waashk/dblp huggingface dataset:
kinematic analysis and optimal design of a 3t1r type parallel mechanism. in previous studies on 4-dof parallel mechanisms with four sub-chains, only symmetric arrangement of those four chains connected to the top plate was considered. such symmetric shape sometimes falls into a sort of architetural singularity. this work demonstrates that an asymmetric placement of the four chains on the top platform is desired to minimize the effect of such architectural singularity. a new 4-dof parallel mechanism exhibiting 4-dof motion (3-dof translational motion and one rotational motion) is examined as an exemplary device. this device consists of a base plate, an upper plate, and four hybrid subchains connecting those two plates together. the position analysis and kinematic modeling for this mechanism are performed, and an optimal design with respect to the workspace size and the kinematic isotropic characteristic is conducted. a set of offset angles on the top platform is found, which significantly minimizes the architectural singularity problem within the valid workspace of the mechanism. then, through the analysis of the workspace and kinematic isotropic property for the optimized mechanism, its high potential for real applications is confirmed. finally, the mechanism was developed to verify the motion capability.
backbone-based connectivity control for mobile networks. while a network of autonomous mobile agents is capable of performing spatially distributed tasks, communication between agents imposes a class of constraints over the corresponding task. this paper proposes a distributed paradigm to deal with a critical communication constraint, connectedness constraint, where a group of mobile agents are required to remain connected while performing a task (e.g., formation control, consensus, etc.). the proposed method adaptively extracts communication backbone of the group, which is formed by a subset of agents, and thus partitions the group into backbone agents and non-backbone agents. the connectedness of the system is maintained at two levels: motion of backbone agents is controlled to maintain existing connections in the backbone; motion of non-backbone agents is determined via a leader-follower formation control method with backbone agents as the leaders. key advantages of the proposed approach are that it can deal with arbitrary system topologies, it is a distributed method, it uses only two-hop neighbor information, and has low communication cost.
robot basketball: a comparison of ball dribbling with visual and force/torque feedback. ball dribbling is a central element of basketball and a main challenge for creating basketball robots is to achieve stability of the periodic dribbling task. in this paper two control designs for ball dribbling with an industrial robot are compared. for the two strategies, the ball position is determined either through force/torque or visual sensor feedback and the ball trajectory is predicted with a recursive least squares algorithm. the end effector trajectory for each dribbling cycle is generated based on the predicted ball position/velocity at the dribbling height and the estimated coefficient of restitution. for both tracking approaches, dribbling for multiple cycles is achieved. the vision-based approach performs better as compared to the force/torque-based approach, in particular for imprecise estimates of the coefficient of restitution.
hybrid design for multiple-goal task realization of robot arm with rotating table. the minimization of task completion time of robot arms has been an extensively studied area in robotics. previous researches mostly focused on optimization methods for the motion planning and collision avoidance, which did not involve any modifications in the hardware design of a robot arm. some researches, on the other hand, fully design a specialized robot arm for a given task. in this study, we propose a hybrid design composed of a hardware design and an optimization method. the hardware design is a tool attachment, which is a fixed linkage attached between the end-effector of a robot arm and a tool. in the optimization method, we incorporate base placement design, goal rearrangement and collision avoidance through motion coordination in order to minimize the task completion time of a robot arm. our proposed design is tested using a 6-dof robot arm and a 1-dof rotating table. the method is evaluated over a single task and a set of tasks showing its effectiveness and applicability for practical applications.
on fast surface reconstruction methods for large and noisy point clouds. in this paper we present a method for fast surface reconstruction from large noisy datasets. given an unorganized 3d point cloud, our algorithm recreates the underlying surface's geometrical properties using data resampling and a robust triangulation algorithm in near realtime. for resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. the proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. to validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments.
