This paper describes an approach to motion generation for quadrotor micro-UAV's navigating cluttered and partially known environments. We pursue a graph search method that, despite the high dimensionality of the problem, the complex dynamics of the system and the continuously changing environment model is capable of generating dynamically feasible motions in real-time. This is enabled by leveraging the differential flatness property of the system and by developing a structured search space based on state lattice motion primitives. We suggest a greedy algorithm to generate these primitives off-line automatically, given the robot's motion model. The process samples the reachability of the system and reduces it to a set of representative, canonical motions that are compatible with the state lattice structure, which guarantees that any incremental replanning algorithm is able to produce smooth dynamically feasible motion plans while reusing previous computation between replans. Simulated and physical experimental results demonstrate real-time replanning due to the inevitable and frequent world model updates during micro-UAV motion in partially known environments.

We present an algorithm for the generation of optimal trajectories for teams of heterogeneous quadrotors in three-dimensional environments with obstacles. We formulate the problem using mixed-integer quadratic programs (MIQPs) where the integer constraints are used to enforce collision avoidance. The method allows for different sizes, capabilities, and varying dynamic effects between different quadrotors. Experimental results illustrate the method applied to teams of up to four quadrotors ranging from 65 to 962 grams and 21 to 67 cm in width following trajectories in three-dimensional environments with obstacles with accelerations approaching 1g.

We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs. A nonlinear controller ensures the faithful tracking of these trajectories. Experimental results illustrate the application of the method to fast motion (5-10 body lengths/second) in three-dimensional slalom courses.

This video features advanced control capabilities for a quadrotor helicopter. We use an Ascending Technologies Hummingbird quadrotor [1] in conjunction with a Vicon [2] motion capture system in a flight arena [3] . The video shows a single quadrotor flying through windows with only three inches of clearance on any side of the platform and perching on vertical, declined, and inclined walls. Robustness to failed perching is demonstrated when it recovers to a stable hover from a failed perching attempt. Next, the video shows control along optimal trajectories through desired waypoints which includes flying through static and thrown hoops. Seen next is a gripper designed for the quadrotor to pickup objects and perch on horizontal surfaces. Finally, multiple quadrotors are shown cooperatively manipulating payloads to which they are rigidly attached.

This paper addresses mechanics, design, estimation and control for aerial grasping. We present the design of several light-weight, low-complexity grippers that allow quadrotors to grasp and perch on branches or beams and pick up and transport payloads. We then show how the robot can use rigid body dynamic models and sensing to verify a grasp, to estimate the the inertial parameters of the grasped object, and to adapt the controller and improve performance during flight. We present experimental results with different grippers and different payloads and show the robot's ability to estimate the mass, the location of the center of mass and the moments of inertia to improve tracking performance.

This paper describes a motion planning algorithm that accounts for uncertainty in the dynamics of vehicles. This noise is a function of the type of controller employed on the vehicle and the characteristics of the terrain and can cause the robot to deviate from a planned trajectory and collide with obstacles. Our motion planning algorithm finds trajectories that balance the trade-off between conventional performance measures such as time and energy versus safety. The key is a characterization of the vehicle's ability to follow planned paths, which allows the algorithm to explicitly calculate probabilities of successful traversal for different trajectory segments. We illustrate the method with a six-legged Rhex-like robot by experimentally characterizing different gaits (controllers) on different terrains and demonstrating the hexapod navigating a multi-terrain environment.

We present a novel approach to controlling the locomotion of a wheel by changing its shape, leading to applications to the synthesis and closed-loop control of gaits for modular robots. A dynamic model of a planar, continuous deformable ellipse in contact with a ground surface is derived. We present two alternative approaches to controlling this system and a method for mapping the gaits to a discrete rolling polygon. Mathematical models and dynamic simulation of the continuous approximation and the discrete n-body system, and experimental results obtained from a physical modular robot system illustrate the accuracy of the dynamic models and the validity of the approach.