waashk/dblp huggingface dataset:
a probabilistic model for the performance analysis of a distributed task allocation algorithm. in this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. in this case, we move a step forward and calculate the variance of the global cost. this second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. the normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. this approximation enables us to compare our algorithm theoretically in different cases.
qualitative robot localisation using information from cast shadows. recently, cognitive psychologists and others have turned their attention to the formerly neglected study of shadows, and the information they purvey. these studies show that the human perceptual system values information from shadows very highly, particularly in the perception of depth, even to the detriment of other cues. however with a few notable exceptions, computer vision systems have treated shadows not as signal but as noise. this paper makes a step towards redressing this imbalance by considering the formal representation of shadows. we take one particular aspect of reasoning about shadows, developing the idea that shadows carry information about a fragment of the viewpoint of the light source. we start from the observation that the region on which the shadow is cast is occluded by the caster with respect to the light source and build a qualitative theory about shadows using a region-based spatial formalism about occlusion. using this spatial formalism and a machine vision system we are able to draw simple conclusions about domain objects and egolocation for a mobile robot.
speed-accuracy optimization for skill learning. robot motor capability is crucial for skill learning because it determines how accurately and rapidly a robot can perform a skill to accomplish a task constrained by the spatial and temporal conditions. due to the capability of robot motor, a robot may not be able to perform skills to satisfy both task spatial and temporal constraints. to determine the robot motor capability, this paper formulates the skill for accomplishing a task as an optimization problem subject to the spatial and temporal conditions of the task, and proposes to develop a speed-accuracy constraint suitable for the optimization problem. the proposed speed-accuracy constraint is derived from the kinematics, dynamics, and control of a robot motor system. by solving the optimization problem, a robot can perform the skill to achieve a task by its fastest speed without violating the spatial and temporal conditions of the task. computer simulations were performed on a puma 560 robot to demonstrate a skill on a typical task to validate the proposed speed-accuracy optimization.
probabilistic situation recognition for vehicular traffic scenarios. to act intelligently in dynamic environments, a system must understand the current situation it is involved in at any given time. this requires dealing with temporal context, handling multiple and ambiguous interpretations, and accounting for various sources of uncertainty. in this paper we propose a probabilistic approach to modeling and recognizing situations. we define a situation as a distribution over sequences of states that have some meaningful interpretation. each situation is characterized by an individual hidden markov model that describes the corresponding distribution. in particular, we consider typical traffic scenarios and describe how our framework can be used to model and track different situations while they are evolving. the approach was evaluated experimentally in vehicular traffic scenarios using real and simulated data. the results show that our system is able to recognize and track multiple situation instances in parallel and make sensible decisions between competing hypotheses. additionally, we show that our models can be used for predicting the position of the tracked vehicles.
rough terrain walking for bipedal robot by using zmp criteria map. a new method for bipedal walking on rough terrain by using zmp criteria map is proposed. the rough terrain walking is classified to "step up" and "step down" by landing timing of a swing leg. the walking pattern is modified in real-time according to the difference between the ideal timing and the measured timing by a force sensor on the foot. in the case of "step up", the landing timing is faster than the ideal, and the swing leg trajectory should be change to follow the step so that the zmp based balance of the sudden caused double support phase is kept. in the case of "step down", the landing timing is later than the ideal, and the swing leg trajectory should be change to seek the ground so that the balance of the unknown future double support phase is kept. the modified walking pattern is decided based on the zmp criteria map. the zmp criteria map can indicate a safe landing timing and landing position of a swing leg. by referring the zmp criteria map, the robust walking pattern can be planned. the proposed method is implemented to hrp-2, and the effectiveness is confirmed through experiments.
ad-hoc wireless network coverage with networked robots that cannot localize. we study a fully distributed, reactive algorithm for deployment and maintenance of a mobile communication backbone that provides an area around a network gateway with wireless network access for higher-level agents. possible applications of such a network are distributed sensor networks as well as communication support for disaster or military operations. the algorithm has minimalist requirements on the individual robotic node and does not require any localization. this makes the proposed solution suitable for deployment of large numbers of comparably cheap mobile communication nodes and as a backup solution for more capable systems in gps-denied environments. robots keep exploring the configuration space by random walk and stop only if their current location satisfies user-specified constraints on connectivity (number of neighbors). resulting deployments are robust and convergence is analyzed using both kinematic simulation with a simplified collision and communication model as well as a probabilistic macroscopic model. the approach is validated on a team of 9 irobot create robots carrying wireless access points in an indoor environment.
