waashk/dblp huggingface dataset:
equipping robot control programs with first-order probabilistic reasoning capabilities. an autonomous robot system that is to act in a real-world environment is faced with the problem of having to deal with a high degree of both complexity as well as uncertainty. therefore, robots should be equipped with a knowledge representation system that is able to soundly handle both aspects. in this paper, we thus introduce an architecture that provides a coupling between plan-based robot controllers and a probabilistic knowledge representation system based on recent developments in statistical relational learning, which possesses the required level of expressiveness and generality. we outline possible applications of the corresponding models in the context of robot control, discussing suitable representation formalisms, inference and learning methods as well as transparent extensions of a robot planning language that allow robot control programs to soundly integrate the results of probabilistic inference into their plan generation process.
motion planning for high dof anthropomorphic hands. the paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. the proposed approach tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. the approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are included to illustrate its validity.
in-pipe robot navigation based on the landmark recognition system using shadow images. in this paper, we present an autonomous in-pipe robot navigating system using vision-based landmark recognition. we propose to use special features of the pipelines (such as elbows or branches) as landmarks by recognizing the shadows of the elbows and branches. to obtain consistent shadow images, the in-pipe robot equips with specially designed illuminator. by analyzing the shadow, the robot can easily identify these landmarks, detect the direction of the passage, and adaptively traverse through while continuously updating the map. the effectiveness of the proposed method is verified by real experiments using the in-pipe robot mrinspect v for inspecting inside of the miniature urban 8-inch gas pipeline structure.
fast running experiments involving a humanoid robot. the present paper describes an implementation of fast running motions involving a humanoid robot. two important technologies are described: a motion generation and a balance control. the motion generation is a unified way to design both walking and running and can generate the trajectory with the vertical conditions of the center of mass (com) in short calculation time. the balance control enables a robot to maintain balance by changing the positions of the contact foot dynamically when the robot is disturbed. this control consists of 1) compliance control without force sensors, in which the joints are made compliant by feed-forward torques and adjustment of gains of position control, and 2) feedback control, which uses the measured orientation of the robot's torso used in the motion generation as an initial condition to decide the foot positions. finally, a human-sized humanoid robot that can run forward at 7.0 [km/h] is presented.
manipulation planning on constraint manifolds. we present the constrained bi-directional rapidly-exploring random tree (cbirrt) algorithm for planning paths in configuration spaces with multiple constraints. this algorithm provides a general framework for handling a variety of constraints in manipulation planning including torque limits, constraints on the pose of an object held by a robot, and constraints for following workspace surfaces. cbirrt extends the bi-directional rrt (birrt) algorithm by using projection techniques to explore the configuration space manifolds that correspond to constraints and to find bridges between them. consequently, cbirrt can solve many problems that the birrt cannot, and only requires one additional parameter: the allowable error for meeting a constraint. we demonstrate the cbirrt on a 7dof wam arm with a 4dof barrett hand on a mobile base. the planner allows this robot to perform household tasks, solve puzzles, and lift heavy objects.
tankbot: a miniature, peeling based climber on rough and smooth surfaces. tankbot is a miniature, energy efficient, lightweight (60 g), and robust climbing robot. it uses the continuous detachment force (peeling) of the flat, bulk tacky elastomer tread to climb. an optimum peeling angle with a preliminary analysis of the pretension effect, and the tread force distributions are presented. a passive tail transfers the peeling force from the rear wheel to the front and ensures intimate continuous contact with the surface. tankbot works in any orientation on smooth surfaces, such as glass and acrylic, of all slope angles (0 - 360 degrees). however, the robot can only work vertically on relatively rough surfaces in any direction, such as wood, metal, painted wall, and painted brick. tankbot can carry a payload of up to 40 g and 100 g on inverted and vertical surfaces, respectively. in addition, the robot can go over obstacles up to 15 mm tall on smooth vertical surfaces. internal transitioning from horizontal to vertical and vertical to horizontal and external transitioning from vertical to the horizontal are also achieved. the potential applications of this robot include inspection, exploration, maintenance, cleaning, repair, and search and rescue.
