Optimal geometric modeler for robot motion planning

Planning collision‐free trajectories under real‐time restrictions is a challenging topic in robotics. In order to reduce computational cost in the collision avoidance process, some authors have proposed different model representations. This article presents an optimal method able to generate automatically geometric models of the objects in a robotic system. For each object, two models are obtained, i.e., the minimum outer and the maximum inner models. Availability of both models allows one to face more successfully robot motion applications. The geometric modeler is focused on the generation of spherically extended polytopes. Each object to model is represented by a set of points taken from its surface. Models are generated through the application of an iterative process based on the Hough transform. When both outer and inner models have been generated, a parameter for evaluating the quality of the models is introduced. This parameter can be used by a rule‐based system for increasing the complexity of the model generated and improving, therefore, the accuracy of the representation. © 2000 John Wiley & Sons, Inc.     

非完整移动机器人鲁棒运动规划

非完整移动机器人利用传感器可以解决不确定性模型和未知环境中的许多问题. 利用移动机器人上配备的传感器的信息组合提出了一种在线视点寻求算法, 结合移动机器人的运动方程和传感器的量测方程采用扩展Kalman估计来对移动机器人的位置进行修正, 以降低运动的不确定性, 从而得到一种鲁棒的规划算法, 仿真的结果证明了上述方法是行之有效的.     

基于传感器的非完整移动机器人运动规划

针对非完整移动机器人在未知室内环境中提出了一种路径规划方法, 通过利用传感器对周围环境的探测和实时处理传感器数据, 以及所设计的目标寻找函数, 可以有效地完成其运动规划. 该方法能够确保移动机器人在无障碍物区或障碍物对机器人不构成危险时加速前进, 在障碍物区能够慢速绕过, 从而使得移动机器人快速且安全地到达目标位置, 仿真的结果证明了该方法的有效性.     

Sampling-based robot motion planning: Towards realistic applications

This paper presents some of the recent improvements in sampling-based robot motion planning. Emphasis is placed on work that brings motion-planning algorithms closer to applicability in real environments. Methods that approach increasingly difficult motion-planning problems including kinodynamic motion planning and dynamic environments are discussed. The ultimate goal for such methods is to generate plans that can be executed with few modifications in a real robotics mobile platform.     

Near-time optimal robot motion planning foe on-line applications

Solving current formulations of the time-optimal point-to-point motion problem for robotic manipulators is a computationally intensive task. Thus, most existing solutions are not suitable for on-line motion planning applications, such as the interception of moving targets, where time-optimality of the motion is advantageous. A novel technique is proposed in this article that separates the time-optimal point-to-point motion problem into the following two sub-problems: (1) selection of a near-time-optimal path between the two endpoints, and (2) generation of time-optimal motion along the selected path (i.e., constrained continuous path motion). Although our approach uses known path-constrained time-optimal-motion algorithms for the second sub-problem, a new method is proposed for the selection of near-time-optimal paths. Based on a study of the characteristics of global-time-optimal paths, the near-optimal path is selected as a minimum-curvature joint spline, tangent to one of the manipulator's acceleration directions at the start point, and tangent to the required manipulator velocity direction at the end point. The algorithm for determining the overall near-optimal path is described herein, along with an example. Simulation test results and computation-time studies indicate that the proposed method is suitable for on-line motion planning applications. © 1995 John Wiley & Sons, Inc.     

Learning object deformation models for robot motion planning

In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot’s motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude.     

An interactive tool for mobile robot motion planning

This paper presents an interactive tool aimed at facilitating the understanding of several well-known algorithms and techniques involved in solving mobile robot motion problems. These range from those modelling the mechanics of mobility to those used in navigation. The tool focuses on describing these problems in a simple manner in order to be useful for education purposes among different disciplines. By highlighting interactivity, the tool provides a novel means to study robot motion planning ideas in a manner that enhances full understanding. Furthermore, the paper discuses how the tool can be used in an introductory course of mobile robotics.     

