Fu Mengyin

Research on route planning and map-matching in vehicle GPS/dead-reckoning/electronic map integrated navigation system

Integrated navigation system is becoming the trend of land vehicle navigation system. Therefore it has great meaning to study the key technologies involved, especially the technologies relative to electronic map. Based on these, combining with the practical vehicleGPS/dead-reckoning/electronic-map integrated navigation system, two problems relative to electronic map are emphatically introduced in this paper. One is an improved map-matching algorithm based on pattern recognition, because it integrates the advantages of map-matching algorithm based on projection and the advantages of map-matching algorithm based on pattern recognition, therefore this algorithm is very fast and efficient. Another is a fast route planning algorithm based on minimum cross angle, as it adopts a special heuristic search method, therefore it greatly reduces the time complexity and the space complexity of the algorithm execution.

Moving object detection under dynamic background in 3D range data

We proposed an unsupervised algorithm to extract profile features and detect moving object under dynamic background in 3D range Data. Moving object detection under dynamic background has become an increasingly popular research topic in mobile robotics. For the characteristics of dynamic background scene, we proposed an online unsupervised moving object detection algorithm, based on Gaussian Mixture Models and Motion Compensation. Furthermore, we did the work of clustering and identifying of the targets. In order to improve the robustness of the algorithm, we used a tracker to track the results of the detection. At last, experimental results on real laser data depicting urban and rural scenes under static and dynamic background are presented.

Semantic motion segmentation for urban dynamic scene understanding

A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status for each pixel of images. First, we extract and match sparse image features to estimate ego-motion between two consecutive stereo images, the result of feature points grouping is used to segment moving object in U-disparity map. Second, a Fully Convolutional Neural Network is employed for semantic segmentation. Moreover, semantic cues are utilized to remove pixels have no potential to be moved in motion mask. Finally, we use a fully connected CRF to integrate motion into semantic segmentation. To validate the effectiveness of the proposed algorithm, we present experimental results with KITTI stereo images that contain moving objects.

Design, modeling and control of a novel amphibious robot with dual-swing-legs propulsion mechanism

This paper describes a novel amphibious robot, which adopts a dual-swing-legs propulsion mechanism, proposing a new locomotion mode. The robot is called FroBot, since its structure and locomotion are similar to frogs. Our inspiration comes from the frog scooter and breaststroke. Based on its swing leg mechanism, an unusual universal wheel structure is used to generate propulsion on land, while a pair of flexible caudal fins functions like the foot flippers of a frog to generate similar propulsion underwater. On the basis of the prototype design and the dynamic model of the robot, some locomotion control simulations and experiments were conducted for the purpose of adjusting the parameters that affect the propulsion of the robot. Finally, a series of underwater experiments were performed to verify the design feasibility of FroBot and the rationality of the control algorithm.

A framework of traffic lights detection, tracking and recognition based on motion models

Detection of traffic lights is a basic technology for autonomous vehicle and driver assistant system. This paper presents a framework of detection, tracking, classification and online mapping using the images captured by a camera mounted on the vehicle and the position and attitude information from GPS/INS. The sequential results of detection, which is treated as observations with uncertainty, are associated with the targets in previous frame. The results of association are filtered and classified. In addition, the target position in the image is predicted based on a novel motion model and aided by a online mapping module that provides the model with 3D location information. The precise motion model significantly improves the performance of the association. The prediction algorithm based on our motion model is evaluated and compared with the other methods.

Control Law Design of Mobile Robot Trajectory Tracking and Development of Simulation Platform

Autonomous steering control of wheeled skid-steer mobile robot is focused on. According to dynamic analysis on the robot motion, kinematic constrains of the robot motion is put forward. As uncertain control factors exist during the robot running state, a novel fuzzy control algorithm is proposed. Based on ATRV2 mobile robot and its running environment information, using random number sequence, power spectra density function and virtual prototype technology, ADAMS and MATLAB co-simulation platform is built up, and the robot simulation running experiment is performed in the environment. At the same time, the simulation results show that the fuzzy control algorithm is robust and effective for the mobile robot control.

