IROS 2015 Papers

Using a new hardware implementation of our designs for tunably compliant spine-like tensegrity robots, we show that the NASA Tensegrity Robotics Toolkit can effectively generate and predict desirable locomotion strategies for these many degree of freedom systems. Tensegrity, which provides structural integrity through a tension network, shows promise as a design strategy for more compliant robots capable of interaction with rugged environments, such as a tensegrity interplanetary probe prototype surviving multi-story drops. Due to the complexity of tensegrity structures, modeling through physics simulation and machine learning improves our ability to design and evaluate new structures and their controllers in a dynamic environment. The kinematics of our simulator, the open source NASA Tensegrity Robotics Toolkit, have been previously validated within 1.3% error on position through motion capture of the six strut robot ReCTeR. This paper provides additional validation of the dynamics through the direct comparison of the simulator to forces experienced by the latest version of the Tetraspine robot. These results give us confidence in our strategy of using tensegrity to impart future robotic systems with properties similar to biological systems such as increased flexibility, power, and mobility in extreme terrains.

In a mixed environment of autonomous driverless vehicles and human driven vehicles operating on the same road, identifying intentions of human drivers and interacting with them in a compliant and responsible manner becomes a challenging problem for the driverless vehicles. In this paper, the problem of vehicle interaction at an intersection merging scenario is formulated as an Intention-Aware motion planning problem using the tools from Mixed Observability Markov Decision Process (MOMDP). We utilize the tools from recent intention aware planning framework to demonstrate a merging behavior in the presence of human drivers by trying to infer and act according to the intentions of the human drivers. A driver behavior model for T-junction intersections is developed in order to calculate the probabilistic state transition functions of the MOMDP model. With proposed solution, it is demonstrated that using intention aware planning improves performance in comparison to present time to merge approach by lowering accident probability and intersection navigation duration. The proposed method is tested on a real autonomous vehicle (AV) in the presence of human driven vehicles to validate our approach.

The research presented in this paper aims to find out what affect cognitive biases play in a robot's interactive behaviour for the goal of developing human-robot long-term companionship. It is expected that by utilising cognitive biases in a robot's interactive behaviours, making the robot cognitively imperfect, will affect how people relate to the robot thereby changing the process of long-term companionship. Previous research carried out in this area based on human-like cognitive characteristics in robots to create and maintain long-term relationship between robots and humans have yet to focus on developing human-like cognitive biases and as such is new to this application in robotics. To start working with cognitive biases `misattribution' and `empathic gap' have been selected which have been shown to be very common biases in humans and as such play a role on human-human interactions and long-term relationships.

We present a novel approach, entitled acoustic structure from motion (ASFM), for recovering 3D scene structure from multiple 2D sonar images, while at the same time localizing the sonar. Imaging sonar or forward looking sonar (FLS) is commonly used for autonomous underwater vehicle (AUV) navigation. An FLS provides bearing and range information to a target, but the elevation of the target is unknown within the sensor's field of view. Hence, current state-of-the-art techniques commonly make a flat surface (ground) assumption so that the FLS data can be used for navigation. Unlike other methods, our solution does not require a flat surface assumption and is capable of utilizing information from many frames, as opposed to pairwise methods that can only gather information from two frames at once. ASFM is inspired by structure from motion (SFM), the problem of recovering 3D structure from multiple camera images, while also recovering the position and orientation from which the images were taken. In this paper, we formulate and evaluate the optimization of several AUV sensor readings of the same scene from different poses, the sonar equivalent of bundle adjustment. We evaluate our approach on both simulated data and FLS sonar data with the assumption that feature extraction and data association have been completed. The acoustic equivalents of those two important features of SFM are left for future work.

