外文文献翻译——基于激光测距仪的行人跟踪

Active PedestrianFollowing Using Laser RangeFinderI. INTRODUCTIONThe ability of robots to track and follow moving targetsis essentialto many real life applications such as museum guidance, office or library assistance.On top of being able to track the pedestrians,one aspectof human-robot interaction is robot 's ability to follow a pedestrian target in an indoor environment. There are various scenarioswhere the robot can be given instructions such as holding books in a library or carrying groceriesat a storewhile following the pedestriantarget.The key componentsof moving 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 pedestriansand obstacles in the surroundings.Consequently,tracking and following a specific target pedestrian become much more challenging. In other words, the following behaviors must be robust enoughto deal with constantocclusionsandobstacleavoidances.When designing the following algorithm, one intuitive approach is to set the target location as the destination for the robot. However, this approachcan easily lead to losing the target becauseit doesnot react to the target 'mo s tion nor consider the visibility problem (since the target may be occluded by obstacles and become invisible). For achieving robust target following and tracking, the robot should have the intelligent to predict targetmotion and gather observationsactively.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 approachwith our approachand prove the importance of active information gathering in planning.This paper is organized as follow: Section II discussesrelated works of DATMO and planning algorithms. Section III describes our DATMO system and introduces our target following planner. Lastly, Section IV illustrates the experimental results.II. RELATED WORKSThere are various approachesto detectand track moving objects suchas building static and dynamic occupancy grid maps proposedby Wolf & Sukhatme[1], finding local minima in the laser scan as in Horiuchi et al. 'w s ork [2] or using machine learning methods in Spinello et al. 'w s ork [3]. Most of DATMO approachesassume that the robot is stationary or has perfect odometry. When tracking moving objects using mobile robots, it has been proven in Wang et al. 'w s ork [4], that SLAM andDATMO can be done simultaneouslyif the measurementscan be divided into static anddynamic parts. In our work, we implement a DATMO algorithm which is similar to the one in Montesanoet al. 'w s ork [5]. A scan matching method is used to correct robot odometry andmoving points aredetectedby maintaining a local occupancygridmap.Moreover, ExtendedKalman Filter(EKF) is applied to track themoving objects.This paperaims to solve moving targetfollowing problem with the existenceof obstacles. For navigating in static environments, there are many successful works such as Fox et al. [6], Ulrich & Borenstein [7] Minguez & Montano [8], Seder & Petrovi '[9c]. However,thosemethodsaredesignedto 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 becausea moving targetcan changeits speedandmoving direction at anytime and the target 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 (typically 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 Pineauet al. [10], AMDP Roy et al. [11] and MOMDP Sylvie et al. [12]. Those methods are much faster than original POMDP, but their computational complexity are still too high to do real-time planning. Our method in this paper is more like asub-optimal but fast approach:assumingthat the robot always receivesthe most possible observationsand plan a path which can reachthe goal and minimize the uncertainty on particular states.For 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 hasenoughlocalization landmarks.In this paper, we propose a motion planner for moving target following. The planner usesan extensionof dynamic window approachpropsedby Chou & Lian [14] to find collision-free velocities and choose a proper velocity using heuristic search. Cost functions are designedfor minimizing the distancebetween robot and target and maximize the possibility that the robot can keepobserving the target in a fixed time horizon. Additionally, we apply the conceptin nearnessdiagram algorithm such asthe 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. III. MOVING TARGET FOLLOWING AND OBSTACLE AVOIDANCE It is essentialthat our DATMO systemis able to track the moving target, pedestrian in this case, with great accuracy.Themore precise pedestrianlocation acquired, the better the robot performs when following the target.A．Detecting and Tracking Moving ObjectsFor detecting moving points in the laser data, we adopt the conceptof 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, ascan matching techniquecalled Iterated Closest Point (ICP) is used to acquire robot pose. The moving points are filtered out of the data prior to scan matching in order to maintain the poseaccuracy.The detectedmoving points are segmentedinto numerous clusters and determinedif they belong to a pedestrianusing featuressuch as motion velocity, local minima, and size. Finally, each of the pedestrian is trackedusing Extended KalmanFilter (EKF) which solves the occlusion problem and provides us the computational advantagein real time performance.B. Following MethodOur goal is to accomplish moving target following with the existence of obstacles. A reasonable solution is to seeit as a path 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 avoidancealgorithm.The procedure of DWA* is shown in Fig. 1 (a), it is a trajectory-rollout algorithm. The right side of Fig. 1 (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 clusteredas navigable areas.Third, for each area,a candidatevelocity is determined according to an objective function. For each candidate velocity, a new robot position is computed as a new node and savedin 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 expandedor the treedepth reachesa certain value. After the tree expansionstops,the deepestnode is determinedas a temporal goal, and the presentcandidate velocity which can lead the robot to thetemporal goal is selected.In our work, we used two different methods for integrating DATMO and DWA*: pseudogoal methodand trajectory optimization method. Pseudo-goalmethod is a more intuitive approach which does not consider the target velocity when following. Another approach is trajectory-optimization where a trajectory-rollout controller is used to approximate a predicted target trajectory and maximize target visibility.Their performanceswill be shownand comparedin the next section.1) Pseudo-goalmethodWhen implementing people following algorithm, an intuitive approachis setting thepresentlocation of the moving target asthe goal of navigation algorithm. Consider the tracked target at an angle with respectto the robot, DWA* algorithm will generate an angular and translational velocity which producesan arc-like trajectory. If the goal is within a closeproximity of the robot, the angularvelocity will besmall andthe rc- like route often resultsin anindirect or detour path for the robot to reachthe goal (as shown in Fig.2). On the contrary,if the goal is set further away from the robot at the same angle, any movement of the goal will result in a much larger displacement changecompareto when the goal is close to the robot. Therefore,the angularvelocity would increase in response to the substantial change of the goal location.Consequently,the trajectory becomesmore direct toward the goal.In the pseudo-goal method, the space in front of the robot is divided into 7 trajectoriesat 35_ , 55_, 70_ , 90_ ,110_, 130_ , 145_ asshown in Fig. 3. A pseudo goal is set 3 times the original distance between the goal and robot along each trajectory. After acquiring the pedestrian location (red circle in Fig. 3), the pseudo goal with trajectory closest to the pedestrianlocation is then selected.By setting the goal further away from the robot, it remediesthe issue of small angular velocity and provides a much more direct path to reachthe goal.Another problem is the limited sensing ability. In thispaper, the algorithm is implemented on a mobile robot with a 180-degree POV LRF. Here we added an “informationgathering mode.”When the moving target entersthe “dangerouzsone, ” (0~35 degreeand 145~180 degreein the robot coordinate) the robot will stop and turn to the target direction rapidly. Adding this mode greatly lowers the risk of losing the target.Pseudo-goalmethod is straight-forward to implement andperforms well when the target velocity is nearly constant.However, when the target velocity changes, the pseudo-goaltrajectory selected may also alter from oneto another and causesthe robot to changeits moving direction drastically.2) Trajectory optimization methodThough the pseudo-goalmethod is very simple to implement, it can easily lose track of the target because it does not take the robot 'li s mited observability into consideration.Anotherdrawback is that the pseudo-goalmethod can'r t eact quickly to the change of target velocity. A better method is to plan according to a reference trajectory but not a fixed location. In this way, target velocity can be consideredand the robot observability can be consideredby adding terms to the cost function.附录二外文翻译（中文）基于激光测距仪的行人跟踪一、引言在现实生活中机器人跟踪运动目标的能力得到广泛的应用，如博物馆导引，办公室或者图书馆帮助。