A novel extended potential field controller for use on aerial robots

Abstract

Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

Collision-Free MPC for Legged Robots in Static and Dynamic Scenes

“…Another approach is to augment the objective with a repulsive potential, typically proportional to the inverse square distance. The former is shown by [15] efficiently as a pairwise distance constraint, and the work in [16] exemplifies the latter together with a constraint on the relative velocity.…”

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

“…Despite reaching a low collision rate in simulation, the policy behaves erratically and cannot avoid obstacles or reach the goal. 3 https://youtu.be/CICWcLJ3aPs…”

“…1 All authors are with the Robotic Systems Lab, ETH Zürich, Switzerland dhoeller@ethz.ch 2 D. Hoeller is also with NVIDIA Digital Object Identifier (DOI): see top of this page. oncoming obstacles [2], [3], or have a reactive controller that directly integrates velocity measurements from time of flight sensors into the control formulation [4]. Another approach is to fit a velocity model on dynamic objects in the scene and use that information in the cost map of a trajectory optimizer [1], [5].…”

Automatic interpretation of unordered point cloud data for UAV navigation in construction

“…Data: A point cloud P Result: A list of clusters L 1 Init an empty queue Q; 2 for each point p i ∈ P do 3 if p i was not processed before then 4 Add it to Q; 5 Mark it as processed; 6 for each point q j ∈ Q do 7 Search for its neighbors within a sphere of radius r < ; 8 for each found neighbor q k j do 9 if q k j was not processed before then 10 Add it to Q; 11 Mark it as processed;…”