturbance rejection and constant orientation of the trunk of a multi-legged robot. This is significant when payloads (such as cameras, optical systems, armaments) are carried by the robot. In particular, this paper presents an application of an on-line learning method to actively correct the open-loop gait generated by a central pattern generator (CPG) or a limit-cycle method. The learning method employed is based on a Nonlinear Autoregressive Neural Network with Exogenous inputs (NARX-NN)- a recurrent neural network architecture typically utilized for modeling nonlinear difference systems. A supervised learning approach is used to train the NARX-NN. The input to the neural network includes states of the robot legs, trunk attitude and attitude rates, and foot contact forces. The neural network is used to estimate the total torque imparted on the robot. The learned effects of the internal forces and disturbances are then applied in an inverse dynamics/computed torque controller, which is utilized to achieve a stable trunk (i.e., a constant orientation of the trunk). The efficacy of the proposed approach is shown in detailed simulation studies of a quadruped robot.

In this paper, we propose a new kind of tripedal locomotion—namely, robotic walking with three legs. We develop a prototype robot, which we call Martian III, having three stretchable legs fixed to each other at the center of the body. Therefore the coxa (the hip joint) is completely fixed in this robot, and the only control we have are the linear actuators to stretch the three legs in the radial directions. The robot is developed as a minimal model to examine our basic idea, to make the robot swing by actuating the legs and then generate locomotion utilizing geometric effect of rigid body rolling on a flat floor. The experiments with robot succeeds in forwarding and rotary locomotion with appropriate choices of oscillatory along each leg. Further analysis of the movement of Martian III indicates that the direction of the swing leg depends on the deformation of each leg. Moreover, due to the result of the measurement experiment and movement analysis of rotation locomotion, we found out two typical type of gaits.

This paper introduces a unique hexapedal locomotion platform tagged TriBot as potential agents for swarm robotic systems. We investigate the differences in performance and kinematic characteristics of two identical prototypes of this mobile robot manufactured using different design and fabrication methods and materials. Among alternatives for mass production, we focus on cut-and-assemble (CA) acrylic bodies and cut-and-fold (CF) origami-inspired polyester structures as two promising methods for manufacturing these agents. Through a comprehensive comparison between the two prototypes, advantages and disadvantages of each robot development approach are presented. This information will enable the selection of the most appropriate robotic platform according to environmental and operational task specifications.

This paper aims to develop a transparent object recognition algorithm which can integrate with visual cues of transparent objects to enhance the retrieval result. Due to the special characteristics of transparent objects, very few vision methods exist to identify them. To achieve our goal, we first use active depth sensor combining with image pre-processing techniques to retrieve transparent candidates. We then propose a transparent candidate classification algorithm using visual cues in color image to further distinguish the transparent ones out of candidates. Experimental results show that our algorithm can achieve good transparent object retrieval results and outperform methods based on depth image. In addition, this algorithm can be integrated with pose estimation methods on robotic bio-laboratory automation applications.

Robotic grasping has been hindered by the inability of robots to perceive unstructured environments. Because these environments can be complex or dynamic, it is important to obtain additional and precise sensing information just before grasping. This paper expands upon the pretouch modality by introducing a transmissive optical sensor. It can unambiguously indicate the presence or lack of objects in close proximity. A wide variety of items that other sensors fail to sense, such as extremely soft or shiny objects, can be detected by the proposed sensor. The sensor is also fully integrated into the fingertips of the PR2 robotic platform and manufactured with inexpensive, commercially-available components. Several experiments are conducted to verify its utility in both environment perception and robotic grasping. It is shown that the perception information supplied by the sensor facilitates effective robotic grasping.

We develop and evaluate methods based on a novel data compression strategy for visual SLAM that uses traveled trajectory analysis. Beyond compressing scene structure based purely on geometry, we aim at developing compact map representations that are useful for re-exploration while preserving scene structure. Our work is evaluated on data collected from a visual sensor and exploits the information intrinsic to the trajectory of exploration together with the visual information of map points. We perform rigorous statistical evaluation and Pareto analysis to show how this approach compares with three widely used baseline compression methods: k-means on point geometry, keyframes and random sampling. Results indicate that compressing maps to levels of 25% or even less of the original data is possible, while preserving good 6D visual relocalisation performance.

This paper aims to smooth jerky trajectories for high-DOF manipulators with Soft Motion [1] shortcuts which are bounded in velocity, acceleration and jerk. The algorithm presented here iteratively picks two points on the trajectory and attempts to replace the intermediate trajectory with a shorter and collision-free segment. The objective of this algorithm is to shorten the execution time of an input trajectory as much as possible while retaining the feasibility. Simulation and real-world experimental results on reaching tasks in human environments show that this technique can generate smooth and collision-free motions for a KUKA Light-Weight Robot.

The Dual-SLIP model has been proposed as a walking template that inherently encodes a rich set of human-like features. Previous work has used the 3D Dual-SLIP with bio-inspired leg actuation to generate a human-like dynamic walking gait over a wide range of speeds. The work presented in this paper extends the 3D Dual-SLIP walking strategy to uneven terrain. With nonlinear optimization based on a multiple-shooting formulation, actuated Dual-SLIP walking gaits over uneven terrain are identified that handle 1-step elevation changes up to ±10 cm. Moreover, this Dual-SLIP actuation strategy enables a constant center of mass (CoM) forward speed at leg midstance to be maintained. The resultant gaits have revealed a leg lengthening/shortening strategy that is similar to that adopted by a human when walking over prepared, uneven terrain. Results demonstrate that the CoM trajectories and ground reaction force patterns found with the approach are comparable to the human data found in the biomechanics literature. The trajectories generated by the Dual-SLIP model are also demonstrated to orchestrate a dynamic walking motion with an anthropomorphic humanoid model in simulation over uneven terrain.

