Study on Non-holonomic Cartesian Path Planning of a Free-Floating Space Robotic System

The non-holonomic characteristic of a free-floating space robotic system is used to plan the path of the manipulator joints, by whose motion the base attitude and the inertial pose (the position and orientation with respect to the inertial frame) of the end-effector attain the desired values. First, the kinematic equations of a free-floating space robot are simplified and the system state variables are transformed to another form composed of base attitude and joint angles. Then, the joint trajectories are parameterized using sinusoidal functions, whose arguments are seven-order polynomials. Third, the planning problem is transformed to an optimization problem; the cost function, defined according to the accuracy requirements of system variables, is the function of the parameters to be determined. Finally, the Particle Swarm Optimization (PSO) algorithm is used to search the solutions of the parameters that determine the joint trajectories. The presented method meets three typical applications: (i) point-to-point maneuver of the end-effector without changing the base attitude, (ii) attitude maneuver of the base without changing the end-effector's pose and (iii) point-to-point maneuver of the end-effector with adjusting the base attitude synchronously. The simulation results of a spacecraft with a 6-d.o.f. manipulator verify the performance and the validity of the proposed method.     

Path planning for a quadruped robot: an artificial field approach

In this paper, we consider the problem of planning a feasible path for a quadruped walking robot in an environment of obstacles. In conventional path-planning problems, the main focus is merely collision avoidance with obstacles since a wheeled robot is involved. However, in the case of a legged robot, both collision avoidance and crossing over obstacles must be taken into account in the process of path planning. Furthermore, the constraints of the gait should be considered to guarantee the feasibility of a planned path. To resolve this complicated problem in a systematic way, a new concept of an artificial thermal field is proposed. Specifically, with the assumption that a robot walks with a periodic crab gait, a robot and obstacles in a three-dimensional (3D) space are projected on a 2D plane. Next, the 2D obstacles are transformed into the configuration space of a quadruped robot. A feasible path is finally sought in an artificial thermal field which is constructed numerically on the discretized configuration space. To verify the efficacy of the proposed approach, three notable simulation results are provided.     

Three-Dimensional Path Planning of a Climbing Robot Using Mixed Integer Linear Programming

The City-Climber robot is a novel wall-climbing robot developed at The City College of New York that has the capability to move on floors, climb walls, walk on ceilings and transit between them. In this paper, we first develop the dynamic model of the City-Climber robot when it travel on different surfaces, i.e., floors, walls and ceilings, respectively. Then, we present a path planning method for the City-Climber robot using mixed integer linear programming (MILP) in three-dimensional (3-D) building environments that consist of objects with primitive geometrical shapes. MILP provides an optimization framework that can directly incorporate dynamic constraints with logical constraints such as obstacle avoidance and waypoint selection. In order to use MILP to solve the obstacle avoidance problem, we simplify and decouple the robot dynamic model into a linear system by introducing a restricting admissible controller. The decoupled model and obstacle can be rewritten as a linear program with mixed-integer linear constraints that account for the collision avoidance. A key benefit of this approach is that the path optimization can be readily solved using the AMPL and CPLEX optimization software with a MATLAB interface. Simulation results show that the framework of MILP is well suited for path planning and obstacle avoidance problems for the wall-climbing robot in 3-D environments.     

Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

This paper introduces a new approach to developing a fast gait for a quadruped robot using genetic programming (GP). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multi-dimensional space. Several recent approaches have focused on using genetic algorithms (GAs) to generate gaits automatically and have shown significant improvement over previous gait optimization results. Most current GA-based approaches optimize only a small, pre-selected set of parameters, but it is difficult to decide which parameters should be included in the optimization to get the best results. Moreover, the number of pre-selected parameters is at least 10, so it can be relatively difficult to optimize them, given their high degree of interdependence. To overcome these problems of the typical GA-based approach, we have proposed a seemingly more efficient approach that optimizes joint trajectories instead of locus-related parameters in Cartesian space, using GP. Our GP-based method has obtained much-improved results over the GA-based approaches tested in experiments on the Sony AIBO ERS-7 in the Webots environment. The elite archive mechanism is introduced to combat the premature convergence problems in GP and has shown better results than a traditional multi-population approach.     

Cycab bi-steerable cars: a new family of differentially flat systems

The Cycab robots are a family of new mobile platforms already used in several research laboratories and meant for different applications such as public transportation in airport terminals or self-service cars in pedestrian zones. From a kinematic point of view, the Cycab specificity is the turning of its rear wheels as a function of the steering angle of the front wheels which makes it a bi-steerable non-holonomic vehicle. This feature enhances the maneuverability of the Cycab in cluttered environments. However, the associated kinematic model is new and different from those commonly treated in the robotics literature (such as the car-like robot, tractor-trailer, etc). To our knowledge such a system has not yet been studied and, in particular, there is no existing motion planner for it. In this paper, we tackle the study of this non-holonomic system, by establishing its kinematic model and analyzing its differential flatness property. We give a necessary and sufficient condition on the front/rear steering angle relation to guarantee the flatness of the system. This opens the way to the application of many motion planning and control methods. From this study we deduce a first motion planner for the system.     

