Models of computation for reactive control of autonomous mobile robots

The paper studies computation models for tasks performed by autonomous mobile robots. Such tasks can be accomplished by reactive control algorithms. Reactive control systems can be described using different models of computation which have as distinguishing feature the abstraction level of time. Thus, three computation models are defined: the untimed model, the synchronous model and the timed model. It is shown that the clocked-synchronous model of computation is more appropriate for describing the controller for a parallel parking task.     

Application of systems identification to the implementation of motion camouflage in mobile robots

Motion camouflage is an animal stealth behaviour in which a shadower—or predator—moves in the vicinity of a shadowee—or prey—in such a way that the later perceives no apparent motion apart from the self motion. Despite some light has been shed on the control mechanism generating this pursuit strategy, it is not fully understood. Motion camouflage represents an interesting challenge in biological motion, and although simulated controllers can be found in the literature, no implementation on real robots has been done so far. This paper presents the first implementation of motion camouflage in real wheeled robots through a polynomial NARMAX model controller. The trajectories to adjust the model are generated using a heuristic approach. The NARMAX model outperforms the heuristic approach in terms of computational time and generates good camouflage trajectories in real robots and simulation. The transparency of polynomial models can also shed some light over this complex animal behaviour.     

Digital twin-based industrial cloud robotics: Framework,control approach and implementation

Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.     

分布式多机器人运动控制的离散事件系统方法

传统多机器人系统的运动控制主要依赖于机器人的动力学方程或运动学方程,通过求解微分方程组来获得机器人的输入控制信号.随着系统中机器人数量的增加和运行环境的复杂化,动力学方程很难描述多机器人系统的运动行为,且无法很好地解决诸如死锁等逻辑故障.本文简略综述了国内外的研究现状,重点介绍笔者所在研究组开展的关于离散事件系统方法在多机器人运动控制方面的应用性研究工作.其动机在于:1)基于离散事件系统方法的运动控制能够有效地解决系统运行过程中产生的诸如死锁等逻辑故障.首先,利用离散事件系统模型对多机器人系统的运动进行建模,从而降低计算复杂性;其次,基于所得离散事件系统模型,设计分布式安全运动控制算法,使各个机器人可以自主地、无碰撞地、无死锁地运动;设计分布式鲁棒运动控制算法,使得失效的机器人对系统的影响最小.2)基于离散事件系统方法的运动控制策略可以结合传统的基于运动学方程的运动控制方法,从而使系统不但能够避免顶层的逻辑故障,而且能够确定机器人执行器的输入信号.     

Artificial biochemical networks: a different connectionist paradigm

Connectionist models are usually based on artificial neural networks. However, there is another route towards parallel distributed processing. This is by considering the origins of the intelligence displayed by the single celled organisms known as protoctists. Such intelligence arises by means of the biochemical interactions within the animal. An artificial model of this might therefore be termed an artificial biochemical network or ABN. This paper describes the attributes of such networks and illustrates their abilities in pattern recognition problems and in generating time-varying signals of a type which can be used in many control tasks. The flexibility of the system is explained using legged robots as an example. The networks are trained using back propagation and evolutionary algorithms such as genetic algorithms.     

Software toolkit for modeling,simulation, and control of soft robots

The technological differences between traditional robotics and soft robotics have an impact on all of the modeling tools generally in use, including direct kinematics and inverse models, Jacobians, and dynamics. Due to the lack of precise modeling and control methods for soft robots, the promising concepts of using such design for complex applications (medicine, assistance, domestic robotics, etc.) cannot be practically implemented. This paper presents a first unified software framework dedicated to modeling, simulation, and control of soft robots. The framework relies on continuum mechanics for modeling the robotic parts and boundary conditions like actuators and contacts using a unified representation based on Lagrange multipliers. It enables the digital robot to be simulated in its environment using a direct model. The model can also be inverted online using an optimization-based method which allows to control the physical robots in the task space. To demonstrate the effectiveness of the approach, we present various soft robots scenarios including ones where the robot is interacting with its environment. The software has been built on top of SOFA, an open-source framework for deformable online simulation and is available at https://project.inria.fr/softrobot/.     

未知环境中群机器人队形移动

群机器人学是多机器人系统的一个重要研究方向,其主要特点是系统包含大量个体.如何控制不受数量限制的群机器人形成队形并在未知环境中移动,是一个极具挑战性的问题.基于人工物理原理分析了群机器人网格队形分布和移动控制原理,制定了详细的系统性能评价指标(稳定时间、碰撞次数、覆盖率、连接数、簇数、分布密度等参数),提出了采用曲线拟合法构建虚拟力模型.通过在有障碍物环境中的仿真,显示出该方法可以鲁棒、有效的完成网格分布和移动任务.最后指出了今后的研究方向.     