A typical instrument for different tasks in minimally invasive surgery is a flexible endoscope. To overcome the problem of restricted motion of today's systems, a hyper-redundant active shaft concept is developed. It features good resistance with respect to manipulation forces through its unique binary, electromagnetic actuation concept. With an active control of each element of the hyper-redundant endoscope shaft and in combination with automated feed motion, it is controlled in a “follow-the-leader” fashion. In general, this approach is characterized by very good path following capabilities. However, due to its binary actuation concept, joint angles cannot be interpolated continuously. Therefore, an adaption to binary actuation with optimized switching sequences to achieve an appropriate path following performance despite of the restricted motion is proposed.

This paper presents a prototype of a meso-scale medical robot for Neurosurgical Intracerebral Hemorrhage Evacuation (NICHE). We use shape memory alloy (SMA) actuators for actuating individual degrees-of-freedom (DoFs). Pairs of antagonistic SMA wires enable bidirectional motion of bending joints. A novel torsion joint formed by a pair of antagonistic SMA torsion springs is installed at the distal end together with a pair of antagonistic SMA wires. This design provides articulation within the hemorrhage at the robot tip, thereby enabling effective hemorrhage evacuation. In this paper we derive the forward kinematics of the robot and the constitutive model of the SMA-actuated torsion joint. We also present experimental procedures to characterize parameters of the constitutive model. Finally, a proof-of-concept experiment is presented to show the working of the robot.

In this paper, a new multifunctional compliant joint is introduced for the design of unibody robotic mechanisms. Developed in the demanding context of interventional MRI, the compliant joint integrates kinematic, actuation, braking and sensing functions. Such integration is performed thanks to multimaterial additive manufacturing. Design and experimental behavior of the component are presented. A robotic assistant built with the proposed component and made of a single element is then introduced. It offers interesting performances while being compact and MR-compatible.

Walking controllers for bipedal robots have not yet reached human levels of robustness in locomotion. Imitating the human motor control might be an alternative strategy for generating robust locomotion in robots. We seek to control bipedal robots with a specific neuromuscular human walking model proposed previously. Here, we present a virtual neuromuscular controller, VNMC, that emulates this neuromuscular model to generate desired motor torques for a bipedal robot. We test the VNMC on a high-fidelity simulation of the ATRIAS bipedal robot constrained to the sagittal plane. We optimize the control parameters to tolerate maximum ground-height changes, which resulted in ATRIAS walking on a terrain with up to ±7 cm height changes. We further evaluate the robustness of the optimized controller to external and internal disturbances. The optimized VNMC adapts to 90% of random terrains with ground-height changes up to ±2 cm. It endures 95% of ±30 Ns horizontal pushes on the trunk, and 90% of 8 Ns backward and 4 Ns forward impulses on the swing foot throughout the gait cycle. Furthermore, the VNMC is resilient to modeling errors and sensor noise much larger than the equivalent uncertainties in the real robot. The results suggest VNMC as a potential alternative to generate robust locomotion in bipedal robots.

This paper describes a novel controller that is capable of simultaneously controlling torque and variable stiffness in real-time, for actuators with antagonistically driven pneumatic artificial muscles (PAMs). To this end, two contributions are presented: i) A stable force feedback controller that can cope with inherent PAM nonlinearities is synthesized using the dissipativity theory, for each PAM unit. ii) On top of this force feedback controller, a mathematical formulation is developed to compute reference force inputs that correspond to desired joint torque and joint stiffness inputs, concerning both agonist and antagonist PAMs. This strategy enables us to introduce real-time sensory feedback; torque and stiffness control is addressed by means of PAM force feedback control with guaranteed stability. To validate the proposed control scheme, a series of experiments were conducted on an experimental setup. As the result, the controller exhibited favorable torque and stiffness tracking in real-time, demonstrating that it could meet the performance criteria to power exoskeleton systems.