sampling-based path planning for geometrically-constrained objects. one of the key factors that affect the success and efficiency of sampling-based path planners is the obtention of samples in the more relevant regions of the workspace. this is known as importance sampling, and different approaches have already been proposed in this direction. this paper proposes a novel method to bias sampling by means of geometric constraints that reduces the sampling space to sets of lower dimensional submanifolds. these constraints may be imposed by the kinematic structure of the actuation system, by the task specification, or provided by a human user as an intuitive way to include problem knowledge to the planner. the method has been implemented and tested on a probabilistic roadmap planner giving promising results. a variant using a deterministic sampling source is also reported.
a switching active sensing strategy to maintain observability for vision-based formation control. vision-based control of a robot formation is challenging because the on-board sensor (camera) only provides the view-angle to the other moving robots, but not the distance that must be estimated. in order to guarantee a consistent estimate of the distance by knowing the control inputs and the sensor outputs in a given interval, the nonlinear multi-robot system must preserve its observability. recent theoretical studies on leader-follower robot formation exploit the interesting influence that the control actions have on observability. based on these results, in this paper we present a switching active control strategy for formation control. our control strategy is active in the sense that, while asymptotically achieving the formation control tasks, it also guarantees the system observability in those cases in which all the robots tend to move along nonobservable paths. as a result, both estimation and formation performances are improved. extensive simulation results show the effectiveness of the proposed design.
task-level imitation learning using variance-based movement optimization. recent advances in the field of humanoid robotics increase the complexity of the tasks that such robots can perform. this makes it increasingly difficult and inconvenient to program these tasks manually. furthermore, humanoid robots, in contrast to industrial robots, should in the distant future behave within a social environment. therefore, it must be possible to extend the robot's abilities in an easy and natural way. to address these requirements, this work investigates the topic of imitation learning of motor skills. the focus lies on providing a humanoid robot with the ability to learn new bimanual tasks through the observation of object trajectories. for this, an imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with gaussian mixture models. the learned information is used to initialize an attractor-based movement generation algorithm that optimizes the reproduced movement towards the fulfillment of additional criteria, such as collision avoidance. experiments performed with the humanoid robot asimo show that the proposed system is suitable for transferring information from a human demonstrator to the robot. these results provide a good starting point for more complex and interactive learning tasks.
graph-based robust shape matching for robotic application. shape is one of the useful information for object detection. the human visual system can often recognize objects based on the 2-d outline shape alone. in this paper, we address the challenging problem of shape matching in the presence of complex background clutter and occlusion. to this end, we propose a graph-based approach for shape matching. unlike prior methods which measure the shape similarity without considering the relation among edge pixels, our approach uses the connectivity of edge pixels by generating a graph. a group of connected edge pixels, which is represented by an "edge" of the graph, is considered together and their similarity cost is defined for the "edge" weight by explicit comparison with the corresponding template part. this approach provides the key advantage of reducing ambiguity even in the presence of background clutter and occlusion. the optimization is performed by means of a graph-based dynamic algorithm. the robustness of our method is demonstrated for several examples including long video sequences. finally, we applied our algorithm to our grasping robot system by providing the object information in the form of prompt hand-drawn templates.
optimal coverage for multiple hovering robots with downward facing cameras. this paper presents a distributed control strategy for deploying hovering robots with multiple downward facing cameras to collectively monitor an environment. information per pixel is proposed as an optimization criterion for multicamera placement problems. this metric is used to derive a specific cost function for multiple downward facing cameras mounted on hovering robot platforms. the cost function leads to a gradient-based distributed controller for positioning the robots. a convergence proof using lasalle's invariance principle is given to show that the robots converge to locally optimal positions. the controller is demonstrated in experiments with three flying quad-rotor robots.
posture control of redundant manipulators on the norm of task space stiffness optimization. this paper investigates the posture of the manipulator that has redundant dof similar to human upper extremity. the human arm naturally takes a posture with no wandering although it has one or two dof redundancy. the authors consider that the posture will be determined in task-oriented, which means that human unconsciously takes a posture of his/her upper extremities, which is suitable for he task that the endpoint is about to do. this study also assumes that all joints are capable to adjust the joint stiffness so that the stiffness of the endpoint in the space is also adjustable. hence our study aims to establish the theoretical method to make the manipulator take a posture that provides a stiffness of the endpoint suitable for the task. the new formula for shaping the manipulator's posture to provide a desired stiffness of the endpoint is presented followed by the similation study to verify it.
