ICRA 2015 Papers

Sampling-based algorithms provide efficient solutions to high-dimensional, geometrically complex motion planning problems. For these methods asymptotic results are known in terms of completeness and optimality. Previous work by the authors argued that such methods also provide probabilistic near-optimality after finite computation time using indications from Monte Carlo experiments. This work formalizes these guarantees and provides a bound on the probability of finding a near-optimal solution with PRM* after a finite number of iterations. This bound is proven for general-dimension Euclidean spaces and evaluated through simulation. These results are leveraged to create automated stopping criteria for PRM* and sparser near-optimal roadmaps, which have reduced running time and storage requirements.

Untethered magnetic devices (UMDs) driven by rotating magnetic fields have been proposed for applications in minimally invasive medicine. Previous work has explored the propulsion and guidance of a single UMD using a rotating magnet dipole source, and actuation of multiple UMDs has focused on the use of uniform magnetic fields. This work seeks to extend the methods for remote actuation using a single magnet dipole source to generate two rotating magnetic fields with arbitrary independent rotation axes at any two points with a common rotation frequency. The solutions found show that there are multiple positions and rotation axes of the magnet dipole that can generate these rotating fields. The method is demonstrated with two screw-type UMDs propelled independently within an agar gel block using a single permanent-magnet actuator.

The simultaneous localization and mapping (SLAM) problem in mobile robotics has traditionally been formulated using random vectors. Alternatively, random finite sets(RFSs) can be used in the formulation, which incorporates non-heursitic-based data association and detection statistics within an estimator that provides both spatial and cardinality estimates of landmarks. This paper mathematically shows that the two formulations are actually closely related, and that RFS SLAM can be viewed as a generalization of vector-based SLAM. Under a set of ideal detection conditions, the two methods are equivalent. This is validated by using simulations and real experimental data, by comparing principled realizations of the two formulations.

Redundant robots performing multiple tasks of different priority levels are often handled by hierarchical control frameworks. This paper proposes a general hierarchical control approach that can handle not only a single standard lexicographic hierarchy, but also a complex priority network involving both strict and non-strict task priorities. In this approach, priorities can be defined by pairs of tasks and are encoded by a priority matrix. Priority modulations are achieved by the regulation of a novel generalized projector, which takes the priority matrix as input. This projector allows a task to be completely projected in the null-space of a set of tasks, while partially projected in those of some other tasks. Such a projector can be used to achieve multiple priority rearrangements simultaneously. The effectiveness of this approach is demonstrated on a KUKA LWR robot performing task priority rearrangements as well as task insertion and deletion.

Swarms consist of large numbers of individual agents that generally maintain no fixed relative positions, which makes describing the behavior of the swarm as a whole difficult. Furthermore, the high number of agents leads to frequent occlusions that prevent observations of the entire swarm. In this paper, we represent the behavior of swarms using velocity fields, yielding a description which is invariant to the number of agents in a swarm, and the position, orientation, and scale of the swarm. The velocity field representation allows the behavior of swarms to be modeled as a Gaussian distribution. We demonstrate that this Gaussian model can be used to reconstruct the behavior of the swarm as a whole from partial observations.

This paper presents a novel proximity query (PQ) approach capable to detect the collision and calculate the minimal Euclidean distance between two non-convex objects in 3D, namely the robot and the environment. Such approaches are often considered as computationally demanding problems, but are of importance to many applications such as online simulation of haptic feedback and robot collision-free trajectory. Our approach enables to preserve the representation of unstructured environment in the form of triangular meshes. The proposed PQ algorithm is computationally parallel so that it can be effectively implemented on graphics processing units (GPUs). A GPU-based computation scheme is also developed and customized, which shows >200 times faster than an optimized CPU with single core. Comprehensive validation is also conducted on two simulated scenarios in order to demonstrate the practical values of its potential application in image-guided surgical robotics and humanoid robotic control.

Computing grasps for an object is challenging when the object geometry is not known precisely. In this paper, we explore the use of Gaussian process implicit surfaces (GPISs) to represent shape uncertainty from RGBD point cloud observations of objects. We study the use of GPIS representations to select grasps on previously unknown objects, measuring grasp quality by the probability of force closure. Our main contribution is GP-GPIS-OPT, an algorithm for computing grasps for parallel-jaw grippers on 2D GPIS object representations. Specifically, our method optimizes an approximation to the probability of force closure subject to antipodal constraints on the parallel jaws using Sequential Convex Programming (SCP). We also introduce GPIS-Blur, a method for visualizing 2D GPIS models based on blending shape samples from a GPIS. We test the algorithm on a set of 8 planar objects with transparency, translucency, and specularity. Our experiments suggest that GP-GPIS-OPT computes grasps with higher probability of force closure than a planner that does not consider shape uncertainty on our test objects and may converge to a grasp plan up to 5.7×faster than using Monte-Carlo integration, a common method for grasp planning under shape uncertainty. Furthermore, initial experiments on the Willow Garage PR2 robot suggest that grasps selected with GP-GPIS-OPT are up to 90% more successful than those planned assuming a deterministic shape. Our dataset, code, and videos of our experiments are available at http://rll.berkeley.edu/icra2015grasping/.