Triple RRTs: An Effective Method for Path Planning in Narrow Passages

Although Rapidly-exploring Random Trees (RRTs) have been successfully applied in path planning of robots with many degrees of freedom under non-holonomic and differential constraints, rapidly identifying and passing through narrow passages in a robot's configuration space remains a challenge for RRTs-based planners. This paper presents a novel two-stage approach to address the problem of multi-d.o.f. robot path planning in high-dimensional configuration space with narrow corridors. The first stage introduces an efficient sampling algorithm called Bridge Test to find a global roadmap that identifies the critical region. The second stage presents two varieties of RRTs, called Triple-RRTs, to search for a local connection under the guidance of the global landmark. The two-stage strategy keeps a fine balance between global heuristics and local connection, resulting in high performance over the previous RRTs-based path planning methods. We have implemented the Triple-RRTs planners for both rigid and articulated robots in two- and three-dimensional environments. Experimental results demonstrate the effectiveness of the proposed method.     

Motion Planning Strategy for Finding an Object with a Mobile Manipulator in Three-Dimensional Environments

In this paper, we investigate the problem of minimizing the average time required to find an object in a known three-dimensional environment. We consider a 7-d.o.f. mobile manipulator with an 'eye-in-hand' sensor. In particular, we address the problem of searching for an object whose unknown location is characterized by a known probability density function. We present a discrete formulation, in which we use a visibility-based decomposition of the environment. We introduce a sample-based convex cover to estimate the size and shape of visibility regions in three dimensions. The resulting convex regions are exploited to generate trajectories that make a compromise between moving the manipulator base and moving the robotic arm. We also propose a practical method to approximate the visibility region in three dimensions of a sensor limited in both range and field of view. The quality and success of the generated paths depend significantly on the sensing robot capabilities. In this paper, we generate searching plans for a mobile manipulator equipped with a sensor limited in both field of view and range. We have implemented the algorithm and present simulation results.     

Development of a steerable,wheel-type,in-pipe robot and its path planning

This paper presents the development of a steerable, wheel-type, in-pipe robot and its path planning. First, we show the construction of the robot and demonstrate its locomotion inside a pipe. The robot is composed of two wheel frames and an extendable arm which links the centers of the two wheel frames. The arm presses the frames against the interior wall of a pipe to support the robot. The wheels of the frames are steered independently so that the robot can turn within a small radius of rotation. Experimental results of the locomotion show that the steering control is effective for autonomous navigation to avoid obstacles and to enter the joint spaces of L- and T-shaped pipes. Generally, autonomous navigation is difficult for wheel-type robots because the steering angles required to travel along a desired path are not easily determined. In our previous work, the relationship between the steering angles and locomotion trajectories in a pipe has already been analyzed. Using this analysis, we propose the path planning in pipes.     

Variants of the Quantized Visibility Graph for Efficient Path Planning

We propose variants of the quantized visibility graph (QVG) for efficient path planning. Conventional visibility graphs have been used for path planning when the obstacles are polygonal. The QVG extends its usability to arbitrarily-shaped objects by representing the obstacles as polygons. We propose QVG variants which represent all combinations of three factors, each with two alternatives: (i) quantization level (fixed-level or multiple-level), (ii) object representation method (inner and boundary cells together or boundary cells only), and (iii) methods used to check whether pairs of points are mutually visible (rotational plane sweep algorithm or sign inequality discrimination (SID) algorithm). In the verification of the efficiency of the proposed QVGs, (i) all QVGs produced the same best path, which was shorter than the convectional algorithms, (ii) computational cost to find the shortest path is lower when using QVGs than when using the convectional algorithms and (iii) the QVG that uses multi-level quantization, partial obstacle representation and SID visibility checking provides the shortest best path and has lower computational cost than all other methods.     

Design and Implementation of a Novel Outdoor Road-Cleaning Robot

In order to clean unknown outdoor environments, we devised a novel outdoor cleaning robot using mainly on-board vision-based auto-navigation in this paper. The track-driven and cleaning mechanisms of the robot are designed for cleaning tasks in outdoor rough terrain. A single image sensing module is exploited for clean-region detection and three ultrasonic ranging modules are used for obstacle avoidance. The cleaning task is performed autonomously after the boustrophedon path planning is completed in the grid-cell map. The map is obtained from transforming the two-dimensional images captured by the image sensor. The robot also self-localizes based on the deduced boundary lines at the end of each cleaning stage to continue its unfinished cleaning work. The experimental results prove that our outdoor cleaning robot performs well in general outdoor environments.     

Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru I'humanoid robot, which is under development in our laboratory. The evaluation by simulations shows that the proposed method has a good performance.     

