Downwash-Aware Trajectory Planning for Large Quadrotor Teams

We describe a method for formation-change trajectory planning for large quadrotor teams in obstacle-rich environments. Our method decomposes the planning problem into two stages: a discrete planner operating on a graph representation of the workspace, and a continuous refinement that converts the non-smooth graph plan into a set of C^k-continuous trajectories, locally optimizing an integral-squared-derivative cost. We account for the downwash effect, allowing safe flight in dense formations. We demonstrate the computational efficiency in simulation with up to 200 robots and the physical plausibility with an experiment with 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.Comment: 8 page

Safe, Scalable, and Complete Motion Planning of Large Teams of Interchangeable Robots

Large teams of mobile robots have an unprecedented potential to assist humans in a number of roles ranging from humanitarian efforts to e-commerce order fulfillment. Utilizing a team of robots provides an inherent parallelism in computation and task completion while providing redundancy to isolated robot failures. Whether a mission requires all robots to stay close to each other in a formation, navigate to a preselected set of goal locations, or to actively try to spread out to gain as much information as possible, the team must be able to successfully navigate the robots to desired locations. While there is a rich literature on motion planning for teams of robots, the problem is sufficiently challenging that in general all methods trade off one of the following properties: completeness, computational scalability, safety, or optimality. This dissertation proposes robot interchangeability as an additional trade-off consideration. Specifically, the work presented here leverages the total interchangeability of robots and develops a series of novel, complete, computationally tractable algorithms to control a team of robots and avoid collisions while retaining a notion of optimality. This dissertation begins by presenting a robust decentralized formation control algorithm for control of robots operating in tight proximity to one another. Next, a series of complete, computationally tractable multiple robot planning algorithms are presented. These planners preserve optimality, completeness, and computationally tractability by leveraging robot interchangeability. Finally, a polynomial time approximation algorithm is proposed that routes teams of robots to visit a large number of specified locations while bounding the suboptimality of total mission completion time. Each algorithm is verified in simulation and when applicable, on a team of dynamic aerial robots

Decentralized 3D Collision Avoidance for Multiple UAVs in Outdoor Environments

The use of multiple aerial vehicles for autonomous missions is turning into commonplace. In many of these applications, the Unmanned Aerial Vehicles (UAVs) have to cooperate and navigate in a shared airspace, becoming 3D collision avoidance a relevant issue. Outdoor scenarios impose additional challenges: (i) accurate positioning systems are costly; (ii) communication can be unreliable or delayed; and (iii) external conditions like wind gusts affect UAVs’ maneuverability. In this paper, we present 3D-SWAP, a decentralized algorithm for 3D collision avoidance with multiple UAVs. 3D-SWAP operates reactively without high computational requirements and allows UAVs to integrate measurements from their local sensors with positions of other teammates within communication range. We tested 3D-SWAP with our team of custom-designed UAVs. First, we used a Software-In-The-Loop simulator for system integration and evaluation. Second, we run field experiments with up to three UAVs in an outdoor scenario with uncontrolled conditions (i.e., noisy positioning systems, wind gusts, etc). We report our results and our procedures for this field experimentation.European Union’s Horizon 2020 research and innovation programme No 731667 (MULTIDRONE