Genetic programming for articulated figure motion

We present an approach to articulated figure motion in which motion tasks are defined in terms of goals and ratings. The agents are dynamically-controlled robots whose behaviour is determined by robotic controller programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI technique of genetic programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically plausible and aesthetically appealing motion can be generated using these methods.     

SAM-animation software for simulating articulated motion

A collection of software used for interactive specification, motion control and graphics simulation of articulated objects of arbitrary complexity is described. While used primarily for simulating and evaluating robotic manipulators, it has also been applied to the animation of biological models. One of the key issues discussed involves a flexible and intuitive approach to the motion specification for that large class of objects which possess redundant degrees of freedom.     

An articulated limb motion planner for optimized movement

Task level animation of articulated figures, such as the human body, requires the ability to generate collision-free goal-directed motion of individual limbs in the presence of obstacles. This paper describes a new articulated limb motion planner for goal-directed point-to-point reaching motions. The produced motion avoids obstacles while optimizing an objective function. This two-phase algorithm uses heuristic guided Monte Carlo techniques to create a consistent underlying paradigm. The first phase consists of an existing potential field based random path planner which generates a population of candidate paths. This initial population is fed into the second phase, a genetic algorithm, which iteratively refines the population as it optimizes with respect to the objective function. The refinement process works on the principle of path coherency, the idea that a family of closely related collision-free paths lies in the vicinity of a given collision-free path. This paper focuses on seven different optimization functions. Optimized trajectories produced by the new motion planner are compared to those generated solely by the random path planner. The presented algorithm is flexible in that a wide range of objective functions can be optimized. Applications of the algorithm include task level animation, ergonomics and robotics.     

Constrained synthesis of textural motion for articulated characters

Obtaining high-quality, realistic motions of articulated characters is both time consuming and expensive, necessitating the development of easy-to-use and effective tools for motion editing and reuse. We propose a new simple technique for generating constrained variations of different lengths from an existing captured or otherwise animated motion. Our technique is applicable to textural motions, such as walking or dancing, where the motion sequence can be decomposed into shorter motion segments without an obvious temporal ordering among them. Inspired by previous work on texture synthesis and video textures, our method essentially produces a reordering of these shorter segments. Discontinuities are eliminated by carefully choosing the transition points and applying local adaptive smoothing in their vicinity, if necessary. The user is able to control the synthesis process by specifying a small number of simple constraints.     

Interpolation synthesis of articulated figure motion

Most conventional media depend on engaging and appealing characters. Empty spaces and buildings would not fare well as television or movie programming, yet virtual reality usually offers up such spaces. The problem lies in the difficulty of creating computer generated characters that display real time, engaging interaction and realistic motion. Articulated figure motion for real time computer graphics offers one solution to this problem. A common approach stores a set of motions and lets you choose one particular motion at a time. The article describes a process that greatly expands the range of possible motions. Mixing motions selected from a database lets you create a new motion to exact specifications. The synthesized motion retains the original motions' subtle qualities, such as the realism of motion capture or the expressive, exaggerated qualities of artistic animation. Our method provides a new way to achieve inverse kinematics capability-for example, placing the hands or feet of an articulated figure in specific positions. It proves useful for both real time graphics and prerendered animation production. The method, called interpolation synthesis, is based on motion capture data and it provides real time character motion for interactive entertainment or avatars in virtual worlds     

Stereo Pictorial Structure for 2D articulated human pose estimation

In this paper, we consider the problem of 2D human pose estimation on stereo image pairs. In particular, we aim at estimating the location, orientation and scale of upper-body parts of people detected in stereo image pairs from realistic stereo videos that can be found in the Internet. To address this task, we propose a novel pictorial structure model to exploit the stereo information included in such stereo image pairs: the Stereo Pictorial Structure (SPS). To validate our proposed model, we contribute a new annotated dataset of stereo image pairs, the Stereo Human Pose Estimation Dataset (SHPED), obtained from YouTube stereoscopic video sequences, depicting people in challenging poses and diverse indoor and outdoor scenarios. The experimental results on SHPED indicates that SPS improves on state-of-the-art monocular models thanks to the appropriate use of the stereo information.     

Estimating pose of articulated objects using low-level motion

In this work a method is presented to track and estimate pose of articulated objects using the motion of a sparse set of moving features. This is achieved by using a bottom-up generative approach based on the Pictorial Structures representation [1]. However, unlike previous approaches that rely on appearance, our method is entirely dependent on motion. Initial low-level part detection is based on how a region moves as opposed to its appearance. This work is best described as Pictorial Structures using motion. A standard feature tracker is used to automatically extract a sparse set of features. These features typically contain many tracking errors, however, the presented approach is able to overcome both this and their sparsity. The proposed method is applied to two problems: 2D pose estimation of articulated objects walking side onto the camera and 3D pose estimation of humans walking and jogging at arbitrary orientations to the camera. In each domain quantitative results are reported that improve on state of the art. The motivation of this work is to illustrate the information present in low-level motion that can be exploited for the task of pose estimation.     

Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic textures and local geometric information better than local TV. But, since nonlocal TV requires weighted difference between pixels in the whole image, it demands much more computational resources than local TV. By using the linearization of the fidelity term and the proximal function, our proposed algorithm does not require any inversion of blurring operator and nonlocal operator. Therefore, the proposed algorithm is very efficient for motion deblurring problems. We compare the numerical performance of our proposed algorithm with that of several state-of-the-art algorithms for deblurring problems. Our numerical results show that the proposed method is faster and more robust than state-of-the-art algorithms on motion deblurring problems.     