机器人导航系统中的路径规划算法

导航系统是反映移动机器人自主特性与智能行为的关键问题之一,它所能完成的功能包括:环境的感知与识别、路径规划、路径跟踪、障碍回避等。路径规划是移动机器人导航技术中的重要组成部分,它是机器人执行各种任务的基础,而如何在动态时变环境中有效的实施路径规划是亟待解决的问题。本文综述了该领域研究的主要内容及其发展动态,在对一些较有代表性的研究思想及其相关算法分析的基础上,指出了存在的不足和有待进一步研究的问题,并提出了一些解决思路。     

Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms

This paper describes the application of the Structured Genetic Algorithm (sGA) to design neuro-controllers for an unstable physical system. In particular, the approach uses a single unified genetic process to automatically evolve complete neural nets (both architectures and their weights) for controlling a simulated pole-cart system. Experimental results demonstrate the effectiveness of the sGA-evolved neuro-controllers for the task—to keep the pole upright (within a specified vertical angle) and the cart within the limits of the given track.     

区间样条小波在机械臂路径规划中的应用研究

研究航天器在轨维护问题,针对空间任务机械臂路径规划中的函数拟合问题,采用区间样条小波函数进行了最小能量的路径规划研究。利用动量矩守恒原理建立了平面多刚体系统动力学模型,并提出了基于能量的优化目标函数;然后,根据三阶区间样条小波的多尺度特性,给出了Sobloev空间上满足给定边界条件的近似函数表达式,并对典型三体空间机械臂路径规划问题进行了优化仿真分析,结果表明区间样条小波能够而自动满足一阶边界条件,达到降低计算量、提高计算精度与效率的目标要求。     

一类整数性目标规划的遗传算法

本文给出了以惩罚函数法将约束优化问题转化为无约束优化问题的通用算法,提出了将遗传算法和惩罚函数法相结合用于求解整数性目标规划问题的具体方法。计算机数值仿真结果表明了该方法的有效性。     

带滑移铰空间机械臂的非完整运动规划遗传算法

本文讨论带滑移铰空间机械臂非完整运动规划的最优控制问题。利用系统的非完整特性,将带滑移铰空间机械臂运动规划转化为非线性系统最优控制问题。提出基于遗传算法的非完整运动规划最优控制方法。通过仿真计算,验证了该方法的有效性和可行性。     

应用几何理论的智能机器人路径规划仿真

路径规划是智能机器人导航的基本环节之一,具有复杂性、随机性等特点。研究应用于医疗服务系统的智能机器人,对于机器人工作环境最大的特征就是周围物体运动的复杂性和随机性。充分考虑了运动障碍物的随机性,研究了随机运动障碍物的位置分布规律,并结合其具有惯性的特点和导航需求,提出了一种应用几何理论对智能机器人运动路线进行逐步判别的直线-切线-垂线路径规划方法。经计算机仿真表明,方法是一种正确、高效、实用的算法,对研究智能机器人绕过随机运动障碍具有一定的参考价值。     

基于遗传算法的六自由度机器人焊接路径规划

对六自由度焊接机器人的轨迹焊接问题,提出了基于遗传算法的路径规划方法。通过D-H法,建立六自由度焊接机器人的运动方程,以系统总的能量损耗为适应度函数,利用遗传算法,给出优化路径。并以实际中的某型号六自由度焊接机器人为例,通过仿真实验证明了该方法的正确性与可行性。     

Study on the Configuration Space Based Algorithmic Path Planning of Industrial Robots in an Unstructured Congested Three-Dimensional Space: An Approach Using Visibility Map

Obstacle avoidance and subsequently collision-free path planning is a potential field of robotics research, specially in the perspective of today's industrial scenario. In this paper, the celebrated method, namely, Visibility Map is being used to generate feasible collision-free near-optimal safe path(s) for a three- dimensional congested robot workspace using heuristic algorithms. The final path is obtainable in terms of joint configurations, by considering the Configuration Space of the task-space. The developed algorithms have been verified by considering typical 2D workspaces at the onset, cluttered with different obstacles (convex and/or concave) with regular geometries and later on, with the real spatial manifold. The outcome of these algorithms has been found instrumental in programming an industrial robot in order to perform a series of task in the shop-floor. A case-study reveals the effectiveness of the heuristics involved in the developed algorithms, by virtue of the successful application in an unstructured industrial environment to carry out robotized material handling operation in real-time.     

Path Planning and Control of a Cooperative Three-Robot System Manipulating Large Objects

After a brief review of the current research on multi-robot systems, the paper presents a path planning and control scheme for a cooperative three-robot system transferring/manipulating a large object from an initial to a desired final position/orientation. The robots are assumed to be capable of holding the object at three points that define an isosceles triangle. The mode of operation adopted is that of a master-and-two-slave robots. The control scheme employs the differential displacement of the object which is transformed into that of the end-effector of each robotic arm, and then used to compute the differential displacements of the joints of the robots. The scheme was applied to several 3-robot systems by simulation and proved to be adequately effective, subject to certain conditions regarding the magnitude of the differential displacements. Here, an example is included which concerns the case of three Stäubli RX-90L robots.