Simultaneous underwater visibility assessment, enhancement and improved stereo

Vision-based underwater navigation and obstacle avoidance demands robust computer vision algorithms, particularly for operation in turbid water with reduced visibility. This paper describes a novel method for the simultaneous underwater image quality assessment, visibility enhancement and disparity computation to increase stereo range resolution under dynamic, natural lighting and turbid conditions. The technique estimates the visibility properties from a sparse 3D map of the original degraded image using a physical underwater light attenuation model. Firstly, an iterated distance-adaptive image contrast enhancement enables a dense disparity computation and visibility estimation. Secondly, using a light attenuation model for ocean water, a color corrected stereo underwater image is obtained along with a visibility distance estimate. Experimental results in shallow, naturally lit, high-turbidity coastal environments show the proposed technique improves range estimation over the original images as well as image quality and color for habitat classification. Furthermore, the recursiveness and robustness of the technique allows implementation onboard an Autonomous Underwater Vehicle for improving navigation and obstacle avoidance performance.

Towards coordinated vision-based docking using an Autonomous Surface Vehicle

This paper describes the development and experimental evaluation of a novel vision-based Autonomous Surface Vehicle with the purpose of performing coordinated docking manoeuvres with a target, such as an Autonomous Underwater Vehicle, on the water’s surface. The system architecture integrates two small processor units; the first performs vehicle control and implements a virtual force obstacle avoidance and docking strategy, with the second performing vision-based target segmentation and tracking. Furthermore, the architecture utilises wireless sensor network technology allowing the vehicle to be observed by, and even integrated within an ad-hoc sensor network. The system performance is demonstrated through real-world experiments.

Issues in autonomous navigation of underground vehicles

This paper describes current research at the Australian Centre for Field Robotics (ACFR) in collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) within the Cooperative Research Centre (CRC) for Mining Technology and Equipment (CMTE) towards achieving autonomous navigation of underground vehicles, like a Load-Haul-Dump (LHD) truck. This work is being sponsored by the mining industry through the Australian Mineral Industries Research Association Limited (AMIRA). Robust and reliable autonomous navigation can only be realised by achieving high level tasks such as path-planning and obstacle avoidance. This requires determining the pose (position and orientation) of the vehicle at all times. A minimal infrastructure localisation algorithm that has been developed for this purpose is outlined and the corresponding results are presented. Further research issues that are under investigation are also outlined briefly.

Evaluation of cooperative exploration strategies using full system simulation

This paper compares different state-of-the-art exploration strategies for teams of mobile robots exploring an unknown environment. The goal is to help in determining a best strategy for a given multi-robot scenario and optimization target. Experiments are done in a 2D-simulation environment with 5 robots that are equipped with a horizontal laser range finder. Required components like SLAM, path planning and obstacle avoidance of every robot are included in a full-system simulation. To evaluate different strategies the time to finish exploration, the number of measurements that have been integrated into the map and the development in size of the explored area over time are used. The results of extensive test runs on three environments with different characteristics show that simple strategies can perform fairly well in many situations but specialized strategies can improve performance with regards to their targeted evaluation measure.

空间机械臂在线实时避障路径规划研究

针对目前空间机械臂避障路径规划算法计算量大难以达到在线实时规划的缺点,对空间机械臂的在线实时避障路径规划问题进行了研究和探讨.采用规则体的包络对障碍物进行建模,并借助C空间法的思想,把障碍物和机械臂映射到两个相互垂直的平面内,将机械臂工作空间的三维问题转化为二维问题,并结合二岔树逆向寻优的方法进行路径搜索,从而大大减少了计算量,达到了在线实时规划的要求.最后在空间机器人仿真系统上对其进行了仿真研究,验证了该方法的可行性.

Convex model predictive control for vehicular systems

In this work, the author presents a method called Convex Model Predictive Control (CMPC) to control systems whose states are elements of the rotation matrices SO(n) for n = 2, 3. This is done without charts or any local linearization, and instead is performed by operating over the orbitope of rotation matrices. This results in a novel model predictive control (MPC) scheme without the drawbacks associated with conventional linearization techniques such as slow computation time and local minima. Of particular emphasis is the application to aeronautical and vehicular systems, wherein the method removes many of the trigonometric terms associated with these systems’ state space equations. Furthermore, the method is shown to be compatible with many existing variants of MPC, including obstacle avoidance via Mixed Integer Linear Programming (MILP).

Stall warning by blade pressure signature analysis

At low mass flow rates axial compressors suffer from flow instabilities leading to stall and surge. The inception process of these instabilities has been widely researched in the past - primarily with the aim of predicting or averting stall onset. In recent times, attention has shifted to conditions well before stall and has focussed on the level of irregularity in the blade passing signature in the rotor tip region. In general, this irregularity increases in intensity as the flow rate through the compressor is reduced. Attempts have been made to develop stall warning/avoidance procedures based on the level of the flow irregularity, but little effort has been made to characterise the irregularity, or to understand its underlying causes. Work on this project has revealed for the first time that the increase in irregularity in the blade passing signature is highly dependent on both tip-clearance and eccentricity. In a compressor with small, uniform, tip-clearance, the increase in blade passing irregularity which accompanies a reduction in flow rate will be modest. If the tip-clearance is enlarged, however, there will be a sharp rise in irregularity at all circumferential locations. In a compressor with eccentric tip-clearance, the increase in irregularity will only occur in the part of the annulus where the tip-clearance is largest, regardless of the average clearance level. In this paper, some attention is also given to the question of whether this irregularity observed in the pre-stall flow field is due to random turbulence, or to some form of coherent flow structure. Detailed flow measurements reveal that the latter is the case. From these findings, it is clear that a stall warning system based on blade passing signature irregularity will not be viable in an aero-engine where tip-clearance size and eccentricity change during each flight cycle and over the life of the compressor. Copyright © 2011 by ASME.