impass: intelligent mobility platform with active spoke system. impass (intelligent mobility platform with active spoke system) is a novel mobile robot driven by two rimless spoke wheels. each of the spokes can be individually actuated with intelligent motion planning to walk over uneven terrain with high mobility. this form of novel locomotion has the potential to combine the efficiency of a wheeled robot and the mobility of a legged robot. a highly mobile robot such as impass could prove very valuable in applications where the terrain is complex and dangerous, such as search and rescue, reconnaissance, or anti-terror response. this video presents an overview of the system concept with examples and demonstrations of its unique mobility. beginning with an overview of the system concept and hardware, the video then demonstrates the different mobility advantages. these include rough terrain locomotion, dynamic surface locomotion, and large step climbing.
modeling and control of a pair of robot fingers with saddle joint under orderless actuations. a new robot hand dynamics model with rolling constraints and with a saddle joint at one finger is proposed, where two saddle-joint actuations are considered to be orderless. spinning motion around the opposition axis connecting two center points of each finger-tip contact area with an object is faithfully treated, and a viscosity model for damping rotational motion of the object is proposed. a class of control signals without referring to object kinematics or using external sensing is proposed. finally, numerical simulation results show the stability of motion of the overall closed-loop dynamics supplied with the proposed control input.
on the potential of physics-based animation for task programming in virtual reality. physics-based animation is becoming an essential feature for any advanced simulation software. in this paper we explore potential benefits of physics-based modeling for task programming in virtual reality. firstly, we show how realistic animation of manipulation tasks can be exploited for learning sequential constraints from user demonstrations. in particular, we propose a method where information about physical interaction is used to discover task precedences and to reason about task similarities at the goal level. a second contribution of the paper is the application of physics-based modeling to the problem of disassembly sequence planning. experiments have been performed in a desktop virtual reality environment with dataglove and motion tracker.
a study on sinus-lifting motion of a snake robot with switching constraints. in this paper, we consider "sinus-lifting motion" which is the effective motion of a snake for rapid movement consisting of lifting some parts of its body off the ground, and switching the lifted and grounded parts dynamically. this motion is analyzed based on control theory. we focus on the constraint force of passive wheels and switching of grounded parts of the snake robot to minimize the constraint force. simulations show that the motion that minimizes the constraint force of the snake robot is similar to the sinus-lifting motion in appropriate amplitudes and periods of the body shape.
producing rigid contacts in cable-driven haptic interfaces using impact generating reels. this paper presents a design for a cable reel that allows a cable-driven haptic interface to produce rigid impacts with virtual objects in a virtual reality settings. the haptic interface studied in this article has three degrees of freedom (3 dof) and acts as a sword-fighting simulator. in order to obtain sharp impacts with this interface, an impact generating reel is proposed to transmit forces across the cables to a user holding the end-effector (sword). a prototype is presented in order to demonstrate the concept. as a method of quantifying the credibility of these impacts, an accelerometer was mounted on the end-effector, where the cables are attached in order to measure the vibrations caused by these impacts. these vibrations are compared with the vibrations caused by an impact with a rigid material such as steel in order to classify the stiffness of the impacts generated by the mechanism
learning and generalization of motor skills by learning from demonstration. we provide a general approach for learning robotic motor skills from human demonstration. to represent an observed movement, a non-linear differential equation is learned such that it reproduces this movement. based on this representation, we build a library of movements by labeling each recorded movement according to task and context (e.g., grasping, placing, and releasing). our differential equation is formulated such that generalization can be achieved simply by adapting a start and a goal parameter in the equation to the desired position values of a movement. for object manipulation, we present how our framework extends to the control of gripper orientation and finger position. the feasibility of our approach is demonstrated in simulation as well as on the sarcos dextrous robot arm. the robot learned a pick-and-place operation and a water-serving task and could generalize these tasks to novel situations.
moving obstacle detection in highly dynamic scenes. we address the problem of vision-based multiperson tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. specifically, we are interested in the application of such a system for supporting path planning algorithms in the avoidance of dynamic obstacles. the complexity of the problem calls for an integrated solution, which extracts as much visual information as possible and combines it through cognitive feedback. we propose such an approach, which jointly estimates camera position, stereo depth, object detections, and trajectories based only on visual information. the interplay between these components is represented in a graphical model. for each frame, we first estimate the ground surface together with a set of object detections. based on these results, we then address object interactions and estimate trajectories. finally, we employ the tracking results to predict future motion for dynamic objects and fuse this information with a static occupancy map estimated from dense stereo. the approach is experimentally evaluated on several long and challenging video sequences from busy inner-city locations recorded with different mobile setups. the results show that the proposed integration makes stable tracking and motion prediction possible, and thereby enables path planning in complex and highly dynamic scenes.
