This paper presents a vision guidance approach using an image-based visual servo (IBVS) for an aerial manipulator combining a multirotor with a multidegree of freedom robotic arm. To take into account the dynamic characteristics of the combined manipulation platform, the kinematic and dynamic models of the combined system are derived. Based on the combined model, a passivity-based adaptive controller which can be applied on both position and velocity control is designed. The position control is utilized for waypoint tracking such as taking off and landing, and the velocity control is engaged when the platform is guided by visual information. In addition, a guidance law utilizing IBVS is employed with modifications. To secure the view of an object with an eye-in-hand camera, IBVS is utilized with images taken from a fisheye camera. Also, to compensate underactuation of the multirotor, an image adjustment method is developed. With the proposed control and guidance laws, autonomous flight experiments involving grabbing and transporting an object are carried out. Successful experimental results demonstrate that the proposed approaches can be applied in various types of manipulation missions.

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.

Controlling or accessing remotely has become a prevalent form of operating numerous types of platforms and infrastructure. An exploding number of vehicles such as drones or cars, in particular, are being controlled wirelessly or connected through networks. This has brought unanimous concern that today’s networked vehicle systems are vulnerable to attacks and the results could be fatal. Unfortunately, in contrast to active investigation on the security of the vehicles themselves, sensors, or communication channels, existing approaches for these real-time, safety-critical systems do not take controllers into enough consideration. In order to protect the controller that performs arithmetic operations using sensor measurements and generates command signals, we adopt homomorphic cryptography for the controller. It removes risks associated with the management of the secret key inside the controller, by eliminating the need to encrypt and decrypt the data for mathematical operation within the controller. Specifically, we propose an efficient Linearly Homomorphic Authenticated Encryption (LinHAE) scheme for the ground control center of a multi-rotor drone, in a manner that enables real-time operation for safe autonomous flight. To facilitate the linear scheme, we design the ground controller targetted to allow state update using additions and multiplications by a system-specific constant. The proposed LinHAE guarantees the security against eavesdropping and forgery attacks, unlike homomorphic encryption alone that does not provide means to check whether the received signal at the drone side is authentic or compromised. We introduce a LinHAE with security and computational tractability, and describe how it can fit into the standard architecture for drone systems and how the specific controller is implemented. Building on these ingredients, we report the first successful operation of a multi-rotor flying robot that autonomously flies under the ground controller with real-time homomorphic authenticated encryption.

This letter presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment. The proposed algorithm utilizes a linear safe corridor (LSC) to formulate the distributed trajectory optimization problem with only feasible constraints, so it does not resort to slack variables or soft constraints to avoid optimization failure. We adopt a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield. The proposed algorithm can compute the trajectories for 60 agents on average 15.5 ms per agent with an Intel i7 laptop and shows a similar flight distance and distance compared to the baselines based on soft constraints. We verified that the proposed method can reach the goal without deadlock in both the random forest and the indoor space, and we validated the safety and operability of the proposed algorithm through a real flight test with ten quadrotors in a maze-like environment.