Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories

Most of today’s mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.

     

Reactive navigation of underwater mobile robot using ANFIS approach in a manifold manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot’s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system (ANFIS) has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace. An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.     

冗余度机器人自运动中的混沌运动及其控制

以一个平面3R刚性冗余度机器人为研究对象,采用PD调节器进行工作空间内的负反馈,控制机器人末端重复跟踪工作空间内封闭路径,对此过程中机器人自运动中可能存在的混沌运动进行了研究.通过运动仿真和相图,以及计算系统的最大李亚普诺夫指数,研究发现,基于雅可比矩阵的伪逆,求解冗余度机器人运动学逆解时其自运动是混沌的.首次应用延迟反馈控制法对所发现的混沌运动进行控制,在适当的参数条件下,成功地将混沌运动转变为规则的周期运动.􀁱 􀁽     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.     

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.     

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics.The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal.In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.     

基于LabVIEW的机器人的运动控制系统研究

为了减少机器人运动轨迹误差,实现对机器人的精准控制,提高机器人的运动效率,设计了基于LabVIEW的机器人的运动控制系统;采用了NI公司的控制板卡,选用了Odriver驱动器作为主控制器,选用大力矩伺服电机作为驱动电机,实现运动控制系统的硬件架构的设计;通过脉冲信号驱动电机运动,获取机器人的运动轨迹数据,通过进行对控制...     

Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human–robot interaction

Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot’s intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance.     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

A chaotic path planning generator for autonomous mobile robots

This work presents a chaotic path planning generator which is used in autonomous mobile robots, in order to cover a terrain. The proposed generator is based on a nonlinear circuit, which shows chaotic behavior. The bit sequence, produced by the chaotic generator, is converted to a sequence of planned positions, which satisfies the requirements for unpredictability and fast scanning of the entire terrain. The nonlinear circuit and the trajectory-planner are described thoroughly. Simulation tests confirm that with the proposed path planning generator better results can be obtained with regard to previous works.     

基于LiDAR/INS的野外移动机器人组合导航方法

移动机器人在地形复杂等野外环境跨区域运动时,机器人运动特性和环境特征变化更为明显,由此引起的点云畸变和特征点稀疏等问题尤为突出,有必要结合传感器标定误差、车轮打滑和车体颠簸等因素进一步改进机器人的位姿估计精度。本文对基于LiDAR/INS的移动机器人环境建模和自主导航方法展开研究,针对LeGO-LOAM等在处理车体姿态快速变化时的性能退化问题,提出一种适用于野外移动机器人运动特性的点云特征分析和多传感融合方法,利用IMU的预积分与LiDAR的scan-to-map构成优化函数,进而迭代更新机器人的位姿。野外环境实验结果表明,当机器人以较高速度做转弯运动或在短时间内多次转向时,本文所提方法仍可以为优化提供良好的初值估计,相比LeGO-LOAM等方法具有更高的位姿估计精度。     

Empirical learning in mobile robot navigation

The purpose of this study is to improve the locomotion performance for autonomous mobile robots in outdoor environments. In this paper improvement of an environment model is called empirical locomotion performance leaming. A system avoids wasting time of observations and actions by analyzing data from the last run. We propose a method of empirical learning. The method is expressed by rewriting the rules on the trajectory data. Brief route information for navigating a robot is represented with motion directions at intersections and metric distances between intersections. The behavior of our robot is based on a locomotion strategy 'sign pattern-based stereotyped motion'. The behaviors are implemented on our mobile robot HARUNOBU-4 and tested at our university campus. Experimental results show a robustness of our proposed behaviors under dynamic environments with existing obstacles. Furthermore, they showed that our proposed rewriting rules improved the locomotion performance. In particular, searching time was shortened by 87% (from 453 to 61 s) and the travel distance was shortened by 10% (from 173.8 to 157.5 m).     

Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.     

Detection of robustly collision-free trajectories in unpredictable environments in real-time

One of the ultimate goals in robotics is to make robots of high degrees of freedom (DOF) work autonomously in real world environments. However, real world environments are unpredictable, i.e., how the objects move are usually not known beforehand. Thus, whether a robot trajectory is collision-free or not has to be checked on-line based on sensing as the robot moves. Moreover, in order to guarantee safe motion, the motion uncertainty of the robot has to be taken into account. This paper introduces a general approach to detect if a high-DOF robot trajectory is continuously collision-free even in the presence of robot motion uncertainty in an unpredictable environment in real time. Our method is based on the novel concept of dynamic envelope, which takes advantage of progressive sensing over time without predicting motions of objects in an environment or assuming specific object motion patterns. The introduced approach can be used by general real-time motion planners to check if a candidate robot trajectory is continuously and robustly collision-free (i.e., in spite of uncertainty in the robot motion).     

