Trajectory planning for human host tracking and following of slave mobile robot on service-related tasks

Summary form only given. People tracking and following has become an increasing popular topic in recent years. The ability of robots to track the people in the surroundings is essential to many real life applications such as museum guidance, office or library assistance. Another aspect of human-robot interaction is the robot´s ability to follow a human target. There are various scenarios where instructions, such as holding books in a library or carrying groceries at a store, are given to the robot when following the target host. Several references that provide more details on this video paper contribution can be found in [1: Chen et al. 2011] and [2: Chen et al. 2011]. The first paper focuses on detecting and tracking moving target in a dynamic environment. The second paper describes the proposed method “Trajectory Optimization” in great length and provides details of performance evaluation regarding robot maneuverability. Some of the important related work includes an architecture of moving object tracking similar to the one in our system being introduced in [3: Wang et al. 2007]. In the DATMO system, the widely used concept of occupancy grid map is adopted to distinguish static and dynamic objects which is similar to the work of [4: Wolf & Sukhatme 2004] Another key aspect of this video paper is regarding motion planning, there are many successful works such as [5: Fox et al. 1997], [6: Ulrich & Borenstein 2000], [7: Minguez & Montano 2004], [8: Seder & Petrovi´c 2007] of auto driving in static environments. However, those methods 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 directly can fail easily because a moving target can change its speed and moving direction at anytime and the target can be occluded by obstacles. In our work, we propose a motion planner for moving target following.- The planner uses an extension of dynamic window approach proposed in [9: Chou et al. 2009] and [10: Chou et al. 2011] to find the collision-free velocities and choose a proper velocity using the A* heuristic search. Proper cost functions are designed for minimizing the distance between the robot and the target and maximizing the possibility that the robot can keep observing the target in a fixed time horizon. Similar to the work of [11: Pomares et al. 2010] which uses a collision avoidance system in the human-robot cooperation, our path planner can also guarantee pedestrian safety and remains collision-free. Additionally, we apply the concept in nearness diagram algorithm proposed in [7: Minguez & Montano 2004] for computing a better estimation of the distance between robot and target and therefore achieve a smooth, non-hesitating performance. This video paper aims to solve tracking and following of a host person in indoor environments using laser range finder on a mobile robot for service tasks. In the past, many researchers have focused on improving robot localization in a complex area, or enhancing robot´s ability to detect and track a human target. There are also others who dedicate their attentions on path planning in an uncertain domain as mentioned earlier. However, each of these problems is often tackled and solved independently. This video paper proposes a complete system structure which consists of robot localization, tracking moving target, and path planning under uncertainty, all integrated together to achieve the purpose of human following. In the proposed system, a method called “Trajectory Optimization” is designed to integrate the DWA* navigation and the DATMO system. The proposed algorithm uses heuristic search to find a robot trajectory which can maximize target visibility and minimize the distance between robot and the target simultaneously. Compared to other similar works which often lacks the ability to avoid obstacles whi