multi-robot coordination using generalized social potential fields. we present a novel approach to compute collision-free paths for multiple robots subject to local coordination constraints. more specifically, given a set of robots, their initial and final configurations, and possibly some additional coordination constraints, our goal is to compute a collision-free path between the initial and final configuration that maintains the constraints. to solve this problem, our approach generalizes the social potential field method to be applicable to both convex and nonconvex polyhedra. social potential fields are then integrated into a "physics-based motion planning" framework which uses constrained dynamics to solve the motion planning problem. our approach is able to plan for over 200 robots while averaging about 110 ms per step in a variety of environments.
biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. dynamical systems can generate movement trajectories that are robust against perturbations. this article presents an improved modification of the original dynamic movement primitive (dmp) framework by ijspeert et al [1], [2]. the new equations can generalize movements to new targets without singularities and large accelerations. furthermore, the new equations can represent a movement in 3d task space without depending on the choice of coordinate system (invariance under invertible affine transformations). our modified dmp is motivated from biological data (spinal-cord stimulation in frogs) and human behavioral experiments. we further extend the formalism to obstacle avoidance by exploiting the robustness against perturbations: an additional term is added to the differential equations to make the robot steer around an obstacle. this additional term empirically describes human obstacle avoidance. we demonstrate the feasibility of our approach using the sarcos slave robot arm: after learning a single placing movement, the robot placed a cup between two arbitrarily given positions and avoided approaching obstacles.
global path planning for robust visual servoing in complex environments. we incorporate sampling-based global path planning with visual servoing (vs) for a robotic arm equipped with an in-hand camera. the path planning accounts for a number of constraints: 1) maintaining continuous visibility of the target within the camera's field of view, 2) avoiding visual occlusion of target features caused by the workspace obstacles, robot's body, or the target itself, 3) avoiding collision with physical obstacles or self collision, and 4) joint limits. incorporating these constraints enhances the applicability of vs to significantly more complex environments/tasks, thereby making the resulting vs much more robust. the proposed planner explores the camera space, i.e. 3d cartesian space, for permissible camera paths satisfying the aforementioned constraints by iteratively extending a search tree in camera space and simultaneously tracking these paths in the robot's joint space using a local planner. the planned camera path is then projected into the image space and tracked using an image-based visual servoing scheme. the validity and effectiveness of the proposed approach in accomplishing vs tasks in complex environments are demonstrated through a number of simulations on a 6-dof robot arm moving among obstacles.
interactive segmentation for manipulation in unstructured environments. to perform successful manipulation, robots depend on information about objects in their environment. in unstructured environments, such information cannot be given to the robot a priori. it is thus critical for the robot to be able to continuously acquire task-specific information about objects. towards this goal, we present a robust perceptual skill for identifying, tracking, and segmenting objects in a cluttered environment. we increase the robot's perceptual capabilities by closely coupling them with the robot's manipulation skills. the robot's interaction with objects in the environment creates a perceptual signal, i.e. motion, that renders segmentation and tracking robust and reliable. in addition, the resulting perceptual signal reveals the type of segmentation most relevant to manipulation, namely a segmentation of rigidly connected physical bodies. we demonstrate our approach with experiments on a real world mobile manipulation platform with multiple objects in a cluttered scene.
development of a haptic device "delta-4" using parallel link mechanism. nowadays, several haptic devices are commercialized and becoming common not only in research fields but also in consumer use. in this paper, a new parallel link mechanism "delta-4" is proposed for a new haptic device within high quality force display capability and operability. delta-4 consists of 3 dof of translational motions. the key features of delta-4 comparing with conventional parallel link mechanisms are: a redundant actuation, a wide working area and a small footprint. the prototype is equipped with a 3 dof of rotation mechanism, which its center of motions is located on the wrist position of an operator. an evaluation test of force display was conducted on a prototype of delta-4 mechanism.
