Interleaved Predictive Control and Planning for an Unmanned Aerial Manipulator With on-the-Fly Rapid Re-Planning in Unknown Environments

Unmanned Aerial Manipulators (UAM) are gaining attention within the unmanned aerial systems research community. They can be used for aerial manipulation tasks such as object retrieval from confined and hard-to-reach spaces. The coupled dynamics between the arm and the base and also the limited flight time would require the development and implementation of optimal motion planning and robust flight control strategies. In this paper, we propose a novel integrated planning and control strategy for object retrieval. A new kinodynamic version of the Rapidly-exploring Random Tree (RRT*), called Lazy-Steering-RRT*, is developed for planning UAM’s motion from its start to a pre-grasp state, while keeping the motion of the arm to a minimum. This planning can be carried out on the fly by using Machine-Learning-based techniques to construct the edges in the search tree in a time-efficient way. This facilitates re-planning, as the environment is gradually sensed by limited-range sensors onboard. Once the UAM reaches the pre-grasp state at the end of the motion cycle, an RRT approach is then utilized, where motions of the base and the arm are coordinated for reaching and grasping the object. A novel partitioned control approach, composed of model predictive and PID controllers, is utilized for the UAM to track the planned trajectory. The overall motion planning and control algorithms have been implemented in simulation using Multibody Physics Engines and a number of representative simulation runs are presented. Our results show that the approach is effective in successfully executing the object retrieval task even in confined spaces. Note to Practitioners—This research was motivated by the problem of retrieving an object from a confined space such as that in a collapsed building using an unmanned aerial manipulator. Our approach has the following advantages over existing methods: a) Planning and re-planning can be carried out on the fly, b) No prior information about the environment is needed, c) High-frequency energy-optimal control of the UAM is carried out, and d) the planner and the controller are coupled. In this paper, we suggest some novel approaches on both planning and control aspects of the navigation and also on the integration of these two modules to provide a navigation package that meets all the requirements in the retrieval task. Furthermore, our algorithm is computationally-light, therefore, well suited for on the fly planning and control.