IROS 2015 论文列表

Concentric tube robots are tentacle-like medical robots that can bend around anatomical obstacles to access hard-to-reach clinical targets. The component tubes of these robots can be swapped prior to performing a task in order to customize the robot's behavior and reachable workspace. Optimizing a robot's design by appropriately selecting tube parameters can improve the robot's effectiveness on a procedure- and patient-specific basis. In this paper, we present an algorithm that generates sets of concentric tube robot designs that can collectively maximize the reachable percentage of a given goal region in the human body. Our algorithm combines a search in the design space of a concentric tube robot using a global optimization method with a sampling-based motion planner in the robot's configuration space in order to find sets of designs that enable motions to goal regions while avoiding contact with anatomical obstacles. We demonstrate the effectiveness of our algorithm in a simulated scenario based on lung anatomy.

We consider the problem of estimating high-quality color models of 3D meshes, given a collection of RGB images of the original object. Applications of a database of high-quality colored meshes include object recognition in robot vision, virtual reality, graphics, and online shopping. Most modern approaches that color a 3D object model from a collection of RGB images face problems in (1) producing realistic colors for non-Lambertian surfaces and (2) seamlessly integrating colors from multiple views. Our approach efficiently solves a non-linear least squares optimization problem to jointly estimate the RGB camera poses and color model. We discover that incorporating 2D texture cues, vertex color smoothing, and texture-adaptive camera viewpoint selection into the optimization problem produces qualitatively more coherent color models than those produced by competing methods. We further introduce practical strategies to accelerate optimization. We provide extensive empirical results on the BigBIRD dataset [15], [21]: results from a user study with 133 participants indicate that on all 16 objects considered, our method outperforms competing approaches. Our code is available for download online at http://rll.berkeley.edu/iros2015colormodels.

This paper is concerned with the mechanical design of a mobile manipulating unmanned ground vehicle (MM-UGV) coupled with an existing commercial unmanned aerial vehicle (UAV) to create a novel hybrid aerial/ground mobile manipulator. A novel systematic optimization-based approach is presented for making important design choices, such as the selection of gearboxes and electric DC motors in the drive-train, manipulator link lengths, and UGV base length. The objective is to minimize the overall mass of the MM-UGV. Constraints related to workspace, dynamic tip-over stability, actuator torque/force limits in static and dynamic motions, executed in the air or on the ground, and battery properties are incorporated into the problem formulation. The system operating conditions in the form of the range of end-effector forces, operating surface grade, and various position, velocity and acceleration variables are provided by the designer. The resulting problem is a robust bilevel nonlinear optimization, in which some of the constraints are derived from maximization/minimization over the operational variables to ensure constraint satisfaction in all possible operation scenarios. The problem is solved using a genetic algorithm. Numerical simulations demonstrate that the proposed strategy produces a design that meets the user-specified requirements.

This paper discusses an optimal torque distribution for a redundant 3-RRR spherical parallel manipulator (SPM). The SPM has parallel singular configurations within its workspace. The singularity leads to an amplification of the actuated joint torques which violates the actuator torque limits. The use of a redundant actuator solves the effect of the singularity. The quadratic sum of the actuated joint torques is minimized in order to obtain an optimal torque distribution. Experiments carried out show the advantage of introducing redundancy of actuators to improve the behavior of the SPM.

This paper presents an algorithm that optimally explores an unknown environment with regions of varying degrees of importance. The algorithm, termed Ergodic Environmental Exploration (E3), is a finite receding horizon optimal control algorithm that minimizes control effort and the difference between the time average behavior of the system's trajectory and the distribution of the gain in information. The novelty of the E3 algorithm is the gain in information distribution used in the exploration trajectory optimization. The gain in information distribution uses an estimate of the information distribution and the confidence value on that estimate. Successful experiments have been conducted using E3 on a real mobile robot to explore an unknown 2-dimensional area. Results of these experiments are discussed and displayed with figures and a movie.

