Toward autonomous avian-inspired grasping for micro aerial vehicles

Abstract

Abstract. Micro Aerial Vehicles, particularly quadrotors, have been used in a wide range of applications. However, the literature on aerial manipulation and grasping is limited and the work is based on quasi-static models. In this paper, we draw inspiration from agile, fast-moving birds such as raptors, that are able to capture moving prey on the ground or in water, and develop similar capabilities for quadrotors. We address dynamic grasping, an approach to prehensile grasping in which the dynamics of the robot and its gripper are significant and must be explicitly modeled and controlled for successful execution. Dynamic grasping is relevant for fast pick-and-place operations, transportation and delivery of objects, and placing or retrieving sensors. We show how this capability can be realized (a) using a motion capture system and (b) without external sensors relying only on onboard sensors. In both cases we describe the dynamic model, and trajectory planning and control algorithms. In particular, we present a methodology for flying and grasping a cylindrical object using feedback from a monocular camera and an Inertial Measurement Unit onboard the aerial robot. This is accomplished by mapping the dynamics of the quadrotor to a level virtual image plane, which in turn enables dynamically-feasible trajectory planning for image features in the image space, and a vision-based controller with guaranteed convergence properties. We also present experimental results obtained with a quadrotor equipped with an articulated gripper to illustrate both approaches. ‡

Energetics in robotic flight at small scales

“…This can be achieved by using light-weight manufacturing materials [58], electronics 2 and sensors (such as cameras). In addition, careful component selection and system design [59,60] can help further.…”

“…(Localization is provided by a motion capture camera system.) If onboard state estimation via cameras is needed, then a 72 g Odroid-xu3 microcomputer can be used instead [2]. Current ongoing work in our group demonstrates that fully autonomous operation (including localization and mapping) can be performed by employing a 450 g (including the case, SSD, and RAM) Intel NUC mini PC (http://www.intel.com/content/www/us/en/nuc/ nuc-kit-nuc5i7ryh.html).…”

“…2 For example, the Intel NUC mini PC: http://www.intel.com/content/www/us/en/nuc/nuc-kit-nuc5i7ryh.html. 3 Consider for instance recent developments in LiDARs, which are critical for mapping, localization and autonomous navigation and obstacle avoidance.…”

Visual Servoing of Quadrotors for Perching by Hanging From Cylindrical Objects

“…Recently, a Port-Hamiltonian approach has been used to formulate a general IBVS approach [15], however an improvement in accuracy over traditional IBVS approaches is not demonstrated. Further, works such as [16]- [19] use a backstepping approach for control, while [20] and [21] use a geometric-based control approach. However, with backstepping, it is necessary to assume that the inner control loops are significantly faster than the outer ones.…”

“…Grasping of cylindrical objects is considered in [20] and [21], where the cylinder's axis is assumed to be perpendicular to gravity and an external motion capture system is used (in addition to onboard sensing) to control the motion along the cylinder axis.…”