Trajectory planning is a critical aspect of autonomous tractor-trailer vehicle design. Trajectory planning algorithms usually compute paths first, trajectories are obtained thereafter. This multi-step feature makes those planners inefficacious to handle time-dependent constraints. In this study, we consider the original trajectory planning mission directly, which is described as an optimal control problem containing the kinematics, mechanical/physical constraints, environmental requirements as well as an optimization criterion. In this formulation, only the fundamental driving principles with no special issues (e.g., backing-up maneuver and jackknife) are considered. For example, the prevailing “small-angle assumption” is not utilized to prevent jackknifing. Instead, we only require that different parts of a tractor-trailer vehicle should not collide, since the emergence of jackknife does not physically violate the kinematics. An interior-point method based simultaneous approach is adopted to solve the formulated optimal control problem. Simulation results verify our proposal is capable of handling scenarios with various user-specified requirements.

Localization of sound sources in adverse environments is an important challenge in robot audition. The target sound source is often corrupted by coherent broadband noise, which introduces localization ambiguities as noise is often mistaken as the target source. To discriminate the time difference of arrival (TDOA) parameters of the target source and noise, this paper presents a binary mask for weighted generalized cross-correlation with phase transform (GCC-PHAT). Simulation and experiments on a mobile robot suggest that the proposed technique improves TDOA discrimination. It also brings the additional benefit of modulating the computing load requirement according to voice activity.

We consider the fundamental problem of gathering a set of n robots in the Euclidean plane which have a physical extent and hence they cannot share their positions with other robots. The objective is to determine a minimum time schedule to gather the robots as close together as possible around a predefined gathering point avoiding collisions. This problem has applications in many real world scenarios including fast autonomous coverage formation. Cord-Landwehr et al. (SOFSEM 2011) gave a local greedy algorithm in a synchronous setting and proved that, for the discrete version of the problem where robots movements are restricted to the positions on an integral grid, their algorithm solves this problem in O(nR) rounds, where R is the distance from the farthest initial robot position to the gathering point. In this paper, we improve significantly the round complexity of their algorithm to R + 2 · (n - 1) rounds. We also prove that there are initial configurations of n robots in this problem where at least R + (n - 1) over 2 rounds are needed by any local greedy algorithm. Furthermore, we improve the lower bound to R + (n - 1) rounds for the algorithm of Cord-Landwehr et al.. These results altogether provide a tight runtime analysis of their algorithm.

A unidirectional ultrasonic motor (USM) is applied to the thorax unit of a flapping micro aerial vehicle (MAV) with the aim to develop an MAV in the 10-gram-range, for which appropriate DC motors with high power-weight ratios are unavailable. As a trial, a unidirectional USM having an ultrasonic transducer with mass of 336 mg and rotor of 3 mm diameter is implemented to a belt drive mechanism in place of a DC motor and spar gear unit. At no load and without wings, the USM works well and achieves a flapping frequency exceeding 32 Hz, while the output power is around 36 mW owing to the mismatch of the two vibration modes that dominate the efficiency of the USM. Although the observed lift force remains low because of the lack of USM power and heaviness of the wings, the appropriateness of the mechanical design is confirmed and improvements toward the 10-gram-range flapping MAV with USM are discussed on the basis of these results. Another actuation mechanism of the flapping MAV is proposed.

This paper reports on a bipedal walking mechanism driven by constant heating, which walks on a heated horizontal surface. The mechanism is based on a passive dynamic walker and powered by thermal deformation of bimetal. A prototype is composed of two legs jointed to a waist shaft and cylindrical feet whose bottoms were looped around by bimetal sheets. When the prototype was placed on a horizontal surface heated at 170 °C, the bimetal sheets deformed to excite lateral swinging motion of the walker body and the walker successfully walked. The paper discusses the gait of the walker by analyzing swing periods in traveling direction and lateral direction. The estimation on the walker gait was quantitatively confirmed through motion tracking of walker legs.