We introduce a task oriented optimal controller based on the finite horizon quadratic regulator (LQR). The task of being at a specified position with a specified velocity in specified time is formalized by cost functions. Moreover, we include soft constraints which are part of the cost function, such that the optimal control can be computed in fixed time. We show how soft constraints help to use redundancy and point out the limitations of our soft constraint approach in simulations.

We introduce a novel setup of self-supervised learning (SSL), in which optical flow provides the supervised outputs. Optical flow requires significant movement for obstacle detection. The main advantage of the introduced method is that after learning, a robot can detect obstacles without moving - reducing the risk of collisions in narrow spaces. We investigate this novel setup of SSL in the context of a Micro Air Vehicle (MAV) that needs to select a suitable landing place. Initially, when the MAV flies over a potential landing area, the optical flow processing estimates a ‘surface roughness’ measure, capturing whether there are obstacles sticking out of the landing surface. This measure allows the MAV to select a safe landing place and then land with other optical flow measures such as the divergence. During flight, SSL takes place. For each image a texton distribution is extracted (capturing the visual appearance of the landing surface in sight), and mapped to the current roughness value by a linear regression function. We first demonstrate this principle to work with offline tests involving images captured on board an MAV, and then demonstrate the principle in flight. The experiments show that the MAV can land safely on the basis of optical flow. After learning it can also successfully select safe landing spots in hover. It is even shown that the appearance learning allows the pixel-wise segmentation of obstacles.

The Rubber Hand Illusion is a well known multisensory illusion in which the participant feels their own unseen hand become a rubber hand that is placed at the position where their hand is supposedly placed, triggered by synchronized haptic stimuli on their hand and the rubber hand. Our previous study showed that a robotized 1-degree-of-freedom rubber hand in the form of a master-slave system significantly enhanced the illusion compared with the classic rubber hand illusion. The illusion gives the sense that the part of body becomes part of the robot - it is thus assumed that this multisensory illusion is a potentially effective way to increase intuitiveness, and therefore increase the operability of a multi-degrees-of-freedom master-slave system. To further investigate the concept, we built a multisensory illusion inducible microsurgical robotic system based on the findings of the previous study and conducted a series of psychophysical experiments. The experimental results reveal that the multisensory illusion and operability is significantly correlated.

In this paper we present an open-source design for the development of low-complexity, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each finger. The proposed design is unique since with a single motor and the proposed differential mechanism the user is able to control each finger independently and switch between different grasping postures in an intuitive manner. Anthropomorphism of robot structure and motion is achieved by employing in the design process an index of anthropomorphism. The proposed robot hands can be easily fabricated using low-cost, off-the-shelf materials and rapid prototyping techniques. The efficacy of the proposed design is validated through different experimental paradigms involving grasping of everyday life objects and execution of daily life activities. The proposed hands can be used as affordable prostheses, helping amputees regain their lost dexterity.

Plan-based robot control has to consider a multitude of aspects of tasks at once, such as task dependency, time, space, and resource usage. Hybrid planning is a strategy for treating them jointly. However, by incorporating all these aspects into a hybrid planner, its search space is huge by construction. This paper introduces the planner CHIMP, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space. The paper makes two contributions: First, it describes how HTN planning is integrated into meta-CSP reasoning leading to a planner that can reason about different forms of knowledge and that is fast enough to be used on a robot. Second, it demonstrates CHIMP's task merging capabilities, i.e., the unification of different tasks from different plan parts, resulting in plans that are more efficient to execute. It also allows to merge new tasks online into a plan that is being executed. This is demonstrated on a PR2 robot.

