Yanyan Dai

Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control

In this paper, the problem of object caging and transporting is considered for multiple mobile robots. With the consideration of minimizing the number of robots and decreasing the rotation of the object, the proper points are calculated and assigned to the multiple mobile robots to allow them to form a symmetric caging formation. The caging formation guarantees that all of the Euclidean distances between any two adjacent robots are smaller than the minimal width of the polygonal object so that the object cannot escape. In order to avoid collision among robots, the parameter of the robots radius is utilized to design the caging formation, and the A⁎ algorithm is used so that mobile robots can move to the proper points. In order to avoid obstacles, the robots and the object are regarded as a rigid body to apply artificial potential field method. The fuzzy sliding mode control method is applied for tracking control of the nonholonomic mobile robots. Finally, the simulation and experimental results show that multiple mobile robots are able to cage and transport the polygonal object to the goal position, avoiding obstacles.

A switching formation strategy for obstacle avoidance of a multi-robot system based on robot priority model.

This paper describes a switching formation strategy for multi-robots with velocity constraints to avoid and cross obstacles. In the strategy, a leader robot plans a safe path using the geometric obstacle avoidance control method (GOACM). By calculating new desired distances and bearing angles with the leader robot, the follower robots switch into a safe formation. With considering collision avoidance, a novel robot priority model, based on the desired distance and bearing angle between the leader and follower robots, is designed during the obstacle avoidance process. The adaptive tracking control algorithm guarantees that the trajectory and velocity tracking errors converge to zero. To demonstrate the validity of the proposed methods, simulation and experiment results present that multi-robots effectively form and switch formation avoiding obstacles without collisions.

Symmetric caging formation for convex polygon object transportation by multiple mobile robots

In this paper, the problem of object caging and transporting is considered by multiple mobile robots using object closure technology. With the consideration of minimizing the number of the robots and decreasing the rotation of the object, the proper points are calculated and assigned to the multiple mobile robots to form the symmetric caging formation. The caging formation guarantees that all of the Euclidean distances between two adjacent robots are smaller than the minimal width of the polygon object and the object cannot escape. Finally, the simulation results represent multiple mobile robots which cage and transport the polygon object to the goal position.

Adaptive Formation Control and Collision Avoidance Using a Priority Strategy for Nonholonomic Mobile Robots

This paper presents four novel collision avoidance processes for nonholonomic mobile robots to generate effective collision-free trajectories when forming and maintaining a formation. A collision priority strategy integrates the static and dynamic collision priorities to avoid a collision efficiently and effectively. In addition, it minimizes the turning angle of the follower robot and decreases system computation time. When avoiding collisions between robots, a novel collision avoidance algorithm is used to find a safe waypoint for the robot, based on the velocity of each robot. An adaptive tracking control algorithm, using the Lyapunov analysis, guarantees that the robots trajectory and velocity tracking errors converge to zero considering parametric uncertainties of both the kinematic and dynamic models. The simulation and experiment results validate the effectiveness of the proposed method.

Leader-follower formation control of multi-robots by using a stable tracking control method

In this paper, the leader-waypoint-follower robot formation is constructed based on the relative motion states to form and maintain the formation of multi-robots by stable tracking control method. The main idea of this method is to find a reasonable target velocity and angular velocity to change the robots current state. The proposed Lyapunov functions prove that robots change current velocities to target velocities which we propose, in globally asymptotically stable mode. The simulation results based on the proposed approach show better performance in accuracy and efficiency comparing with EKF based approach which is applied in multiple robots system in common.

Leader-Follower Formation Control Based on Hybrid Formation Control Framework and Waypoint in Cone Method

This paper proposes a novel leader-follower robot formation based on relative motion states of robots. The follower robot calculates its waypoint by desired distance-angle d-¦Â or desired distance-distance d-d. Since the robots velocities are constrained, the proposed waypoint in cone method guarantees that the follower robot moves to its desired waypoint correctly. In order to form and maintain the formation of multiple robots, we use a stable tracking control method to control each robot. Finally, the simulation results based on the proposed approach show that the follower robot moves to its waypoint effectively to make a formation.