Scalable formation control in stealth with limited sensing range

In this paper, a protocol and a control law are designed for a single robot so that a team of such robots can interact and cooperate to reach the displacements from an eligible reference formation. Each robot is equipped with displacement sensors of limited sensing ranges. Communication channels are assumed to be unavailable to the team, and each robot works in stealth mode. The team is scalable such that new robots can be recruited, and existing robots can be dismissed. In order for the team size to be scalable, the extended formation based on relative displacement is established as the reference formation. Thus, using the extended formation as a reference, the control law and the protocol could be flexible. As potential conflicts deflect the robot team from the desired formation, the control law is designed to expose the conflicts to the involved neighboring robots such that the protocol can resolve them. A numerical example is given to illustrate how an extended formation is designed, and a simulation example is conducted to demonstrate the performance and merits of the proposed techniques. Copyright © 2016 John Wiley & Sons, Ltd.     

多机器人路径规划的安全性验证

近些年来,伴随着人工智能领域的浪潮,机器人越来越多的出现在我们的日常生活中,例如足球机器人、无人机、无人车等.如何保证这些自治机器人尤其是多个机器人在移动过程中的安全成了人们一直很关心的问题.混成通信顺序进程（Hybrid Communicating Sequential Process,HCSP）是一个针对混成系统的形式化建模语言,在通信顺序进程（Communicating Sequential Process,CSP）的基础上引入了微分方程以描述混成系统中的连续行为和控制逻辑,可以方便高效地对大型控制系统尤其是在有通信事件发生时的情形进行形式化建模.本文就是用HCSP建模多机器人的路径控制算法,并用定理证明工具HProver进行形式化验证.结果证明了在满足一定初始条件下,机器人团队在整个运行途中不会发生碰撞.     

Adaptive Distributed Fault-tolerant Formation Control for Multi-robot Systems under Partial Loss of Actuator Effectiveness

This paper presents an adaptive distributed fault-tolerant formation control for multi-robot systems. Both the kinematics and dynamics of differential wheeled mobile robots are considered. In particular, the problem caused by actuator faults is investigated. Based on dynamic surface control techniques, adaptive formation controllers can be obtained under a directed communication network. The closed-loop stability is guaranteed by using Lyapunov stability analysis such that all followers can exponentially converge to a leader-follower formation pattern. Simulation and experimental results illustrate that the desired formation pattern can be preserved for a group of wheeled robots subject to unknown uncertainties and actuator faults.     

Deployment of mobile robots with energy and timing constraints

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.     

基于个性和OCC的机器人情感建模研究

机器人不仅要具有简单的机械作业和逻辑推理能力，还应当具有类似人类的情感能力．本文将个性与情绪、情感、理解、表达相结合，采用OCC模型作为评价标准，建立了符合人类情感规律的、可用于情感机器人的情感模型。通过一个应用上述模型的虚拟人情感交互系统．验证了此模型可以很好的对人类的情感进行仿真．可以应用于情感机器人和人性化计算机、游戏等许多领域。     

Active Vibration Control of Flexible Robots Using Virtual Spring-damper Systems

When using robots for heavy loads and huge operating ranges, elastic deformations of the links have to be taken into account during modeling and controller design. Whereas for conventional rigid multilink industrial robots modeling can schematically be done by standard techniques, it is a massive problem to obtain an accurate analytic model for multilink flexible robots. But an accurate analytic model is essential for most modern controller design techniques, and modeling errors can lead to instability of the controlled system due to spillover since the eigenvalues of the system are only slightly damped. A new approach to active damping control for flexible robots is presented in this paper where the actuators act like virtual spring-damper-systems. As the spring-damper-element is a passive energy dissipative device, it will never destabilize the system and thus the control concept will be very insensitive to modeling errors. Basically, the two parameters, spring stiffness and damping constant of this system, are arbitrary and model independent. To satisfy performance requirements they are adjusted using knowledge of the system model. The more it is known about the system model, the better these parameters may be adjusted. The new input of the controlled system is a virtual variation of the spring base. The paper illustrates this technique with the help of a simple and easy to model one link flexible robot which is also available as a real laboratory testbed.     

