Cooperative Control for Target Tracking with Onboard Sensing

Abstract We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not as-sume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into ac-count the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. It reasons about multiple possi-ble sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations show the performance of our approach and prove its flexibility to find suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment

Terrain Relative Navigation for Planetary Landing using Stereo Vision: Measurements Obtained from Hazard Mapping

As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model.Astrodynamics & Space Mission

Rough terrain motion planning for actuated, tracked robots

Traversing challenging structures like boulders, rubble, stairs and steps, mobile robots need a special level of mobility. Robots with reconfigurable chassis are able to alter their configuration to overcome such structures. This paper presents a two-stage motion planning scheme for reconfigurable robots in rough terrain. First, we consider the robots operating limits rather than the complete states to quickly find an initial path in a low dimensional space. Second, we identify path segments which lead through rough areas of the environment and refine those segments using the entire robot state including the actuator configurations. We present a roadmap and a RRT* method to perform the path refinement. Our algorithm does not rely on any detailed structure/terrain categorization or on any predefined motion sequences. Hence, our planner can be applied to urban structures, like stairs, as well as rough unstructured environments