waashk/dblp huggingface dataset:
programming-by-demonstration of reaching motions - a next-state-planner approach. this paper presents a novel approach to skill acquisition from human demonstration. a robot manipulator with a morphology which is very different from the human arm simply cannot copy a human motion, but has to execute its own version of the skill. when a skill once has been acquired the robot must also be able to generalize to other similar skills, without a new learning process. by using a motion planner that operates in an object-related world frame called hand-state, we show that this representation simplifies skill reconstruction and preserves the essential parts of the skill.
sensing and gait planning of quadruped walking and climbing robot for traversing in complex environment. in this paper, a general study on improving adaptability of quadruped walking and climbing robot in complex environment is presented. first, a sensing system composed of range and gyroscope sensors in a novel arrangement is developed. by combining the sensing signals and the internal state of the robot, the surface geometry of the environment is sufficiently reconstructed in real-time. secondly, a planning algorithm for the robot to overcome the reconstructed environment is conducted. based on the reshaped surface, the planning algorithm not only provides the exact body trajectory and foot positions but also the adaptability of the robot in a specific environment. a method to improve the adaptability of the walking and climbing robot is also introduced. thanks to the adherent ability of the robot, the center of gravity of the robot is allowed to move outside the support polygon to increase the reach-ability of the next swing leg. finally, the effectiveness of the proposed approach is verified by the performances of the experiments in complex environments using a quadruped walking and climbing robot named mrwallspect iv.
on visual servoing based on efficient second order minimization. this paper deals with the efficient second order minimization (esm) and the image-based visual servoing schemes. in other words, it deals with the minimization based on the pseudo-inverse of the mean of the jacobians or on the mean of jacobian pseudo-inverses. chronologically, it has been noticed in tahri and chaumette (2003) [22] that esm generally improves the system behavior when compared with the system in which only the simple jacobian pseudo-inverses are used. subsequently, a mathematical explanation has been given in malis (2004) [12]. in this paper, the proofs given by malis are discussed and it will be shown that there is a limitation to the validity of the esm. we will also show that the use of esm does not necessarily ensure a better system behavior, especially in the situations where large rotational motions are involved. further, a new appropriate formula of the esm is proposed and validated using several kinds of features.
programmable differential brake for passive haptics. this paper outlines work on a novel programmable braking system, which is widely applicable to most passive haptic applications and benefits from a simple design, theoretically infinite positional resolution and the ability to generate stiff collision forces, without the need for any explicit force measurements. results are also given of a preliminary concept demonstrator which is based on a simple 2-degrees-of-freedom (dof) revolute-revolute (rr) manipulator incorporating the programmable brakes. performance measures for the joint, as well as figures describing the ability of the 2-dof prototype to constrain the end effector motion to a plane and a circle, are also provided.
omnivision-based kld-monte carlo localization. mobile robots operating in real and populated environments usually execute tasks that require accurate knowledge on their position. monte carlo localization (mcl) algorithms have been successfully applied for laser range finders. however, vision-based approaches present several problems with occlusions, real-time operation, and environment modifications. in this article, an omnivision-based mcl algorithm that solves these drawbacks is presented. the algorithm works with a variable number of particles through the use of the kullback-leibler divergence (kld). the measurement model is based on an omnidirectional camera with a fish-eye lens. this model uses a feature-based map of the environment and the feature extraction process makes it robust to occlusions and changes in the environment. moreover, the algorithm is scalable and works in real-time. results on tracking, global localization and kidnapped robot problem show the excellent performance of the localization system in a real environment. in addition, experiments under severe and continuous occlusions reflect the ability of the algorithm to localize the robot in crowded environments.
gait planning based on kinematics for a quadruped gecko model with redundancy. recent research on mobile robots has focused on locomotion in various environments. in this paper, a gait-generation algorithm for a mobile robot that can travel from the ground to a wall and climb vertical surfaces is proposed. the algorithm was inspired by a gecko lizard. our gait planning was based on inverse kinematics using the jacobian of the whole body, where the redundancy was solved by defining an object function for the gecko posture to avoid collisions with the surface. the optimal scalar factor for these two objects was obtained by defining a superior object function to minimize the angular acceleration of joints. the algorithm was verified through simulation of the gecko model travelling on given task paths and avoiding abnormal joint movements and collisions.
