Integrated Aerial Platforms (IAPs), consisting of multiple interconnected aircraft, offer a promising framework for aerial manipulation tasks by enhancing localization accuracy and reliability. Unlike aerial swarms, the interconnected nature of IAPs allows for exploiting physical constraints among aircraft to improve positioning and navigation systems. In this paper, we present an advanced decentralized multi-aircraft visual-inertial-range-physical odometry system that considers the position, velocity, and attitude constraints inherent to IAPs. By tightly fusing visual-inertial-range odometry and Ultra-Wideband (UWB) with kinematic constraints, we optimize odometry accuracy through the use of novel constraint-based methods. Our algorithm’s performance is validated on simulated datasets and self-collected datasets, with flight experiments conducted on the IAP, demonstrating a significant improvement with a 28.7% reduction in drift over the baseline on real-world datasets and a 24.5% reduction on simulation datasets.

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 compliant control model based on reinforcement learning (RL) is proposed to allow robots to interact with the environment more effectively and autonomously execute force control tasks. The admittance model learns an optimal adjustment policy for interactions with the external environment using RL algorithms. The model combines energy consumption and trajectory tracking of the agent state using a cost function. Therein, an Unmanned Aerial Vehicle (UAV) can operate stably in unknown environments where interaction forces exist. Furthermore, the model ensures that the interaction process is safe, comfortable, and flexible while protecting the external structures of the UAV from damage. To evaluate the model performance, we verified the approach in a simulation environment using a UAV in three external force scenes. We also tested the model across different UAV platforms and various low-level control parameters, and the proposed approach provided the best results.