This paper presents a general grasp synthesis algorithm. The algorithm follows a stochastic approach that calculate a large number of grasps with good quality in short amount of time. It takes as an input a 3D point cloud that represents the contact points of the object and heuristically selects concurrent points which are covered by contact points and identifies concurrent grasps from such contact points. Empirical examples are performed to show that the proposed algorithm can generate a large number of good quality grasps comparing to other methods.

We consider the problem of deploying a team of robots in an unknown, obstructed environment to form a multi-hop communication network. As a solution, we present a unified framework, onLinE rObotic Network formAtion (LEONA), that is general enough to permit optimizing the communication network for different utility functions in non-convex environments. LEONA adopts the principle of “optimism in the face of uncertainty” to allow the team of robots to form optimal network configurations efficiently and rapidly without having to map link qualities in the entire area. We demonstrate and evaluate this framework on two specific scenarios concerning the formation of a multi-hop communication path between fixed end-points: one minimizing the total path cost, and another maximizing the bottleneck communication rate. Our simulation-based evaluation shows that the use of the optimism principle can significantly reduce resources spent in exploring and mapping the entire region prior to network optimization. We also present a mathematical modeling of how the searched area scales with various relevant parameters in each case.

High feedback gains cannot be used on all robots due to sensor noise, time delays or interaction with humans. The problem with low feedback gain controlled robots is that the accuracy of the task execution is potentially low. In this paper we investigate if trajectory optimization of feedback-feedforward controlled robots improves their accuracy. For rest-to-rest motions, we find the optimal trajectory indirectly by numerically optimizing the corresponding feedforward controller for accuracy. A new performance measure called the Manipulation Sensitivity Norm (MSN) is introduced that determines the accuracy under most disturbances and modeling errors. We tested this method on a two DOF robotic arm in the horizontal plane. The results show that for all feedback gains we tested, the choice for the trajectory has a significant influence on the accuracy of the arm (viz. position errors being reduced from 2.5 cm to 0.3 cm). Moreover, to study which features of feedforward controllers cause high or low accuracy, four more feedforward controllers were tested. Results from those experiments indicate that a trajectory that is smooth or quickly approaches the goal position will be accurate.

The pectoral fin is a major source of thrust generation for fish utilizing median or paired fins (MPF) mode. Along with the pectoral fin, spanwise flexibility plays a major role in the generation of thrust. This paper investigates the effect of spanwise flexibility on propulsion performance using theoretical and experimental studies. For this study, the kinematic model of oscillating pectoral fin is first established based on the mechanism of active/passive flexible deformation. The dynamic model of the oscillating pectoral fin is developed based on the blade element theory. The influence of spanwise flexibility on propulsion performance is analyzed theoretically. Experiments have also been carried out to study the propulsion performance of the bionic pectoral fin by changing spanwise flexibility. Both calculated results and experiment results show that the propulsion performance of the bionic pectoral fin can be enhanced with the certain degree of spanwise flexibility.

The dynamic model of a manipulator described by second-order differential equations suggests that the couplings between its links are strong. Consequently, any control task (position regulation or desired trajectory tracking) is imprecise. Hence, the decoupling of dynamic equations in robotic mechanisms has attracted many researchers in recent years and various solutions have been proposed. This paper deals with a new dynamic decoupling principle, which involves connecting to a serial manipulator a two-link group forming a Scott-Russell mechanism with the initial links of the manipulator. The goal is to simplify the controller design by reducing the effects of complicated manipulator dynamics. The opposite motion of links in the Scott-Russell mechanism combined with optimal redistribution of masses allows the cancellation of the coefficients of nonlinear terms in the manipulator's kinetic and potential energy equations. Then, by using the optimal control design, the dynamic decoupling due to the changing payload is achieved. The proposed approach, which is a symbiosis of mechanical and control solutions, improves the known design concepts permitting the dynamic decoupling of serial manipulators taking into account the changing payload. The suggested design methodology is illustrated by simulations carried out using ADAMS and MATLAB software, which have confirmed the efficiency of the developed approach.