An embedding approach for the design of state‐feedback tracking controllers for references with jumps

We study the problem of designing state‐feedback controllers to track time‐varying state trajectories that may exhibit jumps. Both plants and controllers considered are modeled as hybrid dynamical systems, which are systems with both continuous and discrete dynamics, given in terms of a flow set, a flow map, a jump set, and a jump map. Using recently developed tools for the study of stability in hybrid systems, we recast the tracking problem as the task of asymptotically stabilizing a set, the tracking set, and derive conditions for the design of state‐feedback tracking controllers with the property that the jump times of the plant coincide with those of the given reference trajectories. The resulting tracking controllers guarantee that solutions of the plant starting close to the reference trajectory stay close to it and that the difference between each solution of the controlled plant and the reference trajectory converges to zero asymptotically. Constructive conditions for tracking control design in terms of LMIs are proposed for a class of hybrid systems with linear maps and input‐triggered jumps. The results are illustrated by various examples. Copyright © 2013 John Wiley & Sons, Ltd.     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.     

Moving Personal Robots in Real-Time Using Primitive Motions

Personal robotics is a new and attractive use of robotic technologies. In this paper we study one of its important topics—real-time motion planning of personal robots. We propose to use primitive motions and their combinations to make this possible. The primitive motion has a unified pattern and is borrowed from the motion pattern of human hands observed by previous researchers. The pattern is simple yet powerful and can form complex trajectories. A reflexive motion control scheme is proposed to activate appropriate primitive motions for a desired motion. As a result, real-time motion planning of personal robots becomes possible by using discrete and sparse human commands. Experiments results are presented to show the effectiveness of the proposed scheme.     

Interactive motion mapping for real‐time character control

It is now possible to capture the 3D motion of the human body on consumer hardware and to puppet in real time skeleton‐based virtual characters. However, many characters do not have humanoid skeletons. Characters such as spiders and caterpillars do not have boned skeletons at all, and these characters have very different shapes and motions. In general, character control under arbitrary shape and motion transformations is unsolved ‐ how might these motions be mapped? We control characters with a method which avoids the rigging‐skinning pipeline — source and target characters do not have skeletons or rigs. We use interactively‐defined sparse pose correspondences to learn a mapping between arbitrary 3D point source sequences and mesh target sequences. Then, we puppet the target character in real time. We demonstrate the versatility of our method through results on diverse virtual characters with different input motion controllers. Our method provides a fast, flexible, and intuitive interface for arbitrary motion mapping which provides new ways to control characters for real‐time animation.     

Precise piston trajectory control for a free piston engine

A free piston engine removes the mechanical constraint on the piston motion by eliminating the crankshaft. The extra degree of freedom offers many advantages for reducing fuel consumption and emissions. Nevertheless, stability and robustness of the engine operation has been affected in the meantime. To ensure smooth engine operation, an active motion controller, which utilizes robust repetitive control, was developed previously to regulate the piston motion of a hydraulic free piston engine to track pre-defined trajectories. However, the long piston stroke length, high operating frequency and system nonlinearity impose challenges to precise piston motion control. Therefore, feedforward controllers are investigated in this paper to complement the repetitive control to further improve the tracking performance. The first feedforward design involves the inversion of a linear plant model that describes the dynamics of the engine operation, and the second design is based on the flatness approach, which involves the inversion of a nonlinear model of the system. The two feedforward controllers are designed and implemented on the free piston engine. The experimental and simulation results demonstrate the effectiveness of the proposed control under various operating conditions and reference piston trajectories.     

