1、中文 2862 字 Active Pedestrian Following Using Laser Range Finder 1. INTRODUCTION The ability of robots to track and follow moving targets is essential to many real life applications such as museum guidance, office or library assistance. On top of being able to track the pedestrians, one aspect of huma
2、n-robot interaction is robots ability to follow a pedestrian target in an indoor environment. There are various scenarios where the robot can be given instructions such as holding books in a library or carrying groceries at a store while following the pedestrian target. The key components of moving
3、target following technique include Simultaneous Localization and Mapping(SLAM), Detecting and Tracking Moving Objects (DATMO),and motion planning. More often than not, the robots are required to operate in dynamic environments where there are multiple pedestrians and obstacles in the surroundings.Co
4、nsequently, tracking and following a specific target pedestrian become much more challenging. In other words, the following behaviors must be robust enough to deal with constant occlusions and obstacle avoidances. When designing the following algorithm, one intuitive approach is to set the target lo
5、cation as the destination for the robot. However, this approach can easily lead to losing the target because it does not react to the targets motion nor consider the visibility problem (since the target may be occluded by obstacles and become invisible). For achieving robust target following and tra
6、cking, the robot should have the intelligent to predict target motion and gather observations actively. In this paper, we propose a moving target following planner which is able to manage obstacle avoidance and target visibility problems. Experimental results are shown to compare the intuitive appro
7、ach with our approach and prove the importance of active information gathering in planning.This paper is organized as follow: Section II discusses related works of DATMO and planning algorithms. Section III describes our DATMO system and introduces our target following planner. Lastly, Section IV il
8、lustrates the experimental results. 2. RELATED WORKS There are various approaches to detect and track moving objects such as building static and dynamic occupancy grid maps proposed by Wolf & Sukhatme 1, finding local minima in the laser scan as in Horiuchi et al.s work 2 or using machine learning m
9、ethods in Spinello et al.s work 3. Most of DATMO approaches assume that the robot is stationary or has perfect odometry. When tracking moving objects using mobile robots, it has been proven in Wang et al.s work 4, that SLAM and DATMO can be done simultaneously if the measurements can be divided into
10、 static and dynamic parts. In our work, we implement a DATMO algorithm which is similar to the one in Montesano et al.s work 5. A scan matching method is used to correct robot odometry and moving points are detected by maintaining a local occupancy grid map. Moreover, Extended Kalman Filter(EKF) is
11、applied to track the moving objects. This paper aims to solve moving target following problem with the existence of obstacles. For navigating in static environments, there are many successful works such as Fox et al. 6, Ulrich & Borenstein 7 Minguez & Montano 8, Seder & Petrovic 9. However, those me
12、thods are designed to reach a fixed goal and assume that the environment and robot states are fully-observable. Applying traditional obstacle avoidance algorithms on the target following task can fail easily because a moving target can change its speed and moving direction at anytime and the target
13、can be occluded by obstacles. For planning under imperfect perception, Partially-Observable Markov Decision Process (POMDP) provides a general framework. However, using POMDP to compute optimal policies are usually very computationally expensive because it has to compute a plan over belief space (ty
14、pically N-1 dimensional for an N-state problem.). For applying POMDPs on practical problems, most works aimed on reducing the dimension of belief space. Such like PBVI Pineau et al. 10, AMDP Roy et al. 11 and MOMDP Sylvie et al. 12. Those methods are much faster than original POMDP, but their comput
15、ational complexity are still too high to do real-time planning. Our method in this paper is more like a sub-optimal but fast approach: assuming that the robot always receives the most possible observations and plan a path which can reach the goal and minimize the uncertainty on particular states. Fo
16、r example, in Prentice & Roy 13, the robot aims on reaching the goal with minimum uncertainty on its position, so it may choose a path which is long but has enough localization landmarks. In this paper, we propose a motion planner for moving target following. The planner uses an extension of dynamic
17、 window approach propsed by Chou & Lian 14 to find collision-free velocities and choose a proper velocity using heuristic search. Cost functions are designed for minimizing the distance between robot and target and maximize the possibility that the robot can keep observing the target in a fixed time
18、 horizon. Additionally, we apply the concept in nearness diagram algorithm such as the one in Minguez &Montanos work 8 for computing a better estimation of the distance between robot and target and therefore achieve a smooth,non-hesitating performance. 3. MOVING TARGET FOLLOWING AND OBSTACLE AVOIDAN
19、CE It is essential that our DATMO system is able to track the moving target, pedestrian in this case, with great accuracy.The more precise pedestrian location acquired, the better the robot performs when following the target. A Detecting and Tracking Moving Objects For detecting moving points in the
20、 laser data, we adopt the concept of occupancy grid map proposed by Wolf &Sukhatme 1. A local occupancy grid map is aintained and used to differentiate the moving points. For robot localization, a scan matching technique called Iterated Closest Point (ICP) is used to acquire robot pose. The moving p
21、oints are filtered out of the data prior to scan matching in order to maintain the pose accuracy. The detected moving points are segmented into numerous clusters and determined if they belong to a pedestrian using features such as motion velocity, local minima, and size. Finally, each of the pedestr
22、ian is trackedusing Extended Kalman Filter (EKF) which solves the occlusion problem and provides us the computational advantage in real time performance. B. Following Method Our goal is to accomplish moving target following with the existence of obstacles. A reasonable solution is to see it as a pat
23、h planning problem and use obstacle avoidance algorithms to find collision-free actions. In this paper, we apply our previous work, DWA*, Chou et al. 10 as the obstacle avoidance algorithm. The procedure of DWA* is shown in Fig. 1 (a), it is a trajectory-rollout algorithm. The right side of Fig. 1 (
24、a) shows the procedure for computing proper motion commands. First,the environment information is realized as interval configuration for faster processing. Each interval value represents the maximum distance can be raveled by the robot on a certain circular trajectory. Second, the intervals are clus
25、tered as navigable areas. Third, for each area, a candidate velocity is determined according to an objective function. For each candidate velocity, a new robot position is computed as a new node and saved in a trajectory tree. Then a node with the minimum estimated cost value will be extracted as the base node for generating new nodes. The procedure is repeated until goal location is expanded or the tree depth reaches a certain value.
