Auton Robot DOI 10.1007/s10514-015-9539-8 Active target search for high dimensional robotic systems Sina Radmard 1 · Elizabeth A. Croft 1 Received: 17 July 2015 / Accepted: 11 December 2015 © Springer Science+Business Media New York 2015 Abstract When a robotic visual servoing/tracking system loses sight of the target, the servo fails due to loss of input. To resolve this problem a search method, namely a lost target search (LTS) which will generate efficient actions to bring the target back into the camera field of view (FoV) as soon as possible, is required. For high dimensional platforms, like a camera-mounted manipulator or an eye-in-hand system, such a search must address the difficult challenge of gen- erating efficient actions in an online manner while avoiding kinematic constraints. In this work, we utilize the latest avail- able information from the target just prior to leaving the FoV to initiate an optimal online search. We explain various fea- tures of our overall LTS algorithm and provide simulation comparisons with common methods existing in the litera- ture. Finally, we implement and demonstrate the capabilities of our general algorithm on a laboratory scale 7 degree of freedom (DoF) eye-in-hand system tracking a fast moving target. Keywords Lost target search · High dimensional robot · Online sensor planning Electronic supplementary material The online version of this article (doi:10.1007/s10514-015-9539-8) contains supplementary material, which is available to authorized users. B Sina Radmard sradmard@interchange.ubc.ca Elizabeth A. Croft elizabeth.croft@ubc.ca 1 CARIS Laboratory, Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada 1 Introduction Having a camera as the main, or the only, feedback sen- sor of a robotic platform has become widely appealing in areas like surveillance, industrial manipulation, rescue robots, soccer robots, planetary rovers, etc. (Kragic and Vincze 2009). These various applications for surveillance, manipulation, rescue and servoing share the common task of object tracking. Such a task involves object detection, tra- jectory estimation and hardware manipulation, all of which depend on some visual cues of the target. In the context of a single mounted camera platform servo- ing to, or tracking, an object, most research has focused on maintaining the target within the camera FoV (Nelson and Khosla 1995; Chesi et al. 2003; Murrieta-Cid et al. 2005; Panagou and Kumar 2014). However, due to factors such as limited camera FoV, system constraints, occlusions, and poor lighting conditions, maintaining visibility is not always pos- sible. For example, during visual servoing in Kragi (2001), if the target leaves the FoV or is occluded, a search is launched only in the image space until the target is found or the time spent on searching passes a predefined time constraint. In other words, if the target does not reappear in a predictable and narrowly localized site, such algorithms will fail. The ability to robustly look for and find truly “lost” targets is a natural next step to improve robot autonomy in wide range of applications. In this paper we present a fast and efficient online planner—an adaptive planner that updates and replans as new information arrives—to search for a moving target when a high dimensional camera mounted robotic platform loses track of the moving object. Without loss of generality, we consider a camera-mounted manipulator—a 7-DoF eye-in- hand system—as our exemplar high dimensional robotic platform while the target moves in 3D as depicted in Fig. 1. 123