We address the problem of verifying motion plans for aerial robots in uncertain and partially-known environments. Thereby, the initial state of the robot is uncertain due to errors from the state estimation and the motion is uncertain due to wind disturbances and control errors caused by sensor noise. Since the environment is perceived at runtime, the verification of partial motion plans must be performed online (i.e. during operation) to ensure safety within the planning horizon and beyond. This is achieved by efficiently generating robust control invariant sets based on so-called loiter circles, where the position of the aerial robot follows a circular pattern. Verification of aerial robots is challenging due to the nonlinearity of their dynamics, the high dimensionality of their state space, and their potentially high velocities. We use novel techniques from reachability analysis to overcome those challenges. In order to ensure that the robot never finds itself in a situation for which no safe maneuver exists, we provide a technique that ensures safety of aerial robots beyond the planning horizon. Our method is applicable to all kinds of robotic systems that follow reference trajectories, such as bipedal robotic walking, robotic manipulators, automated vehicles, and the like. We evaluate our method by simulations of high speed helicopter flights.

In the robotic coverage problem, a robot is required to visit every point of a given area using the shortest possible path. In a recently introduced version of the problem, adversarial coverage, the covering robot operates in an environment that contains threats that might stop it. Previous studies of this problem dealt with finding optimal strategies for the coverage, that minimize both the coverage time and the probability that the robot will be stopped before completing the coverage. However, these studies assumed that a map of the environment, which includes the specific locations of the threats, is given to the robot in advance. In this paper, we deal with the online version of the problem, in which the covering robot has no a-priori knowledge of the environment, and thus has to use real-time sensor measurements in order to detect the threats. We employ a frontier-based coverage strategy that determines the best frontier to be visited by taking into account both the cost of moving to the frontier and the safety of the region that is reachable from it. We also examine the effect of the robot's sensing capabilities on the expected coverage percentage. Finally, we compare the performance of the online algorithm to its offline counterparts under various environmental conditions.

We propose a new algorithm capable of online regeneration of gait patterns. The algorithm uses a nonlinear optimization technique to find step parameters that will bring the robot from the present state to a desired state. It modifies online not only the footstep positions, but also the step timing in order to maintain dynamic stability during walking. Inclusion of step time modification extends the robustness against rarely addressed disturbances, such as pushes towards the stance foot. The controller is able to recover dynamic stability regardless of the source of the disturbance (e.g. model inaccuracy, reference tracking error or external disturbance). We describe the robot state estimation and center-of-mass feedback controller necessary to realize stable locomotion on our humanoid platform COMAN. We also present a set of experiments performed on the platform that show the performance of the feedback controller and of the gait pattern regenerator. We show how the robot is able to cope with series of pushes, by adjusting step times and positions.

Localization is a critical capability for many autonomous mobile robots. Implicit in all localization and place recognition techniques is the ability to decide whether a place is novel or familiar; the idea of “has this place been visited before?”. To make this decision accurately, localization systems typically require prior calibration or training on datasets from the specific type of operating environment; a requirement that is not practical in many environments or applications. One approach to this problem has been to remove the perfect data association requirement for place recognition systems by utilizing robust pose graphs. However, this approach requires a batch-based process and consequently does not solve the problem of real-time navigation in the interim before optimization. We present a novel technique which calibrates the localization system online as the robot moves through an environment, alleviating the need for prior calibration of a localization system. We leverage a robust pose graph optimization to determine a suitable model for our place recognition system which is sensor-invariant and does not require manually-labeled data for threshold tuning. The system continually refines place recognition parameters using sensory data and an optimized pose graph in order to increase the instantaneous precision of the location matches output by the underlying robust pose graph. We evaluate the system using the public New College dataset and a multi-sensor indoor office dataset. Our results indicate that the system produces superior mapping performance to a static place recognition model, and superior instantaneous place recognition performance to a robust pose graph technique.

This paper presents a methodology to optimize the drive forces and steering angles for achieving accurate path tracking by a four wheel steer and four wheel drive (4WS4WD) vehicle. The 7-order Bézier curves are applied in path planning and online tracking, and used to obtain the kinematic and dynamic profiles for the guidance of vehicle. The 8-input dynamic model is developed and applied in an objective function, which is optimized by PSO algorithm to obtain the optimal driving and steering motions for tracking the planned path precisely. The slip angles and lateral forces are also considered in the dynamic model, and the vehicle motion optimization can be implemented in real time. Note that this paper does not address the implementation of the force and steering controllers at the wheels. Simulation results are provided to validate the proposed method.