Stall Warning by Blade Pressure Signature Analysis

At low mass flow rates, axial compressors suffer from flow instabilities leading to stall and surge. The inception process of these instabilities has been widely researched in the past---primarily with the aim of predicting or averting stall onset. In recent times, attention has shifted to conditions well before stall and has focused on the level of irregularity in the blade passing signature in the rotor tip region. In general, the irregularity increases in intensity as the flow rate through the compressor is reduced. Attempts have been made to develop stall warning/avoidance procedures based on the level of flow irregularity, but little effort has been made to characterize the irregularity itself, or to understand its underlying cause. Work on this project has revealed for the first time that the increase in irregularity in the blade passing signature is highly dependent on both tip-clearance size and eccentricity. In a compressor with small, uniform, tip-clearance, the increase in blade passing irregularity that accompanies a reduction in flow rate will be modest. If the tip-clearance is enlarged, however, there will be a sharp rise in irregularity at all circumferential locations. In a compressor with eccentric tip-clearance, the increase in irregularity will only occur in the part of the annulus where the tip-clearance is largest, regardless of the average clearance level. In this paper, some attention is also given to the question of whether the irregularity observed in the prestall flow field is due to random turbulence or to some form of coherent flow structure. Detailed flow measurements reveal that the latter is the case. From these findings, it is clear that a stall warning system based on blade passing signature irregularity would be difficult to implement in an aero-engine where tip-clearance size and eccentricity change during each flight cycle and over the life of the compressor. © 2013 American Society of Mechanical Engineers.

Biologically inspired visual odometer for navigation of a flying robot

Experimental research in biology has uncovered a number of different ways in which flying insects use cues derived from optical flow for navigational purposes, such as safe landing, obstacle avoidance and dead reckoning. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of bees. Specifically, we focus on the mechanisms of course stabilization behavior and visually mediated odometer by using a biological model of motion detector for the purpose of long-range goal-directed navigation in 3D environment. The performance tests of the proposed navigation method are conducted by using a blimp-type flying robot platform in uncontrolled indoor environments. The result shows that the proposed mechanism can be used for goal-directed navigation. Further analysis is also conducted in order to enhance the navigation performance of autonomous aerial vehicles. © 2003 Elsevier B.V. All rights reserved.

一种移动机器人在三维动态环境下的路径规划方法

提出一种基于遗传算法的三维动态环境下的路径规划方法,通过对机器人的运动行为进行编码,将各种约束条件融入到遗传算法当中,规划出可实际应用的避障路径,仿真研究表明该方法是简单有效的。

动态多障碍物环境下目标追踪的路径规划方法

采用人工势场(AP)引导进化算法(EA)的搜索方法解决了动态多障碍物环境下的目标追踪问题.把人工势场与FA搜索相结合进行动态路径规划,使搜索沿着势场方向进行,得到一条相对于搜索区域最优的轨迹.以单车、单运动目标,有多个运动障碍物存在的情况为例,通过仿真试验对AP引导EA和单纯EA两种搜索方法的性能进行了比较,说明了AP引导EA搜索方法在处理动态多运动障碍物问题时相对传统EA方法具有实时、高效、收敛的特点,同时在很大程度上解决了单纯AP方法在解决多障碍物问题时的局部极小问题。

非完整自主车基于圆轨迹的道路避障

本文就四轮非完整自主车提出了一种基于圆轨迹的道路避障策略。先将道路上的障碍按照障碍距离自主车的远近划分层次,使一个层次的障碍能在自主车视场中全部出现。然后给出基于圆轨迹的避障算法,即自主车沿由自主车出发位姿和子目标点确定的圆弧轨迹走行。在此之前推导四轮非完整车的运动模型为提出避障策略的基础准备。尽量减小自主车在走行过程中运动状态的改变,基于圆轨迹避障策略能够很好地满足这一要求。最后引入代价函数,给出对于此方法的评价,体现了本方法的优越性。

冗余自由度机械手的避障控制

避障碍物一直是冗余自由度机械手的主要应用 ,本文采用伪逆矩阵法 ,以障碍物和机械手之间的距离的函数作为性能指标函数来解冗余自由度机械手逆解 ,进行避障控制 ,并提出一种简单的计算机械手和障碍物之间的距离方法 ,通过对一个三自由度的平面机械手进行仿真 ,验证了算法的正确性

移动机器人的障碍物群检测方法

利用激光和超声波传感器在用栅格表示法形成地图的基础上 ,提出了进行数据融合以提取环境特征的新方法 :识别障碍物群。该方法能够在密集障碍物环境中为机器人的路径规划和避障提供准确的环境特征信息 ,提高机器人系统的自主性和实时性。实验结果表明了该方法的有效性。

一种欠驱动AUV的三维实时避碰方法

针对以测距声纳为避碰传感器的一类欠驱动型AUV，提出了一种水平面和垂直面相结合的三维实时避碰方法。根据测距声纳和欠驱动AUV 的特殊性，首先从运动规划和路径规划2 个层次提出了AUV 混合型实时避碰结构，并分别设计了基于事件反馈监控的避碰自动机和基于免疫遗传的局部路径规划算法。多种典型障碍场景的半物理仿真实验表明，论文所提方法能够实现AUV 安全、稳定的三维避碰过程。