Real-time Velocity Alteration Strategy for Collision-free Trajectory Planning of Two Articulated Robot Manipulators

Two articulated robots working in a shared workspace can be programmed by planning the tip trajectory of each robot independently. To account for collision avoidance between links, a real-time velocity alteration strategy based on fast and accurate collision detection is proposed in this paper to determine the step of next motion of slave (low priority) robot for collision-free trajectory planning of two robots with priorities. The effectiveness of the method depends largely on a newly developed method of accurate estimate of distance between links. By using the enclosing and enclosed ellipsoids representations of polyhedral models of links of robots, the minimum distance estimate and collision detection between the links can be performed more efficiently and accurately. The proposed strategy is implemented in an environment where the geometric paths of robots are pre-planned and the preprogrammed velocities are piecewise constant but adjustable. Under the control of the proposed strategy, the master robot always moves at a constant speed. The slave robot moves at the selected velocity, selected by a tradeoff between collision trend index and velocity reduction in one collision checking time, to keep moving as far as possible and as fast as possible while avoid possible collisions along the path. The collision trend index is a fusion of distance and relative velocity between links of two robots to reflect the possibility of collision at present and in the future. Graphic simulations of two PUMA560 robot arms working in common workspace but with independent goals are conducted. Simulations demonstrate the collision avoidance capability of the proposed approach as compared to the approach based on bounding volumes. It shows that advantage of our approach is less number of speed alterations required to react to potential collisions.     

工业机器人遥操作系统的空间映射与控制策略

针对工业机器人遥操作系统中存在的主从机器人工作空间差异以及运动控制精度与安全问题,提出了一种工作空间映射算法与位置—速度混合控制策略。首先,将遥操作划分为自由运动和交互两个阶段,在自由运动阶段采用映射算法使主从机器人的工作空间高度覆盖,使主机器人可操控的从机器人运动范围最大化。进一步,在交互阶段设计了一种位置—速度混合控制策略对工业机器人的运动进行准确的控制,使主从机器人的实际位置轨迹准确的跟随,并进一步引入反馈引导力以实现安全的控制。最后在Touch-ABB IRB120主从机器人遥操作实验平台上对所提控制方法进行验证,实验结果表明该方法使得主从机器人运动范围在高度覆盖的同时可以保证遥操作控制的精度。     

Navigation system including associative memory

A new adaptive linear robot control system for a robot work cell that can visually track and intercept stationary and moving objects undergoing arbitrary motion anywhere along its predicted trajectory within the robot's workspace is presented in this paper. The proposed system was designed by integrating a stationary monocular CCD camera with off-the-shelf frame grabber and an industrial robot operation into a single application on the MATLAB platform. A combination of the model based object recognition technique and a learning vector quantization network is used for classifying stationary objects without overlapping. The optical flow technique and the MADALINE network are used for determining the target trajectory and generating the predicted robot trajectory based on visual servoing, respectively. The necessity of determining a model of the robot, camera, all the stationary and moving objects, and environment is eliminated. The location and image features of these objects need not be preprogrammed, marked and known before, and any change in a task is possible without changing the robot program. After the learning process on the robot, it is shown that the KUKA robot is capable of tracking and intercepting both stationary and moving objects at an optimal rendezvous point on the conveyor accurately in real-time.     

Path planning for an autonomous mobile robot considering a region with a velocity constraint in a real environment

Recently, many research projects and competitions have attempted to find an autonomous mobile robot that can drive in the real world. In this article, we consider a path-planning method for an autonomous mobile robot that would be safe in a real environment. In such a case, it is very important for the robot to be able to identify its own position and orientation in real time. Therefore, we applied a localization method based on a particle filter. Moreover, in order to improve the safety of such autonomous locomotion, we improved the path-planning algorithm and the generation of the trajectory so that it can consider a region with a limited maximum velocity. In order to demonstrate the validity of the proposed method, we participated in the Real World Robot Challenge 2010. The experimental results are given.     

Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology

Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.