关于参数规划在自治智能移动式机器人中实际应用的探讨

机器人行走空间的复杂性决定了导航监控系统不仅为机器人提供行走路线,还要依据预先得到的先验数据与知识告诉机器人不同行走段的障碍分布、环境边界走向及约束程度,按照载体的几何形状,运动特性选择合适的速度、加速度,保证在安全的前提下最快完成行走.本文讨论了提取影响机器人当前行走的障碍、环境边界算法和不同行走速度段的划分与关键点抽取方法,讨论了各种速度段的分离、合并、高速行走段的插补和过短行走段的平滑滤除等等.

Visual Navigation: Constructing and Utilizing Simple Maps of an Indoor Environment

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

Invariant Information Local Sub-map Filter (IILSF) for Efficient Simultaneous Localisation and Mapping of Large Environments

This paper presents an Invariant Information Local Sub-map Filter (IILSF) as a technique for consistent Simultaneous Localisation and Mapping (SLAM) in a large environment. It harnesses the benefits of sub-map technique to improve the consistency and efficiency of Extended Kalman Filter (EKF) based SLAM. The IILSF makes use of invariant information obtained from estimated locations of features in independent sub-maps, instead of incorporating every observation directly into the global map. Then the global map is updated at regular intervals. Applying this technique to the EKF based SLAM algorithm: (a) reduces the computational complexity of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. Simulation results show that the method was able to accurately fuse local map observations to generate an efficient and consistent global map, in addition to significantly reducing computational cost and data association ambiguities.

Complex-order dynamics in hexapod locomotion

This paper studies the dynamics of foot–ground interaction in hexapod locomotion systems. For that objective the robot motion is characterized in terms of several locomotion variables and the ground is modelled through a non-linear spring-dashpot system, with parameters based on the studies of soil mechanics. Moreover, it is adopted an algorithm with foot-force feedback to control the robot locomotion. A set of model-based experiments reveals the influence of the locomotion velocity on the foot–ground transfer function, which presents complex-order dynamics.

High level methods for scene exploration

Virtual worlds exploration techniques are used in a wide variety of domains — from graph drawing to robot motion. This paper is dedicated to virtual world exploration techniques which have to help a human being to understand a 3D scene. An improved method of viewpoint quality estimation is presented in the paper, together with a new off-line method for automatic 3D scene exploration, based on a virtual camera. The automatic exploration method is working in two steps. In the first step, a set of “good” viewpoints is computed. The second step uses this set of points of view to compute a camera path around the scene. Finally, we define a notion of semantic distance between objects of the scene to improve the approach.

Dynamic Visual Servoing With Chaos Control for Redundant Robots

This paper presents a new dynamic visual control system for redundant robots with chaos compensation. In order to implement the visual servoing system, a new architecture is proposed that improves the system maintainability and traceability. Furthermore, high performance is obtained as a result of parallel execution of the different tasks that compose the architecture. The control component of the architecture implements a new visual servoing technique for resolving the redundancy at the acceleration level in order to guarantee the correct motion of both end-effector and joints. The controller generates the required torques for the tracking of image trajectories. However, in order to guarantee the applicability of this technique, a repetitive path tracked by the robot-end must produce a periodic joint motion. A chaos controller is integrated in the visual servoing system and the correct performance is observed in low and high velocities. Furthermore, a method to adjust the chaos controller is proposed and validated using a real three-link robot.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model

Improved joint control using a genetic algorithm for a humanoid robot

This paper describes experiments conducted in order to simultaneously tune 15 joints of a humanoid robot. Two Genetic Algorithm (GA) based tuning methods were developed and compared against a hand-tuned solution. The system was tuned in order to minimise tracking error while at the same time achieve smooth joint motion. Joint smoothness is crucial for the accurate calculation of online ZMP estimation, a prerequisite for a closedloop dynamically stable humanoid walking gait. Results in both simulation and on a real robot are presented, demonstrating the superior smoothness performance of the GA based methods.