This letter addresses the problem of trajectory planning in a marsupial robotic system consisting of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) through a non-taut tether with controllable length. To the best of our knowledge, this is the first method that addresses the trajectory planning of a marsupial UGV-UAV with a non-taut tether. The objective is to determine a synchronized collision-free trajectory for the three marsupial system agents: UAV, UGV, and tether. First, we present a path planning solution based on optimal Rapidly-exploring Random Trees (RRT*) with novel sampling and steering techniques to speed-up the computation. This algorithm is able to obtain collision-free paths for the UAV and the UGV, taking into account the 3D environment and the tether. Then, the letter presents a trajectory planner based on non-linear least squares. The optimizer takes into account aspects not considered in the path planning, like temporal constraints of the motion imposed by limits on the velocities and accelerations of the robots, or raising the tether's clearance. Simulated and field test results demonstrate that the approach generates obstacle-free, smooth, and feasible trajectories for the marsupial system.

This paper presents a collision-free path planning method for an aerial vehicle sharing airspace with other aerial vehicles. It is based on grid models and genetic algorithms to find safe trajectories. Monte-Carlo method is used to evaluate the best predicted trajectories considering different sources of uncertainty such as the wind, the inaccuracies in the vehicle model and limitations of on-board sensors and control system.

This paper presents a collision avoidance method for multiple UAVs and other non-cooperative aircraft based on velocity planning and taking into account the trajectory prediction under uncertainties. The proposed method finds a safe trajectory from the predicted trajectory modifying the velocity profile of the different co-operative vehicles involved in the collision. A particle filter is used to predict the trajectories under uncertainties dealing with the influence of different sources of uncertainty such as the atmospheric conditions, the UAV model and the limitations of the sensors and control system on board the UAV.

This paper presents a new system which automatically identifies conflicts between multiple UAVs (Unmanned Aerial Vehicles) and proposes the most effective solution considering the available computation time. The system detects conflicts using an algorithm based on axis-aligned minimum bounding box and resolves them cooperatively using a collision-free trajectory planning algorithm based on a simple one-at-a-time strategy to quickly compute a feasible but non-optimal initial solution and a stochastic optimization technique named Particle Swarm Optimization (PSO) to improve the initial solution. PSO modifies the 4D trajectories of the UAVs with an overall minimum cost. Determining optimal trajectories with short time intervals during the execution of the mission is not feasible, hence an anytime approach using PSO is applied. This approach yields trajectories whose quality improves when available computation time increases. Thus, the method could be applied in realtime depending on the available computation time. The method has been validated with simulations in scenarios with multiple UAVs in a common workspace.