This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader- follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments.

This paper presents estimation and control synthesis for an aerial manipulator to carry an unknown payload. Online estimation is based on parametrization of the aerial manipulator, which consists of a multirotor and a robotic arm. With the estimated physical properties, an augmented adaptive controller is proposed so that the end effector of the robotic arm can track the desired trajectory. Relying on this control structure, finally, we propose a flight motion generation method satisfying the joint angle limitation based on the analysis of the allowable flight area with respect to the joint angle variation. To validate our approach, the simulation results with comparison of conventional adaptive controller are shown. We also perform load carrying experiments using a custom-made aerial manipulator.

Frontier-based exploration is widely adopted for exploring an unknown region. The conventional frontier-based exploration for a mobile robot may collide with three-dimensional (3D) obstacles or can suffer from a slower exploration time because the robot may move to another place before completely exploring the current area. To solve this problem, in this letter, we propose a new exploration algorithm by considering a path traveled by a mobile robot and segmenting a two-dimensional (2D) map. The segmented 2D map is generated in real-time by using the position of the robot and the location of the detected frontiers. To apply our algorithm to the actual experiment, we develop an object detection-based exploration algorithm that can remarkably reduce the probability of collision with 3D obstacles. To verify the effectiveness of our proposed algorithm, we perform simulations (Gazebo) and experiments (in the real world) to compare the conventional approach and our algorithm in a cluttered environment. The simulation and experiment results show that our algorithm can satisfactorily shorten the exploration path and time.

Cooperation among multiple aerial vehicles could provide a solution for overcoming limited payload restrictions. Multiple aerial vehicles can carry a heavy or large object safely by sharing the load, alleviating the risk involved in exerting considerable inertia on a single vehicle.