randomized model predictive control for robot navigation. the paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control lyapunov function. in this approach, the nonlinear optimal control problem is treated using randomized algorithms. the advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an mpc framework, is that the control design offers stability by design, is platform independent, and allows the designer to trade-off performance for (computation) speed, according to the application requirements.
survivability: measuring and ensuring path diversity. a novel criterion is introduced for assessing the diversity of a collection of paths or trajectories. the main idea is the notion of survivability, which measures the likelihood that numerous paths are obstructed by the same obstacle. this helps to improve robustness with respect to collision, which is an important challenge in the design of real-time planning algorithms. efficient algorithms are presented for computing the survivability criterion and for selecting a subset of paths that optimize survivability from a larger collection. the algorithms are implemented and solutions are illustrated for two different systems. chi-square tests are used to show uniform coverage obtained by using the computed paths in a simple breadth-first search. random obstacle placement is used to show superior robustness of these primitives compared to uniform sampling of the control space.
a variable stiffness joint using leaf springs for robot manipulators. safety of a manipulator designed to be used at home requires different approach than industrial robots, where safety is achieved mainly by decreasing the interaction with humans. robots for applications at home, however, require frequent interaction with humans. introducing compliant component gives the answer to the safety issue at the cost of performance degradation. in order to reduce the performance degradation, manipulators equipped with variable stiffness have been studied by many researchers. this paper presents a variable stiffness joint(vsj) designed for a robot manipulator. the stiffness is generated by leaf springs and two actuators are used to control the position and stiffness of the joint. changing the effective length of the spring results in change in stiffness. the position of the joint is controlled via rotating two actuators at the same speed in the same direction. the stiffness is controlled when the two actuators rotate in the different speed. experiments are conducted to show that the position and stiffness are controlled independent with each other and having less stiffness at the joint helps in making unexpected collision with object safer.
perception-centric force scaling function for stable bilateral interaction. in this paper a force scaling function for an haptic system is the output of the psychophysics experiments that have been carried out with the aim of better understanding the human perception capabilities. the experimental work consists in measuring the differential thresholds of force perception applied to the hand-arm system. these findings support our claim that the human perception of forces and torques depends on force intensity and works differently along different directions, thus suggesting that perception can be enhanced by suitable scaling. we have identified a scaling function for each direction and we have shown that this variable scalings can be safely embedded in a passivity based teleoperation system in order to improve the feeling perceived by the user during the interaction with remote environments.
revisiting uncertainty analysis for optimum planes extracted from 3d range sensor point-clouds. in this work, we utilize a recently studied more accurate range noise model for 3d sensors to derive from scratch the expressions for the optimum plane which best fits a point-cloud and for the combined covariance matrix of the plane's parameters. the parameters in question are the plane's normal and its distance to the origin. the range standard-deviation model used by us is a quadratic function of the true range and is a function of the incidence angle as well. we show that for this model, the maximum-likelihood plane is biased, whereas the least-squares plane is not. the plane-parameters' covariance matrix for the least-squares plane is shown to possess a number of desirable properties, e.g., the optimal solution forms its null-space and its components are functions of easily understood terms like the planar-patch's center and scatter. we verify our covariance expression with that obtained by the eigenvector perturbation method. we further compare our method to that of renormalization with respect to the theoretically best covariance matrix in simulation. the application of our approach to real-time range-image registration and plane fusion is shown by an example using a commercially available 3d range sensor. results show that our method has good accuracy, is fast to compute, and is easy to interpret intuitively.
deformation modeling of belt object with angles. a differential geometry based modeling to represent belt object deformation is proposed. deformation of a belt object such as film circuit boards or flexible circuit boards must be estimated for automatic manipulation and assembly. first, the fishbone model to describe deformation of a rectangular belt object is explained. in this model, the object shape is represented by the curved "spine line" and straight "rib lines". we can estimate deformation of the object by minimizing its potential energy under geometric constraints. next, this model is modified to represent deformation in which the rib line at an endpoint does not coincide with the transverse edge. moreover, the modified model is applied to a belt object with angles. the deformed shape of an angled object can be derived by separating it into rectangular parts and angled parts and by assuming that each angled part forms a part of a cylindrical surface. finally, the validity of our proposed model is verified by comparing the computed shape of an l-shaped belt object with its measured shape.