Acquisition of obstacle motion patterns to improve mobile robot motion planning

Mobile robots for advanced applications have to act in environments which contain moving obstacles (humans). Even though the motions of such obstacles are not precisely predictable, usually they are not completely random; long-term observation of obstacle behavior may thus yield valuable knowledge about prevailing motion patterns. By incorporating such knowledge as statistical data, a new approach called statistical motion planning yields robot motions which are better adapted to the dynamic environment. To put these ideas into practice, an experimental system has been developed. Cameras observe the workspace in order to detect obstacle motion. Statistical data is derived and represented as a set of stochastic trajectories. This data can be directly employed in order to calculate collision probability, i.e. the probability of encountering an obstacle during the robot's motion. Further aspects of motion planning are addressed: path planning which minimizes collision probability, estimation of expected time to reach the goal and reactive planning.     

Integrating active localization into high-level robot control systems

High-level control systems are designed to enable mobile robots to successfully perform complex missions such as office delivery and survillance tasks. For that purpose they have to control, coordinate, and monitor different kinds of subtasks like navigation, manipulation, and perception. An important aspect of the effectiveness of high-level control systems is the ability to cope with failures that occur during the execution of such subtaks. In this paper we focus on the particular subtask of estimating the position of the robot and show how to achieve its robust integration into the high-level control system. The principle of this integration is to monitor the certainty of the position estimation and to autonomously relocalize the robot whenever the uncertainly grows too large. We present a localization approach which accurately and efficiently keeps track of the robot's position. Furthermore, it provides a measure for detecting localization failures and it is able to autonomously relocalize the robot in such situations. In addition to this, we introduce structured reactive plans, which can be interrupted by such active localization processes at any point in time and allow the robot to complete its mission afterwards. Our method has been implemented and shown to be robust in long-term experiments involving a typical office delivery scenario.     

Multi-objective approach for robot motion planning in search tasks

This work addresses the problem of single robot coverage and exploration in an environment with the goal of finding a specific object previously known to the robot. As limited time is a constraint of interest we cannot search from an infinite number of points. Thus, we propose a multi-objective approach for such search tasks in which we first search for a good set of positions to place the robot sensors in order to acquire information from the environment and to locate the desired object. Given the interesting properties of the Generalized Voronoi Diagram, we restrict the candidate search points along this roadmap. We redefine the problem of finding these search points as a multi-objective optimization one. NSGA-II is used as the search engine and ELECTRE I is applied as a decision making tool to decide among the trade-off alternatives. We also solve a Chinese Postman Problem to optimize the path followed by the robot in order to visit the computed search points. Simulation results show a comparison between the solution found by our method and solutions defined by other known approaches. Finally, a real robot experiment indicates the applicability of our method in practical scenarios.     

机械臂最优运动规划问题的混合求解

提出一种基于GA和SQP求解机械臂最优运动规划问题的混合算法．首先采用B样条函数逼近关节运动轨迹，将最优控制问题转化为有约束的非线性规划问题，然后引入基于种群的GA算法，给出全局最优解的初始估计；最后利用序列二次规划(SQP)得到高精度全局最优解．仿真结果表明该方法优于单纯的GA或SQP方法。     

Integrating workers’ differences into workforce planning

In today’s competitive market, manufacturers need to work hard towards improving their production system performance in order to satisfy customer demands. In such a situation, most companies develop production systems that can help in quality improvement, cost reduction and throughput time reduction. In this research, we consider a workforce planning (WP) model including some human aspects such as skills, training, and workers’ personalities and motivation. A multi-objective non-linear programming model is developed in order to minimize the hiring, firing, training and overtime costs and minimize the number of fired most productive workers. The purpose is to determine the number of workers for each worker type, the number of workers trained, and the number of overtime hours. Moreover, a decision support system (DSS) based on the proposed model is introduced using the Excel-LINGO software interfacing feature. The results indicate that the proposed model can provide a promising workforce planning approach to easily apply it in practice.     