Screw-based kinematic modeling and geometric analysis of planar mobile robots

Typical mobile robots can be modeled as parallel mechanisms by employing an interfacing variable between the ground and the wheels. Based on this conception, the screw theory was employed in the following work as an alternative approach in the modeling of such parallel-natured mobile robots. This theory allows for a geometric analysis of mobile mechanisms. As a result, the computational load in the derivation of a Jacobian model can be reduced, the kinematic model for different sets of inputs can be easily obtained, and an equivalent serial-chain model can be analyzed. Two mobile robots were examined as exemplary models. The proposed approach can also be applied to kinematic modeling and the analysis of general types of mobile robots.     

Neural networks-based adaptive bidding with the contract net protocol in multi-robot systems

This paper investigates the effectiveness of using the Contract Net Protocol, an auction type system, for controlling task allocation among a group of robots, and presents and evaluates a strategy of using Artificial Neural Networks to formulate adaptive bids within the framework of the Contract Net Protocol. The robots were used in a foraging environment and showed that excellent communication among robots leads to a need for a social control mechanism for managing the robots, such as the Contract Net Protocol. The experiments also confirmed that a moderate benefit can be gained by using adaptive bidding within the framework of the Contract Net Protocol.     

Learning intentions for improved human motion prediction

For many tasks robots need to operate in human populated environments. Human motion prediction is gaining importance since this helps minimizing the hinder robots cause during the execution of these tasks. The concept of social forces defines virtual repelling and attracting forces from and to obstacles and points of interest. These social forces can be used to model typical human movements given an environment and a person’s intention. This work shows how such models can exploit typical motion patterns summarized by growing hidden Markov models (GHMMs) that can be learned from data online and without human intervention. An extensive series of experiments shows that exploiting a person’s intended position estimated using a GHMM within a social forces based motion model yields a significant performance gain in comparison with the standard constant velocity-based models.     

Design and control of tensegrity robots for locomotion

The static properties of tensegrity structures have been widely appreciated in civil engineering as the basis of extremely lightweight yet strong mechanical structures. However, the dynamic properties and their potential utility in the design of robots have been relatively unexplored. This paper introduces robots based on tensegrity structures, which demonstrate that the dynamics of such structures can be utilized for locomotion. Two tensegrity robots are presented: TR3, based on a triangular tensegrity prism with three struts, and TR4, based on a quadrilateral tensegrity prism with four struts. For each of these robots, simulation models are designed, and automatic design of controllers for forward locomotion are performed in simulation using evolutionary algorithms. The evolved controllers are shown to be able to produce static and dynamic gaits in both robots. A real-world tensegrity robot is then developed based on one of the simulation models as a proof of concept. The results demonstrate that tensegrity structures can provide the basis for lightweight, strong, and fault-tolerant robots with a potential for a variety of locomotor gaits.     

Using simplified geometric models in skill-based manipulation

Dexterous manipulation plays an important role in working robots. Manipulator tasks such as assembly and disassembly can generally be divided into several motion primitives. We call these 'skills' and explain how most manipulator tasks can be composed of skill sequences. Skills are also used to compensate for errors both in the geometric model and in manipulator motions. There are dispensable data in the shapes, positions and orientations of objects when achieving skill motions in a task. Therefore, we can simplify geometric models by considering the dispensable data in a skill motion. We call such robust and simplified models 'false models'. This paper describes our definition of false models used in planning and visual sensing, and shows the effectiveness of our method using examples of tasks involving the manipulation of mechanical and electronic parts. Furthermore, we show the application of false models to objects of indefinite sizes and shapes using examples of the same tasks.     

Dynamic adaptive trajectory tracking control of nonholonomic mobile robots using multiple models approach

This paper presents an approach for the trajectory tracking control of nonholonomic wheeled mobile robots (WMR) by combining one of the existing adaptive control methods and multiple identification models. The overall system includes two types of controllers in the control scheme. A kinematic controller developed by using kinematic model produces the required linear and angular velocities of the robot for tracking a reference trajectory. These required velocities are used to calculate the torques using an adaptive dynamic controller with multiple models. The proposed method uses the multiple models of the WMR for the identification of the dynamic parameters and performs switching between the given models. The models used in the identification are identical, except for the initial estimates of the parameters. By using an adaptive dynamic controller with multiple models of the WMR, enhancement in transient response is obtained. Stability analysis of the overall system is given, and simulation results are presented to demonstrate the effective performance of the adaptive control by using multiple models approach.     

Statistical Learning for Humanoid Robots

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.