dynamics and control of a 4-dof wearable cable-driven upper arm exoskeleton. in this paper, we present the dynamics, control, and preliminary experiments on a wearable upper arm exoskeleton intended for human users with four degrees-of-freedom (dof), driven by six cables. the control of this cable-driven exoskeleton is complicated because the cables can transmit forces to the arm only under tension. the standard pd controllers or computed torque controllers perform only moderately since the cables need to be in tension. future efforts will seek to refine these control strategies and their implementations to improve functionality of a human user.
monte carlo simultaneous localization of multiple unknown transient radio sources using a mobile robot with a directional antenna. we report our system and algorithm developments that enable a single mobile robot equipped with a directional antenna to simultaneously localize multiple unknown transient radio sources. due to signal source anonymity, short transmission durations, and dynamic transmission patterns, the robot cannot treat the radio sources as continuous radio beacons. we model the radio source behaviors using a novel spatiotemporal probability occupancy grid (spog) that captures transient characteristics of radio transmissions and tracks the spatiotemporal posterior probability distribution of the radio transmissions. as a monte carlo method, we propose a ridge walking motion planning algorithm that enables the robot to efficiently traverse the high probability regions to accelerate the convergence of the posterior probability distribution. we have implemented the algorithms and the experiment results show that our method consistently outperforms methods such as a random walk or a fixed-route patrol mechanism.
development of backdrivable hydraulic joint mechanism for knee joint of humanoid robots. robots must have similar mechanical impedance characteristics to humans in order to make safe and efficient contact. this impedance requirement applies not only to the surface but also to the actuation mechanisms. the objective of this research is to develop inherently flexible actuator by realizing backdrivability. a class of hydraulic actuation called electro-hydrostatic actuator was applied to knee joint in humanoid robots to satisfy flexibility and large torque output simultaneously. this paper explains the methodology of performance evaluation of actuators and design concept of joint mechanism. mathematical model of electro-hydrostatic transmission is also presented. evaluation of backdrivability, inertia modification control, and compliance control of developed mechanism are performed.
hsm3d: feature-less global 6dof scan-matching in the hough/radon domain. this paper presents hsm3d, an algorithm for global rigid 6dof alignment of 3d point clouds. the algorithm works by projecting the two input sets into the radon/hough domain, whose properties allow to decompose the 6dof search into a series of fast one-dimensional cross-correlations. no planes or other particular features must be present in the input data, and the algorithm is provably complete in the case of noise-free input. the algorithm has been experimentally validated on publicly available data sets.
basic study of biarticular muscle's effect on muscular internal force control based on physiological hypotheses. in a musculoskeletal structure, the internal force among muscles plays an important role. changing the internal force enables to control not only joint angles but also impedance, so that vertebrate animals can produce a motion according to a situation. focusing on a musculoskeletal system with two links and six muscles, this paper investigate the effect of biarticular muscles when feedforward position control is inputted. this control gives the constant internal force balancing at desired posture as feedforward input, based on the ep hypothesis in physiology. from the result, we point out that the biarticular muscles can reduce the convergent time of the motion, and they also can stabilize the system.
milligram-scale high-voltage power electronics for piezoelectric microrobots. piezoelectric actuators can achieve high efficiency and power density in very small geometries, which shows promise for microrobotic applications, such as flapping-wing robotic insects. from the perspective of power electronics, such actuators present two challenges: high operating voltages, ranging from tens to thousands of volts, and a low electromechanical coupling factor, which necessitates the recovery of unused electrical energy. this paper explores the power electronics design problem by establishing the drive requirements of piezoelectric actuators, presenting circuit topologies and control methods suitable for driving different types of piezoelectric actuators in microrobotic applications, and demonstrating experimental realizations of sub-100mg power electronics circuits.
