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.     

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.     

Motion planning and contact control for a tele-assisted hydraulic underwater robot

Implementing tele-assistance or supervisory control for autonomous subsea robots requires atomic actions that can be called from high level task planners or mission managers. This paper reports on the design and implementation of a particular atomic action for the case of a subsea robot carrying out tasks in contact with the surrounding environment.Subsea vehicles equipped with manipulators can have upward of 11 degrees of freedom (DOF), with degenerate and redundant inverse kinematics. Distributed local motion planning is presented as a means to specify the motion of each robot DOF given a goal point or trajectory. Results are presented to show the effectiveness of the distributed versus non-distributed approach, a means to deal with local minima difficulties, and the performance for trajectory following with and without saturated joint angles on a robot arm.Consideration is also given to the modelling of hydraulic underwater robots and to the resulting design of hybrid position/force control strategies. A model for a hydraulically actuated robot is developed, taking into account the electrohydraulic servovalve, the bulk modulus of oil, piston area, friction, hose compliance and other arm parameters. Open and closed-loop control results are reported for simulated and real systems.Finally, the use of distributed motion planning and sequential position/force control of a Slingsby TA-9 hydraulic underwater manipulator is described, to implement an atomic action for tele-assistance. The specific task of automatically positioning and inserting a Tronic subsea mateable connector is illustrated, with results showing the contact conditions during insertion.     

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.     

新的室内移动机器人的实时定位和运动规划方法

提出了一种将传感器信息与先验环境信息融合实现移动机器人定位和运动规划的方法.这种方法选用SICK LMS200型激光扫描测距仪为机器人感知装置,根据室内障碍物的顶点或拐角信息进行定位和运动规划.这种定位和规划方法模仿了盲人在熟悉环境下用拐杖走路的过程,适于室内移动机器人的实时定位和运动规划.     

Online motion planning for unexplored underwater environments using autonomous underwater vehicles

We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, that is, those that meet the vehicle’s motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time‐consuming pruning routines. The second one is to opportunistically check the states’ risk of collision. To evaluate the proposed approach, we use the Sparus II performing autonomous missions in different real‐world scenarios. These experiments consist of simulated and in‐water trials for different tasks. The conducted tasks include the exploration of challenging scenarios such as artificial marine structures, natural marine structures, and confined natural environments. All these applications allow us to extensively prove the efficacy of the presented approach, not only for constant‐depth missions (2D), but, more important, for situations in which the vehicle must vary its depth (3D).     

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.     

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.     

Path deformation method for robot motion planning problems in the presence of obstacles

This paper studies the following path planning problem for a robot. There is a given path avoiding obstacles. If existing obstacles change their location or new obstacles appear, the preplanned path must be appropriately deformed for all obstacles avoidance. We develop a solution approach based on the component-wise method of smoothing the path curvature and the method of potentials. The proposed approach enables to avoid obstacles and smooth the path and its curvature (in the 2D and 3D cases).     

A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes

In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.     

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.     

基于传感器信息的水下机器人滚动路径规划

针对环境中障碍物为任意形状的水下机器人路径规划问题,提出了基于传感器信息的水下机器人滚动路径规划方法,该法充分利用传感器在每一采样时刻的规划窗口内实时探知的环境信息,用模糊控制方法给出机器人下一步的行走方法,探讨了凹形障碍区域中机器人的逃离方法,仿真结果表明了所提方法是有效的。     

A gradient-based path optimization method for motion planning

Most algorithms in probabilistic sampling-based path planning compute collision-free paths made of straight line segments lying in the configuration space. Due to the randomness of sampling, the paths make detours that need to be optimized. The contribution of this paper is to propose a basic gradient-based algorithm that transforms a polygonal collision-free path into a shorter one. While requiring only collision checking, and not any time-consuming obstacle distance computation nor geometry simplification, we constrain only part of the configuration variables that may cause a collision, and not entire configurations. Thus, parasite motions that are not useful for the problem resolution are reduced without any assumption. Experimental results include navigation and manipulation tasks, eg a manipulator arm-filling boxes and a PR2 robot working in a kitchen environment. Comparisons with a random shortcut optimizer and a partial shortcut have also been studied.     

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.     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.     

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。     

3D path planning for a biomass processing robot via motion simulation

Six motion strategies for a Cartesian 4-degrees-of-freedom (4-DOF) biomass processing robot were developed. Each of the corresponding trajectories consists of more than 1300 three-dimensional coordinate points. The motion strategies were evaluated due to their efficiency to select the most promising one for being embedded into the control system of the robot. For evaluation, an algorithm was developed calculating different evaluation parameters such as total cycle time, covered distance and no-load time. The best strategy was able to perform the task within 23 h. The total covered distance within this time period amounts to 33 km. The efficiency of the motion strategy in terms of load- and no-load time phases was ε=57% and has to be enhanced during further research. By doubling maximum velocity to 60 m min⁻¹ in longitudinal direction (x) and to 20 m min⁻¹ in transverse direction (y) the total cycle time would be reduced additionally by maximum 20%.     

XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot

A Reinforcement Learning (RL) algorithm based on eXtended Classifier System (XCS) is used to navigate a spherical robot. Traditional motion planning strategies rely on pre-planned optimal trajectories and feedback control techniques. The proposed learning agent approach enjoys a direct model-free methodology that enables the robot to function in dynamic and/or partially observable environments. The agent uses a set of guard-action rules that determines the motion inputs at each step. Using a number of control inputs (actions) and the developed RL scheme, the agent learns to make near-optimal moves in response to the incoming position/orientation signals. The proposed method employs an improved variant of the XCS as its learning agent. Results of several simulated experiments for the spherical robot show that this approach is capable of planning a near-optimal path to a predefined target from any given position/orientation.     

Whole-body motion planning and control of a quadruped robot for challenging terrain

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event-based state switching of the quadruped robot legs. And an on-line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad-144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.