object recognition and full pose registration from a single image for robotic manipulation. robust perception is a vital capability for robotic manipulation in unstructured scenes. in this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. in this paper, we present an approach for building metric 3d models of objects using local descriptors from several images. each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3d model and the real object. given a new test image, we match the local descriptors to our stored models online, using a novel combination of the ransac and mean shift algorithms to register multiple instances of each object. a robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. the resulting system provides markerless 6-dof pose estimation for complex objects in cluttered scenes. we provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.
integration of multi-level postural balancing on humanoid robots. this paper discusses an integration issue of multi-level postural balancing on humanoid robot. we give a unified viewpoint of postural balancing, which covers ankle strategy to hip strategy. two kinds of distributor of desired ground reaction force to whole-body joint torque are presented. the one distributor leads to a dynamic balancer which covers hip strategy, with the under-actuated situation. a simple angular momentum regulator is also proposed to stabilize the internal motions due to the joint redundancy. the other distributor leads to a static balancer which lies between ankle and hip strategy. furthermore, this paper demonstrates that replacement of the center of mass feedback with the local joint stiffness makes the robot much stabler for some fast motions. motivated by the practicability of the static balancer and the strong push-recovery performance of the dynamic balancer, this paper presents a simple integration by superposition of the both balancers on a compliant human-sized biped robot. the simulation and experimental videos are supplemented.
a combination of particle filtering and deterministic approaches for multiple kernel tracking. color-based tracking methods have proved to be efficient for their robustness qualities. the drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. this issue can be overcome by using several kernels weighting pixels locations. in this paper a multiple kernels configuration is proposed and developed in both probabilistic and deterministic frameworks. the advantages of both approaches are combined to design a robust tracker allowing to track location, size and orientation of the object. a target tracking scheme using visual servoing considering measurements provided by the presented approach validates the proposed method.
on the kinematic modeling and control of a mobile platform equipped with steering wheels and movable legs. mobile platforms equipped with several steering wheels are known to be omnidirectional, i.e., able to independently translate and rotate on the plane. as an improvement to this design, the justin mobile platform also possesses the ability to vary its footprint over time by extending/retracting the wheel legs during motion. in this paper, we discuss the kinematic modeling and control issues for such a platform. the goal is to obtain a tracking controller which is able to realize an arbitrary linear/angular platform motion while, at the same time, independently expanding/retracting each leg. experimental results support the proposed approach.
visual homing for undulatory robotic locomotion. this paper addresses the problem of vision-based closed-loop control for undulatory robots. we present an image-based visual servoing scheme, which drives the robot to a desired location specified by a target image, without explicitly estimating its pose. instead, the control relies on the computation of the epipolar geometry between the current and target images. we analyze controllability and stability of the proposed control scheme, which is validated by simulation studies using the simuun computational tools. preliminary experiments, involving the nereisbot undulatory robotic prototype, are also presented.
a guidance and control strategy for dynamic soaring with a gliding uav. soaring is the process of gaining energy from the atmosphere in-flight using an aerodynamic free-flying platform. dynamic soaring utilizes the energy available in vertical wind gradients and is commonly used by soaring birds. this research aims to develop a guidance and control strategy to utilize dynamic soaring for a fixed-wing gliding uav. the basic strategies for dynamic soaring in vertical wind shear are explored and a simple piecewise trajectory based controller is developed to identify regions suitable for soaring and attempt traveling energy-neutral trajectories.
gel-type sticky mobile inspector to traverse on the rugged wall and ceiling. an epoch-making handy sized sticky mobile robot has been developed, aiming to search and rescue survivors inside the half collapsed buildings caused by big earthquakes. after the robot is tossed through small spaces, it moves around the rugged or bumpy wall and ceiling to overlook the floor by the installed camera. to generate large enough adhesive force even on the rugged wall, a gel mat made of urethane is introduced, whose force can be recovered by the washing function. developed sticky robot based on the above method could realize the 3d mobile performance on the rugged wall and ceiling with the function of washing of the gel mat and monitoring the scene of the inside of the building. the validity of the application to the rescue inspector is also verified.