Cooperative aerial load transportation via sampled communication

In this letter, we propose a feedback-based motion planner for a class of multi-agent manipulation systems with a sparse kinematics structure. In other words, the agents are coupled together only by the transported object. The goal is to steer the load into a desired configuration. We suppose that a global motion planner generates a sequence of desired configurations that satisfy constraints as obstacles and singularities avoidance. Then, a local planner receives these references and generates the desired agents velocities, which are converted into force inputs for the vehicles. We focus on the local planner design both in the case of continuously available measurements and when they are transmitted to the agents via sampled communication. For the latter problem, we propose two strategies. The first is the discretization of the continuous-time strategy that preserves stability and guarantees exponential convergence regardless of the sampling period. In this case, the planner gain is static and computed off-line. The second strategy requires to collect the measurements from all sensors and to solve online a set of differential equations at each sampling period. However, it has the advantage to provide doubly exponential convergence. Numerical simulations of these strategies are provided for the cooperative aerial manipulation of a cable-suspended load.