reliable and efficient landmark-based localization for mobile robots. this paper describes an efficient and robust localization system for indoor mobile robots and agvs. the system utilizes a sensor that measures bearings to artificial landmarks, and an efficient triangulation method. we present a calibration method for the system components and overcome typical problems for sensors of the mentioned type, which are localization in motion and incorrect identification of landmarks. the resulting localization system was tested on a mobile robot. it consumes less than 4% of a pentium4 3.2 ghz processing power while providing an accurate and reliable localization result every 0.5 s. the system was successfully incorporated within a real mobile robot system which performs many other computational tasks in parallel.
a new strategy combining empirical and analytical approaches for grasping unknown 3d objects. this paper proposes a novel strategy for grasping 3d unknown objects in accordance with their corresponding task. we define the handle or the natural grasping component of an object as the part chosen by humans to pick up this object. when humans reach out to grasp an object, it is generally in the aim of accomplishing a task. thus, the chosen grasp is quite related to the object task. our approach learns to identify object handles by imitating humans. in this paper, a new sufficient condition for computing force-closure grasps on the obtained handle is also proposed. several experiments were conducted to test the ability of the algorithm to generalize to new objects. they also show the adaptability of our strategy to the hand kinematics.
kinematic control of a redundant manipulator using an inverse-forward adaptive scheme with a ksom based hint generator. this paper proposes an online inverse-forward adaptive scheme with a ksom based hint generator for solving the inverse kinematic problem of a redundant manipulator. in this approach, a feed-forward network such as a radial basis function (rbf) network is used to learn the forward kinematic map of the redundant manipulator. this network is inverted using an inverse-forward adaptive scheme until the network inversion solution guides the manipulator end-effector to reach a given target position with a specified accuracy. the positioning accuracy, attainable by a conventional network inversion scheme, depends on the approximation error present in the forward model. but, an accurate forward map would require a very large size of training data as well as network architecture. the proposed inverse-forward adaptive scheme effectively approximates the forward map around the joint angle vector provided by a hint generator. thus the inverse kinematic solution obtained using the network inversion approach can take the end-effector to the target position within any arbitrary accuracy. in order to satisfy the joint angle constraints, it is necessary to provide the network inversion algorithm with an initial hint for the joint angle vector. since a redundant manipulator can reach a given target end-effector position through several joint angle vectors, it is desirable that the hint generator is capable of providing multiple hints. this problem has been addressed by using a kohonen self organizing map based sub-clustering (ksom-sc) network architecture. the redundancy resolution process involves selecting a suitable joint angle configuration based on different task related criteria. the simulations and experiments are carried out on a 7 dof powercube(tm) manipulator. it is shown that one can obtain a positioning accuracy of 1 mm without violating joint angle constraints even when the forward approximation error is as large as 4 cm. an obstacle avoidance problem has also been solved to demonstrate the redundancy resolution process with the proposed scheme.
dynamically balanced optimal gaits of a ditch-crossing biped robot. this paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (dofs). three different approaches, namely analytical, neural network (nn)-based and fuzzy logic (fl)-based, have been developed to solve the said problem. the former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. it is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. a genetic algorithm (ga) is used to provide training off-line, to the nn-based and fl-based gait planners developed. once optimized, the planners will be able to generate the optimal gaits on-line. both the nn-based and fl-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. the nn-based and fl-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
a modified adaptive controller design for teleoperation systems. in this paper, a new adaptive controller is proposed to ensure the stability and good performance of a teleoperation system while a wide range of time delays is considered. for this means, a feedforward compensator is designed to ensure system passivity and then a new model reference adaptive controller (mrac) is developed to provide good performance. the developed system demonstrates good stability and force tracking capabilities. a command generator tracker (cgt) is designed for a sample teleoperation system and the results are compared with the proposed system.