We present a novel rough terrain traversability analysis method for mobile robot navigation. We focused on the scenario of mobile robot operation in a disaster environment with limited sensor data. The robot builds a map in real time and analyzes the terrain traversability of its surrounding environment. The proposed method is based on fuzzy inference so that it can handle uncertainties in the sensor data. Two values associated with the terrain traversability, roughness and slope, are calculated from an elevation map built by a laser range finder mounted on the mobile robot. These two values are inputted to the fuzzy inference, and the traversability is analyzed. Based on the traversability output from the fuzzy inference, a vector field histogram (VFH) is generated. The mobile robot course is determined according to the VFH. We demonstrated our algorithm on an artificial environment. The experimental results showed that the mobile robot was able to reach the target position safely while avoiding untraversable areas.

A state estimator for a quadrocopter is presented, using measurements from an accelerometer, angular rate gyroscope, and a set of ultra-wideband ranging radios. The estimator uses an extended aerodynamic model for the quadrocopter, where the full 3D airspeed is observable through accelerometer measurements. The remaining quadrocopter states, including the yaw orientation, are rendered observable by fusing ultra-wideband range measurements, under the assumption of no wind. The estimator is implemented on a standard microcontroller using readily-available, low-cost sensors. Performance is experimentally investigated in a variety of scenarios, where the quadrocopter is flown under feedback control using the estimator output.

This paper presents a method to transfer functional grasps among objects of the same category through contact warping and local replanning. The method transfers implicit knowledge that enables an action on a class of objects for which no explicit grasp or task information has been given in advance. Contact points on the source object are warped based on global and local shape similarities to the target object. These warped contacts are then used to define a hand posture that reaches close to them, while at the same time provides the desired functionality on the object. The approach is tested on different sets of objects with a success rate of 87.5%, and large benefits are shown when compared to a naive technique that only transfers a suitable hand pose to the novel object.

In vehicle performance tests, fuel consumption, the components of exhaust gas, and mileage are measured on a chassis dynamometer that simulates the road load. It is difficult even for a professional driver to maintain speeds within the tolerance band of driving test cycles. Thus, there has long been an expectation that robotic drivers will be able to test vehicle performance with high reproducibility. In previous studies of robotic drivers, the improvement of tracking performance has been studied for each driving test cycle. In this study, we proposed a control system for realizing a driving test with smooth pedal use and low fuel consumption. We derived a set reference speed profile within a tolerance band from a driving test cycle standard. First, we performed a vehicle pre-test with a professional driver. From this driving test, we found that the square value of the power consumed by the vehicle was effective for driving with smooth pedal operation. Therefore, we proposed an objective function considering the square value of the power. We then derived the reference speed in a driving test cycle tolerance band for the robotic driver by the proposed method. Finally, we performed the vehicle test using the robotic driver. In the test, we used a standard JC08 chassis dynamometer, a moving averaged JC08 waveform, and the proposed speed waveform. We made sure that the proposed system was effective using the results of the actual vehicle tests.

This paper is about localising at night in urban environments using vision. Despite it being dark exactly half of the time, surprisingly little attention has been given to this problem. A defining aspect of night-time urban scenes is the presence and effect of artificial lighting - be that in the form of street or interior lighting through windows. By building a model of the environment which includes a representation of the spatial location of every light source, localisation becomes possible using monocular cameras. One of the challenges we face is the gross change in light appearance as a function of distance due to flare, saturation and bleeding - city lights certainly do not appear as point features. To overcome this, we model the appearance of each light as a function of vehicle location, using this to inform our data-association decisions and to regularise the cost function which is used to infer vehicle pose. In this way we develop a place-dependent but stable sensor model which is customised for the particular environment in which we are operating. We demonstrate that our system is able to localise successfully at night over 12 km in situations where a traditional point feature based system fails.

We propose a novel method for non-uniform terrain coverage using Unmanned aerial vehicles (UAVs). The existing methods for coverage path planning consider a uniformly interesting target area and hence all the regions are covered with high resolution. However in many real world applications items of interest are not uniformly distributed but form patches of locally high interest. Therefore, with sparse sampling of uninteresting sections of the environment and high resolution sampling of interesting patches, the trajectory of the robot can become shorter. In this paper, we present a novel coverage strategy based on Space-Filling Curves that can accomplish non-uniform coverage of regions in the target area. Simulations and real robot experiments indicate that with the new strategy, travel time / cost of the task can be (almost) always less than a regular ‘lawnmower’ coverage pattern.

