室外自主移动机器人AMOR的导航技术

在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

Experimental validation and field performance metrics of a hybrid mobile robot mechanism

This paper presents the experimental validation and field testing of a novel hybrid mobile robot (HMR) system using a complete physical prototype. The mobile robot system consists of a hybrid mechanism whereby the locomotion platform and manipulator arm are designed as one entity to support both locomotion and manipulation symbiotically and interchangeably. The mechanical design is briefly described along with the related control hardware architecture based on an embedded onboard wireless communication network between the robot's subsystems, including distributed onboard power using Li‐ion batteries. The paper focuses on demonstrating through extensive experimental results the qualitative and quantitative field performance improvements of the mechanical design and how it significantly enhances mobile robot functionality in terms of the new operative locomotion and manipulation capabilities that it provides. In terms of traversing challenging obstacles, the robot was able to surmount cylindrical obstacles up to 0.6‐m diameter; cross ditches with at least 0.635‐m width; climb and descend step obstacles up to 0.7‐m height; and climb and descend stairs of different materials (wood, metal, concrete, plastic plaster, etc.), different stair riser and run sizes, and inclinations up to 60 deg. The robot also demonstrated the ability to manipulate objects up to 61 kg before and after flipping over, including pushing capacity of up to 61 kg when lifting objects from underneath. The above‐mentioned functions are critical in various challenging applications, such as search and rescue missions, military and police operations, and hazardous site inspections. © 2010 Wiley Periodicals, Inc.     

基于环境势场的移动机器人主动定位

提出一种以环境势场提供辅助线索的主动定位方法.首先建立以环境中各标志物为场源的势场模型;然后为机器人构造以窗口势函数极大值位置和环境势函数极大值位置为主体的运动目标候选集合,并以行为效用和行为代价构成评价函数,从中选出目标位置, 进而确定运动方向;最后根据机器人的运动能力和评价函数分别确定下一步的位移和观测方向.仿真结果验证了该方法的有效性.     

Navigating mobile robots with a modular neural architecture

Neural architectures have been proposed to navigate mobile robots within several environment definitions. In this paper a new neural modular constructive approach to navigate mobile robots in unknown environments is presented. The problem, in its basic form, consists of defining and executing a trajectory to a pre-defined goal while avoiding all obstacles, in an unknown environment. Some crucial issues arise when trying to solve this problem, such as an overflow of sensorial information and conflicting objectives. Most neural network (NN) approaches to this problem focus on a monolithic system, i.e., a system with only one neural network that receives and analyses all available information, resulting in conflicting training patterns, long training times and poor generalisation. The work presented in this article circumvents these problems by the use of a constructive modular NN. Navigation capabilities were proven with the NOMAD 200 mobile robot.     

Range measurements by a mobile robot using a navigation line

A continuous, straight-edged line is used for the visual navigation of an autonomous mobile robot in a factor environment. This line, which resides on the floor and contrasts with background, may also be used to determine range information. Two methods are developed for determining the range of an object in the sensor's field of view. The effects of various error conditions in the system geometry on each ranging method are determined. Equations are derived which yield the percent error in calculating ranges given estimates of these error conditions. Numerical examples using typical sensor parameters are given.     

Development of tethered autonomous mobile robot systems for field works

This paper describes the implementation details, advantages and potential applications of autonomous tethered mobile robot systems using the 'hyper-tether' concept. Hyper-tether is a new research area on tethered connections, which provide tethering among different mobile robot types, such as a robot with the environment and a robot with humans and animals. Its basic function is to actively control the tether's tension and/or length, but it also considers tether launching, anchoring, power delivery, data communication cabling and built-in trajectory command generation capabilities. Many of these features can be efficiently applied to build a tethered mobile robot system which remotely manipulates a working tool that can be useful for land-mine detection and removal, trimming of gardens and grass cutting of wide areas (e.g. golf courses, soccer and baseball fields), spraying of agricultural chemicals, forestry and construction works, etc. In this paper, a simple prototype of hypertether's winch-tether pair and a working tool equipped with a grass cutter was constructed, and basic experiments were performed to demonstrate the validity of the proposed system.     