a protocol for decentralized multi-vehicle mapping with limited communication connectivity. this paper addresses the problem of communication range limitations for decentralized multi-vehicle mapping. we present a novel integrated communication and planning protocol that enables all vehicles to form a common global map. the fusion of mapping information is facilitated through the information filter and performed over a connected acyclic wireless communication network with limited communication range. the formation of the acyclic connected communication network is achieved by partitioning the landmark graph using graph theoretic tools during the planning phase. we provide results that illustrate the effectiveness of our approach over different distributions of landmarks.
the null-space based behavioral control for non-holonomic mobile robots with actuators velocity saturation. this paper presents the application of the null-space based behavioral (nsb) approach to the motion control of a non-holonomic mobile robot with velocity saturated actuators. in particular, the proposed solution aims at managing actuator velocity saturations by dynamically scaling task velocity commands so that the hierarchy of task priorities is preserved in spite of actuator velocity saturations. the approach is tested on a specific case study where the nsb approach elaborates the motion directives for a mobile robot that has to reach a target while avoiding a punctual obstacle. the approach is validated by numerical simulations and by experimental results with a non-holonomic mobile robot.
decentralized localization for dynamic and sparse robot networks. finite-range sensing and communication are factors in the connectivity of a dynamic mobile robot network. state estimation becomes a difficult problem when communication connections for information exchange between all robots are not guaranteed. this paper presents a decentralized state estimation algorithm guaranteed to work in dynamic networks without connectivity requirements. we show that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible, while ensuring the same can be performed by all other robots in the network. our technique is validated through simulations.
tokyo-tech 100 n hand : three-fingered eight-dof hand with a force-magnification mechanism. this paper presents tokyo-tech 100 n hand, a three-fingered eight-degree of freedom hand that can generate a large grasp force of 100n. all fingers are 90 mm in length and the weight of the hand is 941 g. the hand can generate a large grasp force with a force-magnification mechanism whereas all joints can perform a high speed motion of over 200 deg/s with a high-speed driving mechanism. the high-speed driving mechanism consists of a feed screw and nut with a low reduction ratio. we propose a simple and small three-dimensional toggle mechanism for the force-magnification mechanism. the toggle mechanism thrusts the feed screw and nut of the high-speed driving mechanism when the finger is in contact with an object. it can maintain a large grasp force without energy consumption. we experimentally verify the force and speed performances.
moving object classification using horizontal laser scan data. motivated by two potential applications, i.e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. this research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. in this research, a number of features are defined after examining the property of data appearance. a classification method is proposed after examining the likelihood measures between each pair of feature and class. experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.
plc-based control of a robot manipulator with closed kinematic chain. the paper describes the design and implementation of a robot control system on a hardware platform based on a programmable logic controller (plc). the controlled robot is a 4 degrees of freedom (dof) manipulator with a closed kinematic chain, designed for high-performance pick and place applications in a packaging workcell. the control software is fully developed on a commercial plc system, using its standard programming tools and the multi-tasking features of its operating system. in particular, the paper analyse in detail the drawbacks and the advantages related to the choice of standard plcs in this kind of applications, compared to the much common choice of specialized hardware or industrial personal computers, with particular emphasis on the computational performances obtained with the proposed control architecture.
markerless human motion tracking with a flexible model and appearance learning. a new approach to the 3d human motion tracking problem is proposed, which combines several particle filters with a physical simulation of a flexible body model. the flexible body model allows the partitioning of the state space of the human model into much smaller subsets, while finding a solution considering all the partial results of the particle filters. the flexible model also creates the necessary interaction between the different particle filters and allows effective semi-hierarchical tracking of the human body. the physical simulation does not require inverse kinematics calculations and is hence fast and easy to implement. furthermore the system also builds an appearance model on-the-fly which allows it to work without a foreground segmentation. the system is able to start tracking automatically with a convenient initialization procedure. the implementation runs with 10 hz on a regular pc using a stereo camera and is hence suitable for human-robot interaction applications.
