Combined Aerial Cooperative Tethered Carrying and Path Planning for Quadrotors in Confined Environments

TL;DR

This work tackles the problem of planning collision-free paths for two tethered quadrotors carrying a rope-based payload in confined environments. It introduces a six-DOF formation state $X=[x,y,z,\phi_{yaw}, d, \theta_{form}]$ via a composition–decomposition transform and couples it with a dynamic rigid body $\mathbf{V}$ to enable rapid collision checks, then computes a path with an OMPL-based RRT that is decomposed into per-UAV waypoints for MPC execution. Key contributions include the compact formation representation, fast collision checking, and an end-to-end planning-and-execution pipeline that integrates catenary rope modeling, RRT planning, and per-UAV MPC control with synchronized trajectory execution. The approach is validated in a real indoor arena with tunnel-like and inclined-hole obstacles, illustrating the method’s potential for tethered payload transport in tight spaces and its relevance to firefighting and search-and-rescue tasks, with future work aimed at dynamic rope behavior and more accurate rope dynamics.

Abstract

In this article, a novel combined aerial cooperative tethered carrying and path planning framework is introduced with a special focus on applications in confined environments. The proposed work is aiming towards solving the path planning problem for the formation of two quadrotors, while having a rope hanging below them and passing through or around obstacles. A novel composition mechanism is proposed, which simplifies the degrees of freedom of the combined aerial system and expresses the corresponding states in a compact form. Given the state of the composition, a dynamic body is generated that encapsulates the quadrotors-rope system and makes the procedure of collision checking between the system and the environment more efficient. By utilizing the above two abstractions, an RRT path planning scheme is implemented and a collision-free path for the formation is generated. This path is decomposed back to the quadrotors' desired positions that are fed to the Model Predictive Controller (MPC) for each one. The efficiency of the proposed framework is experimentally evaluated.

Figures (12)

• Figure 1: Concept Representation. The formation is avoiding an obstacle while maintaining no-collision between the dynamic rigid body (green) and the obstacles.
• Figure 2: System Block Diagram
• Figure 3: Composition Modelling. Side View (a) and Top View (b) of the plane shows how $\theta_{form}, \ d$ and $\phi_{yaw}$ are calculated.
• Figure 4: Dynamic V rigid body. The top figure (a) shows the initial calculation in the 2D plane and the bottom one (b) the final result in the 3D space enclosing the formation
• Figure 5: State Validity Check