State estimation and traversability map construction method of a quadruped robot on soft uneven terrain

Quadruped robots show excellent application prospects in complex environment detection and rescue. At present, scholars mainly focus on quadruped walking in rigid environments. However, quadruped robots often need to pass through uneven and soft unconstructed terrains, prone to slip and impact. The mismatch between the planned foothold position and the real one resulting from environmental uncertainties makes the robot unstable. In this paper, the state estimation and traversability map construction methods are proposed for quadruped robots to achieve stable walking in an unstructured environment, especially on soft terrains. First, the Error-state Kalman Filter (ErKF) is extended by optimizing the leg odometry information to get an accurate robot state, especially in soft, uneven terrain. The ErKF method fuses the sensor data from the inertial measurement unit, laser, camera, and leg odometry. The leg odometry is optimized by considering the foot slippage, which easily occurs in soft uneven terrains. Then, the unstructured environment is parameterized and modeled by the terrain inclination, roughness, height, and stiffness. A traversability map, which is essential for robot path and foothold planning in autonomous movement, is constructed with the above parameters. Finally, the proposed method is verified by simulation and experiments. The results show that the quadruped robot can walk stably on different soft and uneven terrains.     

Navigation control for a mobile robot

This article describes a navigation method of a mobile robot which uses a single camera and a guide mark. A travel path is instructed to the robot by means of path drawn on a monitor screen. The image of the guide mark provides information regarding the robot's position and heading direction. The heading direction is adjusted while moving if any deviation from the specified path is detected. The proposed method has been implemented in a mobile robot which runs at the average speed of 2.5 ft/s. without deviating more than one foot from the specified path in an indoor environment. © 1994 John Wiley & Sons, Inc.     

Fuzzy control of a mobile robot

This paper describes the design of a new fuzzy logic-based navigation algorithm for autonomous robots. This design effectively achieves correct environment modeling and noisy and uncertain sensory data processing on low-cost hardware equipment. A hierarchical control strategy is presented in which three different reactive behaviors are fused in a single control law by means of a fuzzy supervisor guaranteeing robot safety and task accomplishment. Due to the inherent transparency of fuzzy logic, the proposed algorithm is computationally light, easily reconfigurable, and well-performing in a wide range of differing operating conditions and environments     

Response time of a mobile robot

The response time of an autonomous mobile robot is presented as an internal performance index that characterizes a robot regardless of a particular task. The response time is defined as the time between the occurrence of an event, its detection by a robot, its recognition, the decision making, and the response. For fully autonomous robots and robots that are controlled by and interact with a human, analytical expressions for estimating the response time are presented. An analysis of the robotreconfiguration time when overcoming obstacles is presented. The analytical expressions are supported by numerical examples.     

Inverse kinematics of a mobile robot

The problem of kinematics is to describe the motion of the robotic system without consideration of the forces and torques causing the motion. This paper presents two methods to obtain the inverse kinematics of a mobile robot. In the first method, two rows of the forward kinematics are selected, the inverse of these two rows is obtained, and later the inverse matrix is combined with the third row of the forward kinematics. In the second method, the pseudo-inverse matrix of the forward kinematics matrix is obtained. The comparison result of the two proposed methods is presented. Two simulations show the effectiveness of the proposed inverse kinematics algorithm.     

Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot

Abstract

In this study, we propose a new robot system consisting of a mobile robot and a snake robot. The system works not only as a mobile manipulator but also as a multi-agent system by using the snake robot's ability to separate from the mobile robot. Initially, the snake robot is mounted on the mobile robot in the carrying mode. When an operator uses the snake robot as a manipulator, the robot changes to the manipulator mode. The operator can detach the snake robot from the mobile robot and command the snake robot to conduct lateral rolling motions. In this paper, we present the details of our robot and its performance in the World Robot Summit.     