smoothed sarsa: reinforcement learning for robot delivery tasks. our goal in this work is to make high level decisions for mobile robots. in particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. we introduce a novel reinforcement learning algorithm called smoothed sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. the state space is modeled by a dynamic bayesian network and updated using a region-based particle filter. we take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. smoothed sarsa learns a policy orders of magnitude faster than previous policy search algorithms. we demonstrate our results on the player/stage simulator and on the pioneer robot.
a nonlinear terrain-following controller for a vtol unmanned aerial vehicle using translational optical flow. this paper presents a nonlinear controller for terrain following of a vertical take-off and landing vehicle (vtol). the vtol vehicle is assumed to be a rigid body, equipped with a minimum sensor suite (camera and imu) along with a measure of the forward speed from another sensor such as global positioning system, maneuvering over a textured terrain made of planar surfaces. assuming that the forward velocity is separately set to a desired value, the proposed control approach ensures terrain following and guaranties the vehicle does not collide with the ground during the task. the proposed control acquires an optical flow from three spatially separate observation points, typically obtained via three cameras or three non collinear directions in a unique camera. the proposed control algorithm has been tested extensively in simulation and then implemented on a quadrotor uav to demonstrate the performance of the closed loop system.
vision-based leader-follower formations with limited information. this paper presents a new vision-based leader-follower formation algorithm where the leader's trajectory is unknown to the robots which are following. formation schemes in straight lines and diagonal formations are introduced which are both stable and observable in the presence of limited views. the algorithms are novel since they only use local image measurements through a pinhole camera to estimate the leader's position. this approach does not require specialized markings nor extensive robot communications. the algorithms are also decentralized. we apply an input-output feedback linearization for system stability and utilize an extended kalman filter (ekf) for estimation. simulations illustrate how the proposed formation controls work. real experiments utilizing multiple miniature robots are also presented and illustrate the challenges associated with noisy images in real-world applications.
high performance anthropomorphic robot hand with grasp force magnification mechanism. this paper presents a lightweight 328 g anthropomorphic robot hand that can exert a large grasp force. we propose a combination mechanism of a flexion-drive and a force-magnification-drive for a cable driven multifingered robot hand. the flexion-drive consisting of a feed screw enables quick motion of its fingers and the force-magnification-drive consisting of an eccentric cam, a bearing and a pulley enables a firm grasp. this paper also proposes a three-dimensional linkage for the thumb. this linkage consists of four links and is driven by a feed screw. it can oppose to a large force exerted by the other fingers with the force-magnification-drive. these mechanisms are compact enough to be installed in the developed lightweight hand. we experimentally verify that the maximum fingertip force of the hand exceeds 20 n and that the thumb can hold a large force of 100 n. the time to fully close the hand by using the flexion-drives is 0.47 s. after the fingers make contact with an object, the time to achieve a firm grasp by using the grasp-magnification-drive is approximately 1 s.
multiple-model ransac for ego-motion estimation in highly dynamic environments. robust ego-motion estimation in urban environments is a key prerequisite for making a robot truly autonomous, but is not easily achievable as there are two motions involved: the motions of moving objects and the motion of the robot itself. we proposed a random sample consensus (ransac) based ego-motion estimator to deal with highly dynamic environments using one planar laser scanner. instead of directly sampling on individual measurements, the ransac process is performed at a higher level abstraction for systematic sampling and computational efficiency. we proposed a multiple-model approach to solve the problems of ego-motion estimation and moving object detection jointly in a ransac paradigm. to accommodate ransac to multiple models - a static environment model for ego-motion estimation and a moving object model for moving object detection, a compact representation models moving object information implicitly is proposed. moving objects are successfully detected without incorporating any grid maps, that are inherently time and space consuming. the experimental results show that accurate identification of static environments can help classification of moving objects, whereas discrimination of moving objects also yields better ego-motion estimation, particularly in environments containing a significant percentage of moving objects.