A Minimum-Jerk Speed-Planning Algorithm for Coordinated Planning and Control of Automated Assembly Manufacturing

We previously proposed a general algorithm for coordinating the motions among multiple machines in a shared assembly environment based on a constant-speed motion model. In this paper, we extend this work to a minimum-jerk polynomial motion model and describe a new speed-planning algorithm to plan automated assembly machines' motions. Machines are planned sequentially, based on their priorities, by mapping the motions of higher-priority machines into forbidden regions in two-dimensional space-time graphs. Collision-free minimum-jerk motions are then planned between the forbidden regions in the graphs. The new speed-planning algorithm is evaluated on a dual-robot surface-mount technology assembly machine in which both robots share a common workspace. Note to Practitioners—Automated assembly processes, especially surface-mount technology manufacturing, require a high degree of precision when placing certain components. This motivated us to find a way of maintaining good positional accuracy by planning smooth motions for the machines that perform these tasks. Since many of these machines have two or more robots, their motions must also be coordinated. We developed an algorithm that combines coordinated motion concepts with a minimum-jerk motion model that can solve these problems. The algorithm plans segmented paths for the robots and then sequentially plans their speeds to prevent collisions between them. The planned speeds ensure position, velocity, and acceleration continuity between path segments. The smooth motions resulting from this method enable high-accuracy component placement. The tradeoff for this improvement is increased cycle time compared to other speed-planning methods.     

Design of an Optimal Unknown Input Observer for Load Compensation in Motion Systems

Recently, several model‐based control designs have been proposed for motion systems and computerized numerical control (CNC) machines to improve motion accuracy. However, in real applications, their performance is seriously degraded when significant disturbances or cutting forces are applied. In this paper, we derive straightforward design procedures for a general‐structured unknown input observer (UIO) which perfectly decouples the effect of the external disturbance from the state estimation. Furthermore, we derive the optimal UIO by minimizing the estimation errors for both the state and the disturbance via the Riccati equation. Experimental results show that the performance of alladvanced motion controllers suffers when external loads are applied. By compensating for the disturbance of a servo motor using the proposed optimal‐UIO, the original contouring accuracy, which is degraded by the external loading, can be successfully recovered.     

Automatic motion generation exploiting the global structure of nonlinear dynamics based on finite-time reachability

In this paper, we propose a novel approach for motion generation in robotic systems based on the notion of finite-time reachability. The key idea is to segment the state space in terms of the reachability of particular target regions in finite time. The advantage of this approach is that precisely controlled joint trajectories are not necessary (as is often the case in traditional robotics). Complex movements can be generated by defining a hierarchy of controllers responsible for reaching multiple transitional regions toward the target region. Our method can simultaneously generate movements and the control for nonlinear robot systems. This method generates efficient motions exploiting the intrinsic characteristics of the mechanism and the environment. We present experimental results for two tasks: a pendulum swing-up and balancing problem and a bipedal walking problem.     

Skiing Simulation Based on Skill‐Guided Motion Planning

Skiing is a popular recreational sport, and competitive skiing has been events at the Winter Olympic Games. Due to its wide moving range in the outdoor environment, motion capture of skiing is hard and usually not a good solution for generating skiing animations. Physical simulation offers a more viable alternative. However, skiing simulation is challenging as skiing involves many complicated motor skills and physics, such as balance keeping, movement coordination, articulated body dynamics and ski‐snow reaction. In particular, as no reference motions — usually from MOCAP data — are readily available for guiding the high‐level motor control, we need to synthesize plausible reference motions additionally. To solve this problem, sports techniques are exploited for reference motion planning. We propose a physics‐based framework that employs kinetic analyses of skiing techniques and the ski–snow contact model to generate realistic skiing motions. By simulating the inclination, angulation and weighting/unweighting techniques, stable and plausible carving turns and bump skiing animations can be generated. We evaluate our framework by demonstrating various skiing motions with different speeds, curvature radii and bump sizes. Our results show that employing the sports techniques used by athletes can provide considerable potential to generate agile sport motions without reference motions.     

Reconstructing whole-body motions with wrist trajectories

Reconstructing whole-body motions using only a low-dimensional input reduces the cost of and efforts for performance capture significantly, and yet remains a challenging problem. We introduce a novel technique that synthesizes whole-body motion using the two wrist trajectories. Given the wrist trajectories, we first determine the optimal ankle trajectories from a large number of candidate ankle paths obtained from example poses in the motion database. The optimal trajectory is efficiently achieved by solving for the shortest path problem in a directed acyclic graph. Next, we use both the wrist and ankle trajectories as the low-dimensional control signals to achieve the whole-body pose at each time step. We show that our method can reconstruct various whole-body motions that can be recognized by arm motions, such as walking, stepping, and in-place upper-body motions. Comparisons with ground truth motions and with other methods are provided.     

