Integrated aerial Platforms (IAPs), comprising multiple aircrafts, are typically fully actuated and hold significant potential for aerial manipulation tasks. Differing from a multiple aerial swarm, the aircrafts within the IAP are interconnected, presenting promising opportunities for enhancing localization. Incorporating the physical constraints of these multiple aircrafts to improve the accuracy and reliability of integrated aircraft positioning and navigation systems is a challenging yet highly significant problem. In this paper, we introduce a distributed multi-aircraft visual-inertial-range odometry system that analyzes the position, velocity, and attitude constraints within the IAP. Leveraging constraint relationships in the IAP, we propose corresponding methods that tightly fuse visual-inertial-range odometry and kinematic constraints to optimize odometry accuracy. Our system’s performance is validated using a collected dataset, resulting in a notable 28.7% reduction in drift compared to the baseline.

In this paper, we introduce PADUAV, a novel 5-DOF aerial platform designed to overcome the limitations of traditional tiltrotor vehicles. PADUAV features a unique mechanical design that incorporates two off-the-shelf quadrotors passively articulated to a rigid frame. This innovation enables free pitch rotation without mechanical constraints like cable winding, significantly enhancing its capabilities for various tasks. To control PADUAV’s 5 degrees of freedom, we propose a versatile and straightforward 5-DOF geometric tracking control strategy that generates 2D force and 3D torque. A decomposition approach is designed to distribute the output to the torque and thrust commands for each subplane, with no need for complex optimization. We validate our approach through three simulation experiments conducted in the Gazebo environment, leveraging the utilities provided by the RotorS simulator. These experiments not only demonstrate the feasibility of our platform but also provide new perspectives for future aerial platform development, particularly in terms of simulation-based approaches.

A solution for a tractor-trailer vehicle's generic maneuver planning task can be introduced by an optimal control problem (OCP). However, the curse of dimensionality is excited along with the OCP solution due to the collision-avoidance constraints in a large scale. The collision-avoidance conditions are weakened by simply constructing a corridor along a homotopically guiding route such that the vehicle's maneuvers are safely separated from obstacles. This approach is motivated by the safe flight corridor (SFC) applied for path planning of unmanned aerial vehicle (UAV). But SFC cannot be applied directly to the ground vehicle cases because a tractor-trailer vehicle cannot be modeled as a mass point in a narrow environment. An extension of the SFC is proposed, which requires different bodies of a multi-body vehicle to stay in different safe travel corridors. In this way, a reduced-scale OCP is formulated, and the problem scale becomes irrelevant to the environmental complexity. Simulation results illustrate that near-optimal maneuvers can be derived within less CPU time.

It is necessary to evaluate the safety of highly automated vehicles (HAVs) rigorously before their deployment on public roads. This paper describes a procedure to conduct behavioral competence tests for HAVs using the unprotected left-turn as the illustrating scenario. We first describe a model-based method for test case generation. Subsequently, we propose two methods for synchronizing the motions of the primary other vehicle (POV) and the vehicle under test (VUT), one using fixed speed profile, the other using model predictive control (MPC). Finally, we implement the POV algorithm for the left-turn scenario on an experimental vehicle, and conduct field tests using both virtual and real VUT in the Mcity test facility.

This paper is made up of a series of performance evaluations of computer vision algorithms, namely detectors and descriptors. The OpenCV 3.1 implementations of these algorithms were used for these evaluations. The main purpose behind these evaluations was to determine the best algorithms to use for a UAV guidance system.

This paper discusses the formation landing problem of quadrotor UAVs, which is considered as a UAV leader-follower problem, avoiding static obstacles. Rapidly-exploring random tree algorithm is used to generate the path for the leader UAV firstly. In particular, specifics of tree-grow including nodes selection, parent node connection, feasible and optimal path generation are explained. Given the leader UAV position, path finding for the follower UAV is conducted to avoid both static obstacles and the leader quadrotor. Based on the intensive simulations, which are conducted in ROS-Gazebo environment, the proposed framework is considered to be applicable in real-time formation landing of quadrotor UAVs.