an application of lyapunov stability analysis to improve the performance of narmax models. previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. the proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. during this run, the sensory perception and the desired velocity commands of the robot are logged. having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using armax/narmax models and system identification techniques. these produce linear or non-linear polynomials which can be formally analysed, as well as used in place of ''traditional robot'' control code. in this paper we focus our attention on how the mathematical analysis of narmax models can be used to understand the robot's control actions, to formulate hypotheses and to improve the robot's behaviour. one main objective behind this approach is to avoid trial-and-error refinement of robot code. instead, we seek to obtain a reliable design process, where program design decisions are based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. we demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal.
affective social robots. for human-robot interaction to proceed in a smooth, natural manner, robots must adhere to human social norms. one such human convention is the use of expressive moods and emotions as an integral part of social interaction. such expressions are used to convey messages such as ''i'm happy to see you'' or ''i want to be comforted,'' and people's long-term relationships depend heavily on shared emotional experiences. thus, we have developed an affective model for social robots. this generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot, with a focus on developing long-term human-robot relationships. this paper presents the general affect model as well as particular details of our implementation of the model on one robot, the roboceptionist. in addition, we present findings from two studies that demonstrate the model's potential.
multi-component information-equalized extended strong tracking filter for global localization: a scheme robust to kidnapping and symmetrical environments. this paper proposes a novel multi-component information-equalized extended strong tracking filtering (mist) method for mobile robot global localization. the main contributions of this paper come into three aspects: (1) it is the first time that a strong tracking filter (stf) is introduced into the robotics domain and is extended to be suitable for fusing observations with arbitrarily time-varying dimensionality, based on the proposed extended orthogonality principle and the equivalent space transformation; (2) the information asymmetry problem is analyzed, and an information equalization method based on extracting and equalizing the information underlying the residuals between actual and predicted observations is proposed and integrated with the extended strong tracking filter (estf) so as to make it to be equally sensitive to the saltation and estimation-error in any dimension of the state space; (3) a probabilistic data association mechanism and the dynamic multiple component-filters evolving mechanism are proposed and combined with the information-equalized estf (iestf), which results in the final form of the proposed mist localization method. mist uses multiple individual iestfs (component filters) to track multiple probable pose hypotheses. the number of iestfs is automatically tuned through merging, splitting, deletion and generation, so as to adapt to the time-varying multimodal posterior distribution of the estimated robot's pose. the correctness of each hypothesis (tracked by an individual iestf) is evaluated based on a probabilistic formulation. the effectiveness of the proposed mist method has been validated by real robot experiments and compared with the performances of popular existing methods such as mhl and mcl, which shows that mist has a definite robustness to sensor noises, the kidnapped robot problem, nonlinearity of the system, and symmetric environments, as well as a high convergence speed and computational efficiency.
biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. in this paper a novel stereo correspondence algorithm is presented. it incorporates many biologically and psychologically inspired features to an adaptive weighted sum of absolute differences (sad) framework in order to determine the correct depth of a scene. in addition to ideas already exploited, such as the color information utilization, gestalt laws of proximity and similarity, new ones have been adopted. the presented algorithm introduces the use of circular support regions, the gestalt law of continuity as well as the psychophysically-based logarithmic response law. all the aforementioned perceptual tools act complementarily inside a straightforward computational algorithm applicable to robotic applications. the results of the algorithm have been evaluated and compared to those of similar algorithms.