A switching formation strategy for obstacle avoidance of multi-robot system

This paper describes a switching formation strategy for multiple robots, in order to avoid an obstacle and cross obstacles. In the strategy, a leader robot plans a safe path using geometric obstacle avoidance control method (GOACM). By calculating a new desired distance and desired bearing angle with the leader robot, the follower robots follow the leader robot, in a safe switching formation. The adaptive controller guarantees that the robots trajectory and velocity tracking errors converge to zero with the consideration of the uncertainties in kinematic and dynamic models. The simulation results show the effectiveness of the proposed approaches.

Omni-directional Vision SLAM using a Motion Estimation Method based on Fisheye Image

Abstract: This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle’s feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous 360° panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps. Keywords: omnidirectional vision, vision slam, fish-eye lens, optical flow I. 서론 모든 로봇에게 자기의 위치와 주변 지도 작성의 방법은 로봇이 개발된 이후로 계속 되어온 로봇 연구자들의 고민 중에 하나이다. 로봇에 대한 연구가 시작된 이후 자기 위치 인식과 주변 지도 작성 즉, SLAM (Simultaneous Localization and Mapping)은 가장 핵심적인 연구 과제로서 많은 연구자들의 다양한 접근을 통한 연구를 바탕으로 EKF-SLAM, Fast SLAM등의 다양한 알고리즘과 레이저 스캐너 센서, 카메라 센서, 초음파 센서 등 다양한 센서에 적합한 알고리즘이 개발되어 왔다. 최근 로봇 시스템은 컴퓨팅 성능을 보장한다면 충분히 적은 오차로 자기 위치와 지도를 작성할 수 있게 되었다. 그러나 컴퓨팅 성능을 확보하기 힘든 모바일 로봇이나 의료 로봇에 대한 SLAM 연구는 아직 미비한 점이 있다. SLAM은 다양한 센서를 기반으로 연구되고 있는데 그 중에서도 레이저 스캐너 센서를 이용한 것과 카메라를 이용한 경우가 대표적이다. 기존에는 초음파나 IR 센서를 이용하여 특정 각도에 대한 장애물까지의 거리를 기반으로 하였으나 레이저 스캐너 센서가 활용이 되면서부터 분해능이 높아져서 세밀한 거리 정보를 바탕으로 주변 장애물 정보를 습득하게 되었다. 카메라를 이용한 경우는 사람이 대부분의 주변 정보를 시각을 통하여 얻는 것과 같이 로봇도 카메라를 이용하여 주변의 특징점 정보를 얻어 분석하여 자기 위치를 분석하게 된다. 카메라를 이용한 SLAM은 한 대를 사용하는 mono SLAM, 사람의 눈처럼 두 대를 사용하는 stereo SLAM, 실내 천장 정보를 이용하는 ceiling SLAM 등 다양한 대상으로 연구되었다. 이런 다양한 센서를 기반으로 얻은 주변 정보와 엔코더 정보를 바탕으로 한 odometry 정보를 변수로 적용한 Kalman filter에 대한 다양한 연구를 통해 SLAM의 정확도가 많이 상승하게 되었다. 그리고 최근에는 3D 레이저 센서나 RGB-D 카메라를 기반으로 한 3D SLAM에 대하여 많은 연구가 진행되고 있다. 특히 카메라를 이용한 다양한 형태의 VSLAM에서 단일 카메라를 이용한 mono SLAM 방식은 시스템이 단순하지만 단일 방향으로 정보를 얻기 때문에 주변 모든 정보를 획득하기 에는 오래 걸리는 단점을 가지고 있으며 스테레오 카메라 방식은 동일 특징점을 카메라를 2대에서 측정하여 3차원 깊이 정보를 구하기 때문에 주변의 3차원적 정보를 얻는 장점을 가지지만 데이터 처리량이 많은 단점을 가지고 있었으나 최근에는 FPGA를 사용함으로써 많이 해결되고 있지만 이를 활용하여 SLAM을 하기에는 위험이 존재한다. 전방향 카메라

Object recognition of a mobile robot based on SIFT with de-speckle filtering

This paper presents a novel object recognition method, of a mobile robot, by combining scale invariant feature transform (SIFT) and de-speckle filtering to enhance the recognition capability. The main idea of the proposed algorithm is to use SIFT programming to identify other robots after de-speckle filtering process to remove outside noise. Since a number of features are much larger than needed, SIFT method requires a long time to extract and match the features. The proposed method shows a faster and more efficient performance, which enhances localization accuracy of the slave robots. From the simulation results, the method using de-speckle filtering based SIFT shows that the number of features in the extraction process, and that the points in matching process are reduced.