Jiatong Bao

Dynamic hand gesture recognition based on SURF tracking

A novel method of dynamic hand gesture recognition based on Speeded Up Robust Features (SURF) tracking is proposed. The main characteristic is that the dominant movement direction of matched SURF points in adjacent frames is used to help describing a hand trajectory without detecting and segmenting the hand region. The dynamic hand gesture is then modeled by a series of trajectory direction data streams after time warping. Accordingly, the data stream clustering method based on correlation analysis is developed to recognize a dynamic hand gesture and to speed up calculation. The proposed algorithm is tested on 26 alphabetical hand gestures and yields a satisfactory recognition rate which is 87.1% on the training set and 84.6% on the testing set.

Dynamic Hand Gesture Recognition Based on SURF Tracking: Dynamic Hand Gesture Recognition Based on SURF Tracking

A novel method of dynamic hand gesture recognition based on Speeded Up Robust Features (SURF) tracking is proposed. The main characteristic is that the dominant movement direction of matched SURF points in adjacent frames is used to help describing a hand trajectory without detecting and segmenting the hand region. The dynamic hand gesture is then modeled by a series of trajectory direction data streams after time warping. Accordingly, the data stream clustering method based on correlation analysis is developed to recognize a dynamic hand gesture and to speed up calculation. The proposed algorithm is tested on 26 alphabetical hand gestures and yields a satisfactory recognition rate which is 87.1% on the training set and 84.6% on the testing set.

Research on Centroid Position for Stairs Climbing Stability of Search and Rescue Robot

This paper represents the relationship between the stability of stairs climbing and the centroid position of the search and rescue robot. The robot system is considered as a mass point- plane model and the kinematics features are analyzed to find the relationship between centroid position and the maximal pitch angle of stairs the robot could climb up. A computable function about this relationship is given in this paper. During the stairs climbing, there is a maximal stability-keeping angle depends on the centroid position and the pitch angle of stairs, and the numerical formula is developed about the relationship between the maximal stability-keeping angle and the centroid position and pitch angle of stairs. The experiment demonstrates the trustworthy and correction of the method in the paper.

A Combination of Terrain Prediction and Correction for Search and Rescue Robot Autonomous Navigation

This paper presents a novel two-step autonomous navigation method for search and rescue robot. The algorithm based on the vision is proposed for terrain identification to give a prediction of the safest path with the support vector regression machine (SVRM) trained off-line with the texture feature and color features. And correction algorithm of the prediction based the vibration information is developed during the robot traveling, using the judgment function given in the paper. The region with fault prediction will be corrected with the real traversability value and be used to update the SVRM. The experiment demonstrates that this method could help the robot to find the optimal path and be protected from the trap brought from the error between prediction and the real environment.

Human-robot collaborative teleoperation system for semi-autonomous reconnaissance robot

Several key issues about the teleoperation system for semi-autonomous reconnaissance robot have been addressed. A human-robot collaborative semi-autonomous mobile robot architecture (SAMRA) which combine the complementary capabilities of both robots local autonomy and human intelligence is proposed firstly. Then, a novel autonomous mission executive mechanism based on macro behavior and a hybrid behavior coordination mechanism is brought out. Meanwhile, several types of intelligent behavior are defined briefly. The implement method of teleoperation system software based on multi-agent system is introduced in succession. Finally, a visualized human-robot interface with live video/audio, 3D drawing simulation, and graphic mission planner is presented. Experimental results demonstrate the autonomous mission executor is able to interpret and execute the planned mission specification successfully, and the human-robot collaborative teleoperation system for semi-autonomous reconnaissance robot has excellent property of telepresence, flexibility and robustness.

Optimal path planning in field based on traversability prediction for mobile robot

This paper presents a novel method on building relationship between the optimal path and the terrain traversability. some color and texture features are used for the input set to train a self learning function. The trained function is used for the traversability prediction. Considering the traveling smoothness of the field robot, the sub-regions with minimal original traversability is not the optimal path. The distance coefficient is suggested which is depending on the optimal subregion in the last searching row and the original traversability prediction is transformed to computed traversability prediction based on the distance coefficient. The pathes with different initial sub-regions is formed and the optimal path is picked up following the minimal sum of computed traversability prediction of all sub-regions in this path. And two experiments are shown and discussed to demonstrate the effectiveness and efficiency of the method mentioned in this paper.

Combining Vision Learning and Interaction for Mobile Robot Path Planning

This paper addresses the question of how to make a robot learn natural terrain selectively and use the knowledge to estimate the terrain for planning an optimal path. A scheme which combines vision learning and interaction is proposed. The vision learning module employs an online boosting learning algorithm to constantly receive and learn the terrain samples each of which comprise the visual features extracted from the sub terrain region image and the traversability measured by the onboard Inertia Measurement Unit (IMU). Using this knowledge, the robot could estimate the new terrains and search for the optimal path to travel using the particle swarm optimization method. To overcome the shortcoming that the robot could not understand the intricate environment exactly, the vision interaction method, which complements the robots capacity of terrain estimation with the human reasoning ability of path correction, is further applied. Experimental results show the effectiveness of the proposed method.

Designed and implementation of a semi-autonomous search robot

This paper describes a design and implementation for a semi-autonomous search robot, which could be extremely valuable as search platforms for the application in the situations which is involving hazard and dangerous circumstance such as virulent chemical material, radioactivation or earthquake relief. The robot has the novel structure with tracks. The Robot mechanism has been analysis, the flippers in front should help the robot climbing the stairs or some other obstacles. The relationship between the robots movement and the pulse measurement from the encoders has been developed. According to the task requirements, several kinds of sensors, such as ultrasonic sensor, infrared sensor, GPS receiver and so on, are on board to detect the environment where the robot locates on. A friendly human-robot interface is designed which is used to send the control commands to the robot from far off. Depending on the high efficient wireless transmission system, the robot could work leaving the operator 1500m in the field. A semi-autonomous on local control method has been adopted for the robot to integrate teleoperation and local autonomous navigation. Experiment results in the building and field show that the robot could fit the requirement of the scene and is a efficient tools for the people who need to be in dangerous environment.

Support Vector Machine Based Robotic Traversability Prediction with Vision Features

Abstract This paper presents a novel method on building relationship between the vision features of the terrain images and the terrain traversability which manifests the difficulty of field robot traveling across one terrain. Vision features of the image are extracted based on color and texture. The travesability is labeled with the relative vibration. The support vector machine regression method is adopted to build up the inner relationship between them. In order to avoid the over-learning during training, k-fold method is used and average mean square error is defined as the target minimized to get the optimal parameters based on parameter space grid method. For the traveling smoothness of field robot, the original traversability prediction is transformed to computed traversability prediction based on different initial sub-regions. The optimal path is given by minimizing the sum of computed traversability prediction of all sub-regions in each path. Three experiments are discussed to demonstrate the effec...