Lasers are being used in various surgical procedures to remove tissue or bones, to coagulate vessels or other structures. Due to difficulties in handling only a limited number of surgeons manage to display sufficient levels of precision in Minimally Invasive Surgery (MIS) procedures. Prior works on robotic laser surgery demonstrated shorter learning curves and higher ablation precision, but unfortunately ignored the fact that most clinically relevant tasks are bi-manual by nature. Surgeons are also reluctant to use current commercial surgical robotic systems for complex laser tasks, indicating that the lack of haptic feedback prevents them from efficient and safe tissue handling in preparation of laser treatment. This paper expands earlier robotic laser work towards bi-manual operation. The paper introduces a system for precisely tensioning tissue that is being targeted by the laser. An artificial test-setup that captures some essential features of bimanual laser surgery is described. Experiments have been conducted to investigate the effect of haptic feedback on ablation performance. A comparison is made of achievable levels of ablation precision when there is no haptic feedback, when there is haptic feedback and when an automatic tension control algorithm is deployed. The conducted experimental results confirm the great potential of haptic feedback and automatic tensioning systems for complex bi-manual lasering tasks.

Recent advances in robotics engineering have enabled the realization of self-folding machines. Rigid origami is usually used as the underlying model for the self-folding machines whose surface remains rigid during folding except at joints. A key issue in designing rigid origami is foldability that concerns about finding folding steps from a flat sheet of crease pattern to a desired folded state. Although recent computational methods allow rapid simulation of folding process of certain rigid origamis, these methods can fail even when the input crease pattern is extremely simple. In this paper, we take on the challenge of planning folding and unfolding motion of origami tessellations, which are composed of repetitive crease patterns. The number of crease lines of a tessellation is usually large, thus searching in such a high dimensional configuration space with the requirement of maintaining rigidity is nontrivial. We propose a motion planner that takes symmetry into consideration and reuses folding path found on the essential crease pattern. Both of these strategies enable us to fold large origami tessellation much more efficiently than existing methods. Our experimental results show that the proposed method successfully folds several types of rigid origami tessellations that existing methods fail to fold.

Aerial robots provide valuable support in several high-risk scenarios thanks to their capability to quickly fly to locations dangerous or even inaccessible to humans. In order to fully benefit from these features, aerial robots should be easy to transport and rapid to deploy. With this aim, this paper focuses on the development of a novel pocket sized quadrotor with foldable arms. The quadrotor can be packaged for transportation by folding its arms around the main frame. Before flight, the quadrotor's arms self-deploy in 0.3 seconds thanks to the torque generated by the propellers. The paper describes the design strategies used for developing lightweight, stiff and self-deployable foldable arms for miniature quadrotors. The arms are manufactured according to an origami technique with a foldable multi-layer material. A prototype of the quadrotor is presented as a proof of concept and performance of the system is assessed.

Quality evaluation of oocyte and embryo has become one of the most important issues for many biomedical applications such as domestic animals and fertilization treatment. We developed measurement of oxygen consumption rate of individual oocyte by indirect fluorescence measurement using fluorescence oxygen sensor array having stripe-shape on a microfluidic chip. The fluorescence oxygen sensor was made of the mixture of photo-crosslinkable resin, polyethylene glycol diacrylate (PEG-DA) and Tris (2, 2'-bipyridyl) dichloro ruthenium (II) hexahydrate (Ru(bpy)3Cl2). The sensor was patterned to stripe-shape in the polydimethylsiloxane using photofabrication and molding technique. Oxygen concentration around the oocyte was distributed according to spherical diffusion theory because of similar oxygen permeability of polydimethylsiloxane and that of medium. The sensor array detects two-dimensional distribution of oxygen concentration and oxygen consumption ratio of the oocyte is calculated by image processing and Fick's low. We demonstrated non-contact measurement of oxygen consumption ratio of single mouse oocyte on a microfluidic chip (0.59 ± 0.03 fmol/s).

Underwater robots conventionally use vision and sonar sensors for perception purposes, but recently bio-inspired sensors that can sense flow have been developed. In literature, flow sensing has been shown to provide useful information about an underwater object and its surroundings. In the light of this, we develop an underwater landmark recognition technique which is based on the extraction and comparison of compact flow features. The proposed features are based on frequency spectrum of a pressure signal acquired by a piezo-resistive sensor. We report experiments in semi-natural (human-made flume with obstacles) and natural (river) underwater conditions where the proposed technique successfully recognizes previously visited locations.

We present the control design, system integration, and free flight evaluation of a novel 227 g swashplateless coaxial helicopter. This micro aerial vehicle (MAV) achieves authority over roll, pitch, and yaw orientation as well as maneuvering thrust using only two propellers directly affixed to two motors. No additional aerodynamic control surfaces or actuators are introduced. Instead, cyclic control is obtained through the underactuated dynamic response of the main rotor itself to a modulated drive torque. Comparisons are drawn to conventional four-motor quadrotors and four-actuator fixed pitch coaxial helicopters in terms of mechanical complexity and actuator mass fraction. Trim power consumption in hover is reported across a range of symmetric and asymmetric loading states. Closed loop trajectory tracking maneuvers are demonstrated in a motion capture environment.