Constrained motion planning for robot manipulators using local geometric information

This paper proposes a new motion planning algorithm for robot manipulator systems with path constraints. The constraint function of a manipulator determines the subspace of its joint space, and a proposed sampling-based algorithm can find a path that connects valid samples in the subspace. These valid samples can be obtained by projecting the samples onto the subspace defined by the constraint function. However, these iteratively generated samples easily fall into local optima, which degrades the search performance. The proposed algorithm uses the local geometric information and expands the search tree adaptively to avoid the local convergence problem. It increases the greediness of the search tree when it expands toward an unexplored area, which produces the benefit of reducing computational time. In order to demonstrate the performance of the algorithm, it is applied to two example problems: a maze problem using PUMA 560 under predefined constraints and a closed-chain problem using two Selective Compliance Assembly Robot Arms. The results are compared with those obtained with an existing algorithm to show the improvement in performance.     

A unified motion planning method for a multifunctional underwater robot

This article deals with motion planning for a multifunctional underwater robot that can perform various tasks such as swimming, walking, and grasping objects. We have developed a unified motion planning method that can generate motion planning for a variety of movements using a single algorithm. With this method, motion planning problems are modeled as finite-horizon Markov decision processes, and optimum motion planning is achieved by dynamic programming. However, conventional dynamic programming is sometimes considered to have limited applicability because of “the curse of dimensionality.” To avoid this issue, we applied a random network as a state transition network to suppress the explosion in the number of states. The effectiveness of the proposed method is demonstrated through numerical simulations involving two types of task for multifunctional robots. One is a reaching task, and the other is a thrust force generation task.     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

Design and motion planning of an autonomous climbing robot with claws

This paper presents the design of a novel robot capable of climbing on vertical and rough surfaces, such as stucco walls. Termed CLIBO (claw inspired robot), the robot can remain in position for a long period of time. Such a capability offers important civilian and military advantages such as surveillance, observation, search and rescue and even for entertainment and games. The robot’s kinematics and motion, is a combination between mimicking a technique commonly used in rock climbing using four limbs to climb and a method used by cats to climb on trees with their claws. It uses four legs, each with four-degrees-of-freedom (4-DOF) and specially designed claws attached to each leg that enable it to maneuver itself up the wall and to move in any direction. At the tip of each leg is a gripping device made of 12 fishing hooks and aligned in such a way that each hook can move independently on the wall’s surface. This design has the advantage of not requiring a tail-like structure that would press against the surface to balance its weight. A locomotion algorithm was developed to provide the robot with an autonomous capability for climbing along the pre-designed route. The algorithm takes into account the kinematics of the robot and the contact forces applied on the foot pads. In addition, the design provides the robot with the ability to review its gripping strength in order to achieve and maintain a high degree of reliability in its attachment to the wall. An experimental robot was built to validate the model and its motion algorithm. Experiments demonstrate the high reliability of the special gripping device and the efficiency of the motion planning algorithm.     

An aperiodic straight motion planning method for a quadruped walking robot

In even terrain, wave gait is the periodic gait having the optimal stability. In this paper, we focus on aperiodic forward straight motion having the lifting sequence of wave gait in order for quadruped to adapt to terrain and to have good moving capability. We investigated the condition of support pattern from which such gait motion can be generated. It is proved that from any support pattern satisfying the condition, it is always possible to transform the given support pattern to the support pattern of wave gait. An aperiodic gait planning method that adapt to terrain and maximize moving capability is proposed. A simulation result shows that the proposed method works well in rough terrain having forbidden areas.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

Discrete abstractions for robot motion planning and control in polygonal environments

In this paper, we present a computational framework for automatic generation of provably correct control laws for planar robots in polygonal environments. Using polygon triangulation and discrete abstractions, we map continuous motion planning and control problems, specified in terms of triangles, to computationally inexpensive problems on finite-state-transition systems. In this framework, discrete planning algorithms in complex environments can be seamlessly linked to automatic generation of feedback control laws for robots with underactuation constraints and control bounds. We focus on fully actuated kinematic robots with velocity bounds and (underactuated) unicycles with forward and turning speed bounds.     

Method for tuning the motion planning parameters of a mobile robot

This paper presents a method for configuring the motion planning system of an omniwheeled mobile robot with a differential drive. A simulation program that models the horizontal movement of the robot is described. This simulation program is used to select the optimal parameters for the differential drive control algorithm. Then, the motion planning system is tested on a real robot, which is called RB-2, to adjust the parameters selected. This approach allows the control algorithm to be tuned efficiently and effectively, minimizing the number of its test runs on the physical robot.