automatic deployment of autonomous cars in a robotic urban-like environment (rule). we present a computational framework and experimental setup for deployment of autonomous cars in a miniature robotic urban-like environment (rule). the specifications are given in rich, human-like language as temporal logic statements about roads, intersections, and parking spaces. we use transition systems to model the motion and sensing capabilities of the robots and the topology of the environment and use tools resembling model checking to generate robot control strategies and to verify the correctness of the solution. the experimental setup is based on khepera iii robots, which move autonomously on streets while observing traffic rules.
on achievable accuracy for pose tracking. this paper presents cramér-rao bound-like inequalities for pose tracking, which is defined as the problem of recovering the robot displacement given two successive readings of a relative sensor. computing the exact fisher information matrix (fim) for pose tracking is hard, because the state comprises the map, which is infinite-dimensional and unknown. this paper shows that the fim for pose tracking can be bounded by a function of the fim for localization on a known map, thereby reducing the analysis to a finite-dimensional problem. the resulting bounds are independent of the map prior and representation. the results are valid for any relative sensor; the experimental verification is done for the particular case of pose tracking using range-finders (scan matching).
search-based planning for a legged robot over rough terrain. we present a search-based planning approach for controlling a quadrupedal robot over rough terrain. given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. we decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. we show how r* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.
on autonomous detection of pressured air and gas leaks using passive ir-thermography for mobile robot application. today, pressured air and gas leaks are typically detected using in-situ sensor technology. in this contribution the application of passive ir-thermography is proposed to permit remote leak detection by assessing the resulting temperature profile disturbance due to expansion of pressured gas. remote measurements are advantageous as they are easier and safer to conduct. scanning high-rise objects can be achieved using a ground-bound system without requiring complex climbing. in the paper a novel method for automated leak detection by feature extraction and pattern recognition is presented. this enables autonomous mobile robots with remote leak detection capability.
range estimation from a moving camera: an immersion and invariance approach. the paper proposes an original solution to the range identification problem for perspective dynamical systems. the depth of a static point observed by a pinhole camera undergoing a predefined 3-d motion, is estimated from its 2-d projection on the image plane. the proposed nonlinear observer relies on the immersion and invariance (i&i) methodology and offers several advantages over the existing range estimators. the paper also provides an analytical study of nonlinear observability performed with the extended output jacobian. extensive simulation experiments illustrate the theory and show the effectiveness of the proposed design.
adaptive expert systems for indirect coverage control. herds of livestock, when left to their own devices, will forage for food in predictable patterns, and will often overgraze preferred areas while leaving other areas untouched. the field of grazing management looks to improve the efficiency of land use by moving the animals through different pastures at regular intervals, akin to coverage algorithms used in robotics but with non-uniform coverage and additional constraints due to the animals' natural behaviors. the knowledge of the field of grazing management is largely in the hands of ranchers and other domain experts, and as such has not been the subject of much computational study. in this work, we have created novel learning techniques for expert systems that use both off-line and on-line learning to generate efficient performance. the rules of the expert systems are initially developed using an evolutionary algorithm, and after deployment, an adaptive algorithm tracks the state of the system and updates rule weights to improve both coverage efficiency and animal stress levels. we present a series of results based on increasingly complex versions of simulated herd models that show improvements over unconstrained motion in each case, and suggest how the algorithm can apply to robotic coverage problems.
vision-based detection of finger touch for haptic device using transparent flexible sheet. a haptic device using transparent flexible sheet, which is an improvement of our previous one, presents visual and haptic sensations when a user pushes a virtual soft object. it consists of a transparent flexible sheet and a computer display; the display is placed behind the sheet. the user feels the softness of the object by pushing the sheet, and sees the stereoscopic cg image of the deformed object through the sheet. in the present study we propose a new method of detecting finger touch on the sheet and measuring the indentation of the contact point; the method is specialized for this haptic device. a camera is placed behind the sheet. a checkered pattern is reflected on the backside of the sheet like a two-way mirror. the camera monitors this reflected pattern. when the finger pushes the sheet, the sheet is deformed. then the reflected pattern near the fingertip, seen from the camera, is distorted. monitoring this distortion makes it possible to detect finger touch on the sheet. as the indentation of the contact point increases, the reflected pattern is more distorted. thus measuring the amount of the distortion allows the measurement of the indentation. the proposed method is ascertained by some experiments.