Lane recognition self-learning scheme of mobile robot based on integrated perception system

In this paper, a kind of integrated perception system for mobile robot is presented, which consists of 3D Lidar, 2D camera and their spatial registration. Based on the system and support vector machine (SVM), a self-supervised learning scheme between 3D point cloud data and 2D image data has been established, which can identify the traversable lane in driving environments through data association and parameters training. With this approach, vision-based autonomous navigation can be achieved and its effectiveness has been verified by extensive robot experiments.

Moving base disturbance suppression method of rotary INS based on rotation angular rate

Rotation modulation is an effective method to suppress the errors of the inertial navigation system (INS). However, when the carrier is in angular motion status, the effect of error suppression will be influenced. The influence is obvious especially when the carriers heading angle changes largely. A parameter named moving-base rotation modulation (MRM) coefficient is proposed, which is used to analyze the relationship between the angular rate of rotation modulation and the effect of rotation modulation. According to the analysis result, when the rotary INS works in the rotation angular rate range with a smaller MRM coefficient, the disturbance to error compensation from the carriers motion will be decreased, so that the precision of INS will be improved. This method is simple to realize and without extra system complexity, which is practical in engineering application.

Two-stage strong tracking filter with its application in transfer alignment

This paper deals with the transfer alignment problem of strapdown inertial navigation systems, using the velocity information and angular rate information of the ship. Major error sources for velocity-plus-angular rate matching is unmodeled ship body flexure dynamics. To reduce this alignment error, a two-stage strong tracking filter (TSTF) is designed. This TSTF is derived by merging the optimal two-stage Kalman filter with strong tracking filter. The ship body flexure is modeled as time-varying noise and its stochastic properties are estimated by TSTF. The simulation results show that the proposed method is effective to estimate the misalignment angles of the slave inertial navigation systems.

Control Laws Design and Validation of Autonomous Mobile Robot Off-Road Trajectory Tracking Based on ADAMS and MATLAB Co-Simulation Platform

Autonomous automobile technology is a rapidly developing field, with interest in both academia and industry. Outdoor navigation of autonomous vehicles, especially for roughterrain driving, has already been a new research focus. DARPA Grand Challenge and LAGR program stand for the top development level in this research region. Rough-terrain driving research offers a challenge that the in-vehicle control system must be able to handle rough and curvy roads, and quickly varying terrain types, such as gravel, loose sand, and mud puddles – while stably tracking trajectories between closely spaced hazards. The vehicle must be able to recover from large disturbances, without intervention (Gabriel, 2007). Since robotics autonomous navigation tasks in outdoor environment can be effectively performed by skid-steering vehicles, these vehicles are being widely used in military affairs, academia research, space exploration, and so on. In reference (Luca, 1999), a model-based nonlinear controller is designed, following the dynamic feedback linearization paradigm. In reference (J.T. Economou, 2000), the authors applied experimental results to enable Fuzzy Logic modelling of the vehicle-ground interactions in an integrated manner. These results illustrate the complexity of systematic modeling the ground conditions and the necessity of using two variables in identifying the surface properties. In reference (Edward, 2001), the authors described relevant rover safety and health issues and presents an approach to maintaining vehicle safety in a navigational context. Fuzzy logic approaches to reasoning about safe attitude and traction management are presented. In reference (D. Lhomme-Desages, 2006), the authors introduced a modelbased control for fast autonomous mobile robots on soft soils. This control strategy takes into account slip and skid effects to extend the mobility over planar, granular soils. Different from above researches, which control the robots on the tarmac, grass, sand, gravel or soil, this chapter focuses on the motion control for skid-steering vehicles on the bumpy and rocklike terrain, and presents novel and effective trajectory tracking control methods, including the longitudinal, lateral, and sensors pan-tilt control law. Furthermore, based on ADAMS&MATLAB co-simulation platform, iRobot ATRV2 is