particle filter based information-theoretic active sensing. this work addresses the task of active sensing, or information-seeking control of mobile sensor platforms. formulation of a control objective in terms of information gain allows mobile sensors to be both autonomous and easily reconfigurable to include a variety of sensor and target models. tracking a moving target using a camera mounted on a fixed-wing unmanned aircraft is considered, but the control formulation is not specific to this choice of sensor or estimation task. a control formulation is developed which minimizes the entropy of an estimate distribution over a receding horizon subject to stochastic non-linear models for both the target motion and sensors. previous similar work has been restricted to either a stationary target, a horizon of length one, or gaussian estimates. the prediction of conditional entropy is shown to be inherently complex, and a computationally efficient sequential monte carlo method is developed. the entropy prediction depends on this monte carlo method as well as a novel approach for entropy calculation in the context of particle filtering. these methods are verified through simulation and post-processing of experimental flight data.
energy constrained multi-robot sensor-based coverage path planning using capacitated arc routing approach. multi-robot sensor-based coverage path planning requires every point given in the workspace has to be covered at least by a sensor of a robot in the robot team. in this study, a novel algorithm was proposed for the sensor-based coverage of narrow environments by considering energy capacities of the robots. for this purpose, the environment was modeled by a generalized voronoi diagram-based graph to guarantee complete sensor-based coverage. then, depending on the required arc set, a complete coverage route was created by using the chinese postman problem or the rural postman problem, and this route was partitioned among robots by considering energy capacities. route partitioning was realized by modifying the ulusoy partitioning algorithm which has polynomial complexity. this modification handles two different energy consumptions of mobile robots during sensor-based coverage, which was not considered before. the developed algorithm was coded in c++ and implemented on p3-dx mobile robots both in laboratory and in mobilesim simulation environments. it was shown that the convenient routes for energy constrained multi-robots could be generated by using the proposed algorithm in less than 1 s.
bridging the gap between feature- and grid-based slam. one important design decision for the development of autonomously navigating mobile robots is the choice of the representation of the environment. this includes the question of which type of features should be used, or whether a dense representation such as occupancy grid maps is more appropriate. in this paper, we present an approach which performs slam using multiple representations of the environment simultaneously. it uses reinforcement to learn when to switch to an alternative representation method depending on the current observation. this allows the robot to update its pose and map estimate based on the representation that models the surrounding of the robot in the best way. the approach has been implemented on a real robot and evaluated in scenarios, in which a robot has to navigate in- and outdoors and therefore switches between a landmark-based representation and a dense grid map. in practical experiments, we demonstrate that our approach allows a robot to robustly map environments which cannot be adequately modeled by either of the individual representations.
clustering and line detection in laser range measurements. this article presents two algorithms that extract information from laser range data. they are designed to work sequentially. the first method (dcc) separates the data into clusters by means of a convolution operation, using a high-pass filter. the second one (reholt) performs line detection in each of the clusters previously discovered. the reliability of the algorithms devised is tested on the experimental data collected both indoors and outdoors. when compared with other methods found in the literature, the ones proposed here prove to achieve higher performance.
learning grasping points with shape context. this paper presents work on vision based robotic grasping. the proposed method adopts a learning framework where prototypical grasping points are learnt from several examples and then used on novel objects. for representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labelled synthetic images. we evaluate and compare the performance of linear and non-linear classifiers. our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.
swarm-supported outdoor localization with sparse visual data. the localization of mobile systems with video data is a challenging field in robotic vision research. apart from support technologies like a gps, a self-sufficient visual system is desirable. we introduce a new heuristic approach to outdoor localization in a scenario with sparse visual data and without odometry readings. localization is interpreted as an optimization problem, and a swarm-based optimization method is adapted and applied, remaining independent of the specific visual feature type. the new method obtains similar or better localization results in our experiments while requiring only two-thirds of the number of image comparisons, indicating an overall speed-up by 25%.
a mathematical model, implementation and study of a swarm system. the work reported in this paper is motivated towards the development of a mathematical model for swarm systems based on macroscopic primitives. a pattern formation and transformation model is proposed. the pattern transformation model comprises two general methods for pattern transformation, namely a macroscopic transformation method and a mathematical transformation method. the problem of transformation is formally expressed and four special cases of transformation are considered. simulations to confirm the feasibility of the proposed models and transformation methods are presented. comparison between the two transformation methods is also reported.