Link!: Potential Field Guidance Algorithm for In-Flight Linking of Multi-Rotor Aircraft

Link! is a multi-center NASA e ort to study the feasibility of multi-aircraft aerial docking systems. In these systems, a group of vehicles physically link to each other during flight to form a larger ensemble vehicle with increased aerodynamic performance and mission utility. This paper presents a potential field guidance algorithm for a group of multi-rotor vehicles to link to each other during flight. The linking is done in pairs. Each vehicle first selects a mate. Then the potential field is constructed with three rules: move towards the mate, avoid collisions with non-mates, and stay close to the rest of the group. Once a pair links, they are then considered to be a single vehicle. After each pair is linked, the process repeats until there is only one vehicle left. The paper contains simulation results for a system of 16 vehicles

Project Link!: Dynamics and Control of In-Flight Wing Tip Docking

Project Link! is a NASA-led effort to study the feasibility of multi-aircraft aerial docking systems. In these systems, a group of vehicles physically link to each other during flight to form a larger ensemble vehicle with increased aerodynamic performance and mission utility. This paper presents a dynamic model and control architecture for a system of fixed-wing vehicles with this capability. The dynamic model consists of the 6 degree-of-freedom fixed-wing aircraft equations of motion, a spring-damper-magnet system to represent the linkage force between constituent vehicles, and the NASA-Burnham-Hallock wingtip vortex model to represent the close-proximity aerodynamic interactions between constituents before the linking occurs. The control architecture consists of a guidance algorithm to autonomously drive the constituents towards their linking partners and an inner-loop angular rate controller. A simulation was constructed from the model, and the flight dynamic modes of the linked system were compared to the individual vehicles. Simulation results for both before and after linking are presented

Modular Hydraulic Propulsion: A Robot that Moves by Routing Fluid Through Itself

This paper introduces the concept of Modular Hydraulic Propulsion, in which a modular robot that operates in a fluid environment moves by routing the fluid through itself. The robot’s modules represent sections of a hydraulics network. Each module can move fluid between any of its faces. The modules (network sections) can be rearranged into arbitrary topologies. We propose a decentralized motion controller, which does not require modules to communicate, compute, nor store information during run-time. We use 3-D simulations to compare the performance of this controller to that of a centralized controller with full knowledge of the task. We also detail the design and fabrication of six 2-D prototype modules, which float in a water tank. Results of systematic experiments show that the decentralized controller, despite its simplicity, reliably steers modular robots towards a light source. Modular Hydraulic Propulsion could offer new solutions to problems requiring reconfigurable systems to move precisely in 3-D, such as inspection of pipes, vascular systems or other confined spaces

Finding Optimal Modular Robots for Aerial Tasks

Traditional aerial vehicles have limitations in their capabilities due to actuator constraints, such as motor saturation. The hardware components and their arrangement are designed to satisfy specific requirements and are difficult to modify during operation. To address this problem, we introduce a versatile modular multi-rotor vehicle that can change its capabilities by reconfiguration. Our modular robot consists of homogeneous cuboid modules, propelled by quadrotors with tilted rotors. Depending on the number of modules and their configuration, the robot can expand its actuation capabilities. In this paper, we build a mathematical model for the actuation capability of a modular multi-rotor vehicle and develop methods to determine if a vehicle is capable of satisfying a task requirement. Based on this result, we find the optimal configurations for a given task. Our approach is validated in realistic 3D simulations, showing that our modular system can adapt to tasks with varying requirements