New technique of mobile robot navigation using a hybrid adaptive fuzzy potential field approach

This paper presents a summary of the research aimed at developing a new reliable methodology for robot navigation and obstacle avoidance. This new approach is based on the artificial potential field (APF) method, which is used extensively for obstacle avoidance. The classical APF is dependent only on the separation distance between the robot and the surrounding obstacles. The new scheme introduces a variable, which is used to determine the importance that each obstacle has on the robot's future path. The importance variable is dependent on the obstacles position, both angle and distance, with respect to the robot. Simulation results are presented demonstrating the ability of the algorithm to perform successfully in simple environments.     

Spanning-tree based coverage of continuous areas by a mobile robot

This paper considers the problem of covering a continuous planar area by a square-shaped tool attached to a mobile robot. Using a tool-based approximation of the work-area, we present an algorithm that covers every point of the approximate area for tasks such as floor cleaning, lawn mowing, and field demining. The algorithm, called Spanning Tree Covering (STC), subdivides the work-area into disjoint cells corresponding to the square-shaped tool, then follows a spanning tree of the graph induced by the cells, while covering every point precisely once. We present and analyze three versions of the STC algorithm. The first version is off-line, where the robot has perfect apriori knowledge of its environment. The off-line STC algorithm computes an optimal covering path in linear time O(N), where N is the number of cells comprising the approximate area. The second version of STC is on-line, where the robot uses its sensors to detect obstacles and construct a spanning tree of the environment while covering the work-area. The on-line STC algorithm completes an optimal covering path in time O(N), but requires O(N) memory for its implementation. The third version of STC is “ant”-like. In this version, too, the robot has no apriori knowledge of the environment, but it may leave pheromone-like markers during the coverage process. The ant-like STC algorithm runs in time O(N), and requires only O(1) memory. Finally we present simulation results of the three STC algorithms, demonstrating their effectiveness in cases where the tool size is significantly smaller than the work-area characteristic dimension. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于混合势场法的移动机器人路径规划

针对目前移动机器人在路径规划中出现的问题,提出一种自主移动机器人路径规划的新方法——混合势场法。分析了人工势场法的不足,找出局部极小值点的形成原因;针对人工势场法中障碍物附近目标不可达问题,采用了在斥力场函数中加入斥力因子,使得机器人顺利到达目标点;针对陷入局部极小值和振荡的问题,提出了混合势场法,通过将势场法和可视图法结合起来,使得机器人走出局部极小值和振荡区域。最后,将混合势场法应用于室内移动机器人的路径规划中,仿真实验证明了该方法的有效性。     

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.     

基于改进人工势场法的移动机器人路径规划

针对势场法的障碍物附近目标不可达的问题,改进了传统人工势场斥力函数,确保目标点是机器人的势场全局最小点,使得机器人顺利到达目标点。针对势场法的局部最小值问题,提出了一种连接局部最小值区域障碍物的方法,建立了机器人离散传感器模型,使机器人快速走出局部最小值区域。改进后的人工势场法适用于复杂室内环境下的机器人路径规划。仿真结果证明了该方法的有效性。     

基于势场法的移动机器人路径规划仿真研究

针对势场法的障碍物附近目标不可达（GNRON）问题,采用改进斥力势场函数,把机器人和目标的相对距离考虑进去,从而确保目标点为整个势场的全局最小点,使得机器人能够顺利到达目标。针对局部极小引起的陷阱区域问题,提出了增加引导点的方法,使得机器人能够快速走出陷阱区域,向目标点移动。通过仿真实验,还实现了机器人在限定区域内漫游。改进后的势场法适用于复杂环境下的移动机器人路径规划。仿真结果证明了此方法的有效性。