Fragment-based responsive character motion for interactive games

Fragment-based character animation has become popular in recent years. By stringing appropriate motion capture fragments together, the system drives characters responding to the control signals of the user and generates realistic character motions. In this paper, we propose a novel, straightforward and fast method to build the control policy table, which selects the next motion fragment to play based on the current user’s input and the previous motion fragment. During the synthesis of the control policy table, we cluster similar fragments together to create several fragment classes. Dynamic programming is employed to generate the training samples based on the control signals of the user. Finally, we use a supervised learning routine to create the tabular control policy. We demonstrate the efficacy of our method by comparing the motions generated by our controller to the optimal controller and other previous controllers. The results indicate that although a reinforcement learning algorithm known as value iteration also creates the tabular control policy, it is more complex and requires more expensive space–time cost in synthesis of the control policy table. Our approach is simple but efficient, and is practical for interactive character games.     

双足机器人跨越动态障碍物在线控制系统设计

在双足机器人跨越动态障碍物的在线控制问题中,脚步规划和步态控制的学习时间是关键问题;提出了一种将机器人的步态控制和脚步规划分别独立设计的控制策略;步态控制目的是产生关节点轨迹并控制对理想轨迹的跟踪,考虑到双足机器人关节点轨迹的不连续性,应用小脑模型连接控制CMAC记忆特征步态的关节点轨迹;脚步规划的控制目标是通过对环境的视觉感知预测机器人的运动路径,算法是基于无需对动态环境精确建模的模糊Q学习算法;仿真结果表明该控制策略的可行性,并且可以有效缩短在线学习时间。     

Adaptive Motion/Force Tracking Control for a Class of Mobile Manipulators

In this paper, modeling and adaptive motion/force tracking control is considered for a class of mobile manipulators under the holonomic and affine constraints with the presence of uncertainties and disturbances. Based on a suitable reduced dynamic model, adaptive controllers are proposed to ensure that the states of a closed‐loop system asymptotically track desired trajectories while the constraint force remains bounded by tuning design parameters. Detailed simulation results confirm the effectiveness of the control strategy.     

Adaptive control strategies for cooperative dual-arm manipulators

The article describes three strategies for adaptive control of cooperative dual-arm robots. In the position-position control strategy, the adaptive controllers ensure that the end-effector positions of both arms track desired trajectories in Cartesian space despite unknown time-varying interaction forces exerted through the load. In the position-hybrid control strategy, the adaptive controller of one arm controls end-effector motions in the free directions and applied forces in the constraint directions; while the adaptive controller of the other arm ensures that the end-effector tracks desired position trajectories. In the hybrid-hybrid control strategy, the adaptive controllers ensure that both end-effectors track reference position trajectories while simultaneously applying desired forces on the load. In all three control strategies, the coupling effects between the arms through the load are treated as “disturbances” which are rejected by the adaptive controllers while following desired commands in a common frame of reference. The adaptive controllers do not require the complex mathematical model of the arm dynamics or any knowledge of the arm dynamic parameters or the load parameters such as mass and stiffness. The controllers have simple structures and are computationally fast for on-line implementation with high sampling rates. Simulation results are given to illustrate the proposed adaptive control strategies.     

Simulation of Human Motion Data using Short-Horizon Model-Predictive Control

Many data‐driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model‐predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low‐gain proportional‐derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.     