3D pose estimation for articulated vehicles using Kalman-filter based tracking

Knowledge about relative poses within a tractor/trailer combination is a vital prerequisite for kinematic modelling and trajectory estimation. In case of autonomous vehicles or driver assistance systems, for example, the monitoring of an attached passive trailer is crucial for operational safety. We propose a camerabased 3D pose estimation system based on a Kalman-filter. It is evaluated against previously published methods for the same problem.     

Analyzing and capturing articulated hand motion in image sequences

Capturing the human hand motion from video involves the estimation of the rigid global hand pose as well as the nonrigid finger articulation. The complexity induced by the high degrees of freedom of the articulated hand challenges many visual tracking techniques. For example, the particle filtering technique is plagued by the demanding requirement of a huge number of particles and the phenomenon of particle degeneracy. This paper presents a novel approach to tracking the articulated hand in video by learning and integrating natural hand motion priors. To cope with the finger articulation, this paper proposes a powerful sequential Monte Carlo tracking algorithm based on importance sampling techniques, where the importance function is based on an initial manifold model of the articulation configuration space learned from motion-captured data. In addition, this paper presents a divide-and-conquer strategy that decouples the hand poses and finger articulations and integrates them in an iterative framework to reduce the complexity of the problem. Our experiments show that this approach is effective and efficient for tracking the articulated hand. This approach can be extended to track other articulated targets.     

Robotic capability determined from planes of motion

A consequence of the specification of robots using kinematically simple joints is a simplified kinematic analysis. The analysis is performed here in terms of points and planes; i.e., the point/plane method. It is shown that the capability of a robot is completely specified by its planes of motion; the logical position space of a robot is defined in terms of these planes, and capability is presented in tables.     

Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains

A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility.     

Geometry-based flipper motion planning for articulated tracked robots traversing rough terrain in real-time

Tracked robots operating on rough terrain are often equipped with controllable flippers to help themselves overcome large obstacles or gaps. How to automate the control of these auxiliary flippers to achieve autonomous traversal remains an open question, which still necessitates inefficient manual teleoperation in practice. To tackle this problem, this article presents a geometry-based motion planning method for an articulated tracked robot to self-control its flippers during autonomous or semiautonomous traversal over rough terrain in urban search and rescue environments. The proposed method is developed by combining dynamic programming with a novel geometry-based pose prediction method of high computational efficiency, which is applicable for typical challenging rescue terrains, such as stairs, Stepfields, and rails. The efficient pose prediction method allows us to make thousands of predictions about the robot poses at future locations for given flipper configurations within the onboard sensor range. On the basis of such predictions, our method evaluates the entire discretized configuration space and thereby determines the optimal flipper motion online for a smooth traversal over the terrain. The overall planning algorithm is tested with both simulated and real-world robots and compared with a reinforcement-learning-based method using the RoboCup Rescue Robot League standard testing scenarios. The experimental results show that our method enables the robots to automatically control the flippers, successfully go over challenging terrains, and outperform the baseline method in passing smoothness and robustness to different terrains.     

Collision-free motion of an articulated kinematic chain in adynamic environment

A method for the collision avoidance of an articulated kinematic chain in a dynamic environment is presented. The inverse kinematic solution is used to get the configuration of the chain for a given position of any part of the chain. The calculation of collision-free motion is based on the local information about the motion. Intermediate positions are used to get the new motion path of the chain. The movement of environmental objects during the collision detection and the generation of new motions are considered. This generalizes the method for arbitrarily configured kinematic chains and for a dynamic environment. Its implementation for the motion planning of robot manipulators in an offline programming system is briefly described     

结合两步估算和运动补偿的子像素运动估计

针对图像超分辨中的运动估计问题,提出了一种两步估计方法,用于估计低分辨图像帧间的子像素相对运动。第一步,采用相关法或匹配法计算两幅低分辨率图像间的整像素相对位移,对其中的一幅按估计的参数进行运动补偿,第二步,对补偿后的两幅图像使用梯度法计算小数像素相对位移。通过两步计算,得到了比较精确的帧间相对运动参数。该方法不需要对低分辨率图像进行插值,以获得图像的高分辨近似,运算速度也较快。     

基于模型估计的关节式月球车参数化轨迹规划*

为了在轨迹规划阶段提高月球车在三维地形中的轨迹规划精度,以被动关节式地形自适应月球车为研究对象,融合关节机器人D-H坐标建模方法构建月球车悬架运动学模型,结合数值求解方法,推导了任意崎岖三维地形中月球车姿态估计模型.在模型估计基础上利用参数化控制原理,建立了满足约束条件下被动关节式月球车在任意地形中的基于模型估计的一般性参数化轨迹生成模型.针对轮式月球车的非完整性特点,结合数值求解方法,推导了非线性模型的求解方法.最后利用仿真方法,以八轮摇杆摇臂关节式月球车为例,验证了崎岖地形中基于模型估计的轨迹生成方法的正确性,且可提高关节式月球车在崎岖地形中的规划精度.     

一种双模式的运动估计算法

为了平衡运动估计中搜索算法的复杂度与搜索精度,基于双模式算法的思想,提出一种结合基于改进的粒子群算法(PSO)和十字搜索算法(ARPS)的双模式运动搜索算法.该算法对不同运动程度的图像采用不同的运动搜索算法(运动剧烈时采用PSO算法,运动平缓时采用ARPS算法),有效地结合了PSO的全局性特点以及ARPS的局部性特点,同时保持了ARPS的快速性.实验表明,该算法的整体性能高于传统的单模式运动估计算法以及已有的多模式运动估计算法.