Temporal logics have proven effective for correct-by-construction synthesis of controllers for a wide range of robotic applications. Receding horizon frameworks mitigate the computational intractability of reactive synthesis for temporal logic, but have thus far been limited by pursuing a single sequence of short horizon problems to the goal. We propose a receding horizon algorithm for reactive synthesis that automatically determines a path to the currently pursued goal at runtime, responding as needed to nondeterministic environment behavior. This is achieved by allowing each short horizon to have multiple local goals, and determining which local goal to pursue based on the current global goal, the currently perceived environment and a pre-computed invariant dependent on the global goal. We demonstrate the utility of this additional flexibility in grant-response tasks, using a search-and-rescue example. Moreover, we show that these goal-dependent invariants mitigate the conservativeness of the receding horizon approach.

Many robotic missions require online estimation of the unknown state transition models associated with uncertainty that stems from mission dynamics. The learning problem is usually distributed among agents in multiagent scenarios, either due to the absence of a centralized processing unit or because of the large size of the joint learning problem. This paper addresses the problem of multiagent learning in the likely scenario that agents estimate different models from their measured data, but they can share information by communicating model parameters. Previous approaches either consider homogeneous scenarios or perform model transfer in an open-loop manner, which hinders the convergence rate. We develop a closed-loop multiagent learning algorithm, Collaborative Filtering-Decentralized Incremental Feature Dependency Discovery (CF-Dec-iFDD), which enables agents to learn and share models in heterogeneous scenarios. Each agent learns a linear function approximation of the actual model, and the number of features is increased incrementally to adjust model complexity based on the observed data. The agents obtain feedback from other agents on the model error reduction associated with the communicated features. Although this increases the communication cost of exchanging features, it improves the quality/utility of what is being exchanged, leading to improved convergence rate. The approach is demonstrated in indoor hardware flight tests on a forest fire management scenario for which agents must learn the transition model of the fire spread depending on external factors such as wind and vegetation. It is shown that CF-Dec-iFDD has superior convergence rate compared to the alternative approaches.

In this paper, we propose a novel online gait task recognition algorithm for hip exoskeleton. The proposed algorithm provides an automatic and prompt recognition result in just one step based on the relations between both hip joint angles at the moment of foot contact. Gait task recognition is one of the challenges that walking assist devices must address to offer adaptable and reliable assistance to users. However gait task recognition in hip exoskeleton is challenging because the sensors are very limited and fast gait task recognition is required to prevent inadequate assistance and reduce fall risk. Although in general foot contact event can be considered as crucial information during walking, it has not received attention in hip exoskeletons with no sensors corresponding foot force or pressure. In this study, we exploit foot contact event as a critical point to perform gait task recognition in hip exoskeleton. The proposed algorithm suggests a foot contact estimation method without using any foot force or pressure sensors and a rule-based inference system to recognize a new gait task in real time. Results presented from experiments will demonstrate the validity and performance of the proposed algorithm.

In this paper, we propose a cooperative control of a wearable robot for paraplegics. The control system adjusts the stride length and walking velocity of a robot according to user's voluntary movement while generating a continuous walking pattern. We assume that the desired stride length and walking speed correspond to the preceding motion of a walking aid (walker), and the motion of the walker by the user was measured by a laser range sensor and an accelerometer. The continuous walking pattern during a step was planned by optimization of joint trajectories under inequality conditions. To perform continuous walking, the desired trajectory is required before the walker movement. To adjust the foot placement according to the walker movement, the desired walking pattern in the late swing phase is replanned. For the latest movement distance of the walker, the posture at heel strike is modified by Newton-like iterative calculations. We confirmed that a normal subject could walk continuously with a wearable robot controlled by our system while changing the stride length and walking speed.