In this article, we present a preliminary analysis of a heavy-lift airship carrying a payload through a cable-driven parallel robot. With unlimited access to isolated locations around the globe, heavy-lift airship enables affordable and safe delivery of heavy cargo thanks to its vertical takeoff and landing capabilities. By considering the airship and the cable-driven parallel robot as a combined system, the kinematic and dynamic models are developed. The choice of the proposed decentralized control structure is justified by the weak coupling of the two subsystems (i.e. airship and cable-driven parallel robot) which makes it possible to control the above two subsystems independently. A robust sliding mode control, capable of auto-piloting and controlling the airship, is developed. Furthermore, an inverse dynamic controller is applied to the cable-driven parallel robot in order to ensure loading and unloading phase. The feature of the proposed control system is that the coupled dynamics between the airship and the cable-driven parallel robot are explicitly incorporated into control system design, without any simplifying assumption. Numerical simulation results are presented and a stability analysis is provided to confirm the accuracy of our derivations.

This abstract is an overview of our research project entitled “Innovative Forest Plantation”, currently in progress. The aim of this project is to automate traditionally manual tasks for poplar plantations in the first years after planting, in particular mechanical weeding without the use of herbicides. The poplar forest is considered as a semi-structured environment where the dense canopy prevents the use of GPS signals and laser sensors are often preferred to localize the vehicle. In this paper, we focus on one of the main functionalities: autonomous navigation, which consists in detecting and locating trees to move safely in such complex environment. Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is very well-suited solution. The constructed map can be reliably used to plan semantic paths of the mobile robot in order treat specifically each tree. Simulations conducted on Gazebo and Robot Operation System (ROS) have proven that the robot could navigate autonomously in a poplar forest.