Path planning directed motion control of virtual humans in complex environments

Natural motion synthesis of virtual humans have been studied extensively, however, motion control of virtual characters actively responding to complex dynamic environments is still a challenging task in computer animation. It is a labor and cost intensive animator-driven work to create realistic human motions of character animations in a dynamically varying environment in movies, television and video games. To solve this problem, in this paper we propose a novel approach of motion synthesis that applies the optimal path planning to direct motion synthesis for generating realistic character motions in response to complex dynamic environment. In our framework, SIPP (Safe Interval Path Planning) search is implemented to plan a globally optimal path in complex dynamic environments. Three types of control anchors to motion synthesis are for the first time defined and extracted on the obtained planning path, including turning anchors, height anchors and time anchors. Directed by these control anchors, highly interactive motions of virtual character are synthesized by motion field which produces a wide variety of natural motions and has high control agility to handle complex dynamic environments. Experimental results have proven that our framework is capable of synthesizing motions of virtual humans naturally adapted to the complex dynamic environments which guarantee both the optimal path and the realistic motion simultaneously.     

Pose Controlled Physically Based Motion

In this paper we describe a new method for generating and controlling physically‐based motion of complex articulated characters. Our goal is to create motion from scratch, where the animator provides a small amount of input and gets in return a highly detailed and physically plausible motion. Our method relieves the animator from the burden of enforcing physical plausibility, but at the same time provides full control over the internal DOFs of the articulated character via a familiar interface. Control over the global DOFs is also provided by supporting kinematic constraints. Unconstrained portions of the motion are generated in real time, since the character is driven by joint torques generated by simple feedback controllers. Although kinematic constraints are satisfied using an iterative search (shooting), this process is typically inexpensive, since it only adjusts a few DOFs at a few time instances. The low expense of the optimization, combined with the ability to generate unconstrained motions in real time yields an efficient and practical tool, which is particularly attractive for high inertia motions with a relatively small number of kinematic constraints.     

Human motion retrieval using topic model

Content‐based human motion retrieval is important for animators with the development of motion editing and synthesis, which need to search similar motions in large databases. Obtaining text‐based representation from quantization of mocap data turned out to be efficient. It becomes a fundamental step of many researches in human motion analysis. Geometric features are one of these techniques, which involve much prior knowledge and reduce data redundancy of numerical data. We describe geometric features as basic unit to define human motions (also called mo‐words) and view a human motion as a generative process. Therefore, we obtain topic motions, which possess more semantic information using latent Dirichlet allocation by learning from massive training examples in order to understand motions better. We combine probabilistic model with human motion retrieval and come up with a new representation of human motions and a new retrieval framework. Our experiments demonstrate its advantages, both for understanding motions and retrieval. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling,Identification, and Control of an Unmanned Surface Vehicle

This paper describes planar motion modeling for an unmanned surface vehicle (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planing conditions. The modeling and identification objective is to determine a model that is sufficiently rich to enable effective model‐based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hullforms and actuator configurations. We focus, however, on a specific platform: a modified rigid hull inflatable boat with automated throttle and steering. Analysis of experimental results for this vessel indicates that Nomoto's first‐order steering model provides the best compromise between simplicity and fidelity at higher speeds. At low speeds, it is helpful to include a first‐order lag model for sideslip. Accordingly, we adopt a multiple model approach in which the model structure and parameter values are scheduled based on the nominal forward speed. The speed‐scheduled planar motion model may be used to generate dynamically feasible trajectories and to develop trajectory tracking control laws. The paper describes the development, analysis, and experimental implementation of two trajectory tracking control algorithms: a cascade of proportional‐derivative controllers and a nonlinear controller obtained through backstepping. Experimental results indicate that the backstepping controller is much more effective at tracking trajectories with highly variable speed and course angle. © 2013 Wiley Periodicals, Inc.     

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

This paper presents a technical overview of Team DRC‐Hubo@UNLV's approach to the 2015 DARPA Robotics Challenge Finals (DRC‐Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC‐Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel‐driven navigation, zero‐moment‐point (ZMP) ‐based locomotion, and position‐based manipulation and controls, are described in this paper. By introducing strategically critical elements and key lessons learned from DRC‐Trials 2013 and the testbed of Charleston, we also illustrate how DRC‐Hubo has evolved successfully toward the DRC‐Finals.