The travel range of a mobile robot is directly linked to its energy consumption. Apart from the actuators, the computing resources are the main energy consumers. Thus, robots that can navigate with minimal computational resources would be able to travel longer distances and, hence, would be valuable tools in applications such as search and rescue or planetary exploration. Inspired from the navigational abilities of insects, we developed the Trail-Map as a data structure for biologically inspired homing that can easily be scaled in case of memory or computational shortage. The Trail-Map can be built in constant time and enables constant time homing vector calculation. In this paper, we evaluate the Trail-Map-based homing performance of a simulated mobile robot equipped with an omnidirectional camera under the presence of sensor noise, such as odometry errors and observation errors. Further, we will show that Trail-Map-based homing outperforms SLAM methods in terms of computational resources while achieving a comparable homing performance.

Robot table tennis is a challenging domain in both robotics, artificial intelligence and machine learning. In terms of robotics, it requires fast and reliable perception and control; in terms of artificial intelligence, it requires fast decision making to determine the best motion to hit the ball; in terms of machine learning, it requires the ability to accurately estimate where and when the ball will be so that it can be hit. The use of sophisticated perception (relying, for example, in multi-camera vision systems) and state-of-the-art robot manipulators significantly alleviates concerns with perception and control, leaving room for the exploration of novel approaches that focus on estimating where, when and how to hit the ball. In this paper, we move away from the hardware setup commonly used in this domain—typically relying on robotic manipulators combined with an array of multiple fixed cameras—and give the first steps towards having autonomous aerial table tennis robotic players. Specifically, we focus on the task of hitting a ping pong ball thrown at a commercial drone, equipped with a light cardboard racket and an onboard camera. We adopt a general framework for learning complex robot tasks and show that, in spite of the perceptual and actuation limitations of our system, the overall approach enables the quadrotor system to successfully respond to balls served by a human user.

Autonomous mobile robots are employed to perform increasingly complex tasks which require appropriate task descriptions, accurate object recognition, and dexterous object manipulation. In this paper we will address three key questions: How to obtain appropriate task descriptions from natural language (NL) instructions, how to choose the control program to perform a task description, and how to recognize and manipulate the objects referred by a task description? We describe an evaluated robotic agent which takes a natural language instruction stating a step of DNA extraction procedure as a starting point. The system is able to transform the textual instruction into an abstract symbolic plan representation. It can reason about the representation and answer queries about what, how, and why it is done. The robot selects the most appropriate control programs and robustly coordinates all manipulations required by the task description. The execution is based on a perception sub-system which is able to locate and recognize the objects and instruments needed in the DNA extraction procedure.

Flexible bevel-tipped needle steering has been an active research topic since the last decade. However, most of the work presented so far performs flexible needle steering in static phantoms or non-static virtual environments. The insertion of flexible bevel-tipped needles into soft-tissue subjected to motion disturbances is still an open research problem. In this paper, we propose a flexible needle steering algorithm that is able to handle physiological motion disturbances. The system estimates the disturbance using the force information provided by a force sensor placed in contact with the soft-tissue. The system is evaluated through experiments steering a flexible needle towards a physical target into a moving soft-tissue phantom. Three experimental cases with motion disturbances based on clinical procedures in the lung, kidney and heart are used to assess the targeting error. The average targeting error of all 15 experimental trials is 1:05±0:41mm. Our results demonstrate the ability of the proposed system to compensate for motion disturbances applied to the soft-tissue phantom.

In this paper we address the problem of collaborative active state estimation within the framework of multi-robot simultaneous localization and mapping (SLAM). We assume each robot has to autonomously navigate to a pre-specified set of goals in unknown environments and develop an approach that enables the robots to collaborate in order to reduce the uncertainty in their state estimation. We formulate this problem as multi-robot belief space planning, where the belief represents the probability distribution of robot states from the entire group, as well as the mapped environment thus far. Our approach is capable of guiding each robot to reduce its uncertainty by re-observing areas previously observed (only) by other robots. Direct observations between robot states, such as relative-pose measurements, are not required, providing enhanced flexibility for the group as the robots do not have to coordinate rendezvous with each other. Instead, our framework supports indirect constraints between the robots, that are induced by mutual observations of the same area possibly at different time instances, and accounts for these future multi-robot constraints within the planning phase. The proposed approach is evaluated in a simulation study.

Although passive sensors are widely used for many mobile robotics applications that perform mapping and localization functions, there are many environments (e.g., mining and planetary) where active sensors are more practical. However, at present, most 3D SLAM algorithms that do use LiDAR and/or time-of-flight (ToF) sensors exploit only range and bearing information associated with these measurements, but not intensity information. This paper presents a new approach that attempts to explicitly incorporate an intensity model as part of a sparse bundle adjustment (SBA) estimation problem. An observability analysis shows that a solution exists, and simulation results verify its potential utility.