Motion Planning for a UAV with a Straight or Kinked Tether

Abstract

This paper develops and compares two motion planning algorithms for a tethered UAV with and without the possibility of the tether contacting the confined and cluttered environment. Tethered aerial vehicles have been studied due to their advantages such as power duration, stability, and safety. However, the disadvantages brought in by the extra tether have not been well investigated by the robotic locomotion community, especially when the tethered agent is locomoting in a non-free space occupied with obstacles. In this work, we propose two motion planning frameworks that (1) reduce the reachable configuration space by taking into account the tether and (2) deliberately plan (and relax) the contact point(s) of the tether with the environment and enable an equivalent reachable configuration space as the non-tethered counterpart would have. Both methods are tested on a physical robot, Fotokite Pro. With our approaches, tethered aerial vehicles could find their applications in confined and cluttered environments with obstacles as opposed to ideal free space, while still maintaining the advantages from the usage of a tether. The motion planning strategies are particularly suitable for marsupial heterogeneous robotic teams, such as visual servoing/assisting for another mobile, tele-operated primary robot.

Aerial Base Station Deployment in 6G Cellular Networks Using Tethered Drones: The Mobility and Endurance Tradeoff

“…The overall region within which the tUAV can hover is limited by the heights of the neighboring buildings. Motion planning techniques can be adopted to determine the reachable 3-dimensional (3D) locations for a given environment, as discussed in [11]. In the rest of this article, we will refer to this region as the hovering region.…”

A Multilevel Architecture for Autonomous UAVs

“…Given the nature of the optical sensors used, the drone can follow 3D trajectories with controlled velocity and acceleration. In the literature, most works employ motion capture systems (mocap) [6,7] for the development of indoor autonomous navigation algorithms [8]. The achievable accuracy of these systems is very high, although they can only work in environments suitably set up by external computers and fixed cameras that frame the drone.…”

“…In the scientific community, one of the topics of major interest in the UAV field concerns autonomous navigation based on computer vision [1,2]. Many authors have proposed motion capture systems (mocap) [3][4][5][6][7][8], as they guarantee the highest performance. Given the high precision that these systems are able to achieve, they are often used to compare different control techniques.…”

Explicit Motion Risk Representation

“…3 right shows that by taking different paths, reaching the goal may have different path-dependent risk. [14], [15] show that longer tether length and more number of contacts in path 1 will introduce more uncertainty, and therefore more risk (state-dependent risk for path 1 may be actually lower since the distance to closest obstacle and visibility are larger). Tether length and number of contacts are also normalized using the potential maximum values given the map size and obstacle density at hand (here, 20 unit length for tether length and 10 for number of contacts).…”