Sung-Gil Wee

Decentralized cooperative mean approach to collision avoidance for nonholonomic mobile robots

This paper presents a novel, decentralized, control-theoretic approach to address collision avoidance for multi-robot systems. We create a virtual obstacle at the mean position of the robots. A control is be designed such that each robot will avoid the closest obstacle when a collision is possible. The closest obstacle can be the virtual obstacle or the nearest robot. We present two such control laws. The first assumes perfect knowledge of the velocities of all nearby robots and can allow a saturated velocity input for each robot. In practice, the velocities of the other robots are hard to measure or estimate precisely. Therefore, the second control law removes the assumption of known velocities based on a high-gain, robust control scheme. We prove the first control scheme is globally asymptotically stable, and the robust control law is globally uniformly ultimately bounded. To verify the effectiveness of the proposed approach, Monte Carlo simulations and experiments have been conducted.

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.

Hybrid Path Planning of Multi-Robots for Path Deviation Prevention

Abstract: This paper suggests a hybrid path planning method of multi-robots, where a path deviation prevention for maintaining a specific formation is implemented by using repulsive function, A* algorithm and UKF (Unscented Kalman Filter). The repulsive function in potential field method is used to avoid collision among robots and obstacles. A* algorithm helps the robots to find optimal path. In addition, error estimation based on UKF guarantees small path deviation of each robot during navigation. The simulation results show that the swarm robots with designated formation successfully avoid obstacles and return to the assigned formation effectively. Keywords: swarm robots, collision avoidance, repulsive function, A*, path planning, UKF I. 서론 로봇 관련 기술이 발전함에 따라서 이종 센서를 가진 로봇들이 상호 협력하여 주어진 작업을 효율적으로 수행하는 경우가 증가하고 있다. 특히 국방 · 건설 · 탐사 · 보안 등의 분야에서 군집 로봇의 행동 제어기술, 상황인지 기술, 네트워킹 기술 등과 관련된 연구가 국내외에서 활발히 진행되고 있다[1-3]. 군집로봇은 개별 로봇의 지능과 정보를 공유하고 이를 바탕으로 보다 정확하게 주변 상황을 인지하여 적절한 판단을 통해 작업의 효율성을 높일 뿐 아니라 분산연산처리를 통한 빠른 제어가 이루어진다. 또한 분산 제어, 센서 개발, 로봇간 충돌 회피, 경로계획 등 다양한 분야의 융합 기술이 군집로봇에 적용되고 있다[4-7]. 포텐셜 필드는 대형유지를 위한 군집로봇의 경로탐색과 충돌회피에 폭넓게 사용되고 있다[9]. 또한 A* 알고리즘은 최단 경로탐색을 위해 보편적으로 많이 사용되고 있다[13,14]. 그러나 대부분의 국지 경로계획 알고리즘은 local minima 문제라는 고유한 한계를 가지고 있다. 이러한 한계를 극복하기 위하여 Koditschek [8] 등은 Local minima free Potential Field 방법을 개발하였고, Chang [9] 등은 Potential Field 방법과 Voronoi Diagram 접근법에 기반한 경로계획 알고리즘을 제안하고 주행과 지도 작성 기능을 동시에 수행 가능한 Hybrid path planner를 제안하였다. 또한, Carpin [10] 등은 분산 군집로봇 시스템에서의 협업적 선두로봇 추종을 위한 대형유지 중의 돌발적 장애물 회피법을 제안하였다. 또한 군집로봇의 경로계획을 위해 군집로봇을 팀 레벨에서의 행동들과 각 로봇레벨에서의 행동들로 구분하여 정의하고 Situated automata에 기초한 접근법을 제안하였다. 본 논문에서는 다수의 로봇이 군집 형성을 통한 최단 경로 탐색 및 로봇과 장애물간, 로봇과 로봇간의 충돌회피기술을 접목하여군집이동시효율적인제어법을제안한다. 이를위하여 군집로봇내의 개개 로봇들이 스스로 상황을 판단하고 행동하여 최단경로로 목표점까지 도달하기 위해 A* 방법을 적용하고 로봇의 경로 이탈방지를 위한 UKF, 장애물 회피와 로봇간 충돌 방지를 위해 Potential Field의 repulsive 함수를 사용한 복합적 경로계획법을 제시한다.

Formation control based on virtual space configuration for multi-robot collective navigation

This paper presents a formation-control method based on virtual-space configuration for multi-robot, collective navigation. To maintain the configuration of a multiple-robot formation, each robot creates a virtual space composed of virtual robots around it, so that it can avoid collisions with, and keep a constant distance from, the other robots. In addition, this paper suggests a method by which follower robots might access the outer contour of the virtual space of the leader robot to determine their headings and to maintain a safe configuration of leader and follower robots.

Localization strategy based on multi-robot collaboration for indoor service robot applications

In this paper, we propose a multi-robot collaborative localization strategy for a group of mobile robots to enhance their formation-control and navigation performance even when the robots share only limited navigation information during indoor, multi-robot service applications.

Camera relative pose estimation for visual servoing using quaternions

Abstract We present a novel approach to estimate the rotation and translation between two camera views from a minimum of five matched points in the images. Our approach simultaneously recovers the 3D structure of the points up to a common scale factor, and is immune to a variety of problems that plague existing methods that are based on the Euclidean homography or Essential matrix. Methods based on homography only function when feature points are coplanar in 3D space. Methods based on the Essential matrix often lose accuracy as the translation between two camera views goes to zero or when points are coplanar. By recovering the rotation and translation independently using quaternions, our algorithm eschews the shortcomings of these methods. Moreover, we do not impose any constraints on the 3D configuration of the points (such as coplanar or non-coplanar constraints). Our method is particularly well-suited for Position-Based Visual Servoing (PBVS) applications. Investigations using both simulations and experiments validate the new method. Comparisons between the proposed algorithm and the existing algorithms establish that our algorithm is robust to noise. A Matlab implementation of our algorithm is available online and free.

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.