Steven Lake Waslander

Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment

Quadrotor helicopters are emerging as a popular platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their construction and maintenance, their ability to hover, and their vertical take o and landing (VTOL) capability. Current designs have often considered only nominal operating conditions for vehicle control design. This work seeks to address issues that arise when deviating significantly from the hover flight regime. Aided by well established research for helicopter flight control, three separate aerodynamic eects are investigated as they pertain to quadrotor flight, due to vehicular velocity, angle of attack, and airframe design. They cause moments that aect attitude control, and thrust variation that aects altitude control. Where possible, a theoretical development is first presented, and is then validated through both thrust test stand measurements and vehicle flight tests using the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) quadrotor helicopter. The results enabled improved controller performance.

Aerodynamics and control of autonomous quadrotor helicopters in aggressive maneuvering

Quadrotor helicopters have become increasingly important in recent years as platforms for both research and commercial unmanned aerial vehicle applications. This paper extends previous work on several important aerodynamic effects impacting quadrotor flight in regimes beyond nominal hover conditions. The implications of these effects on quadrotor performance are investigated and control techniques are presented that compensate for them accordingly. The analysis and control systems are validated on the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control quadrotor helicopter testbed by performing the quadrotor equivalent of the stall turn aerobatic maneuver. Flight results demonstrate the accuracy of the aerodynamic models and improved control performance with the proposed control schemes.

The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC)

As an alternative to cumbersome aerial vehicles with considerable maintenance requirements and flight envelope restrictions, the X4 flyer is chosen as the basis for the Stanford testbed of autonomous rotorcraft for multi-agent control (STARMAC). This paper outlines the design and development of a miniature autonomous waypoint tracker flight control system, and the creation of a multi-vehicle platform for experimentation and validation of multi-agent control algorithms. This testbed development paves the way for real-world implementation of recent work in the fields of autonomous collision and obstacle avoidance, task assignment formation flight, using both centralized and decentralized techniques.

Quadrotor Helicopter Trajectory Tracking Control

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), a eet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. This paper develops an autonomous vehicle trajectory tracking algorithm through cluttered environments for the STARMAC platform. A system relying on a single optimization must trade o the complexity of the planned path with the rate of update of the control input. In this paper, a trajectory tracking controller for quadrotor helicopters is developed to decouple the two problems. By accepting as inputs a path of waypoints and desired velocities, the control input can be updated frequently to accurately track the desired path, while the path planning occurs as a separate process on a slower timescale. To enable the use of planning algorithms that do not consider dynamic feasibility or provide feedforward inputs, a computationally ecient algorithm using space-indexed waypoints is presented to modify the speed prole of input paths to guarantee feasibility of the planned trajectory and minimum time traversal of the planned. The algorithm is an ecient alternative to formulating a nonlinear optimization or mixed integer program. Both indoor and outdoor ight test results are presented for path tracking on the STARMAC vehicles.

Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.

Wind Disturbance Estimation and Rejection for Quadrotor Position Control

ight control can be quite signicant, and can lead to dangerous situations when operating in close proximity to obstacles or other aerial vehicles. This work seeks to improve quadrotor positioning performance by formally modeling the wind eects on quadrotor dynamics in order to estimate wind velocities in ight and control the vehicle accordingly. Models for wind disturbances, quadrotor dynamics in wind and onboard measurements are presented and an estimation algorithm is developed for the current wind velocity experienced by the vehicle. This wind estimate is used to improve positioning accuracy by both eliminating the eect of wind on the feedback position control law and adding a wind compensator to mitigate the eect of the expected wind disturbance. Simulation results are presented for multiple scenarios, and work is ongoing for implementation on board a quadrotor testbed.

Distributed Cooperative Search using Information-Theoretic Costs for Particle Filters, with Quadrotor Applications ∗

Search and rescue missions can be efficiently and automatically performed by small, highly maneuverable unmanned aerial vehicle (UAV) teams. The search problem is complicated by a lack of prior information, nonlinear mapping between sensor observations and the physical world, and potentially non-Gaussian sensor noise models. To address these problems, a distributed control algorithm is proposed, using information theoretic methods with particle filters, to compute optimal control inputs for a multi-vehicle, coordinated localization of a stationary target. This technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs that maneuver the UAVs to make observations that minimize the expected future uncertainty of the target state. Because the method directly uses the particle filter state and an accurate sensor noise model to compute the mutual information, it is no longer necessary to discard information by using linear and Gaussian approximations. To ensure safety of the vehicles, the algorithm incorporates collision avoidance and control authority constraints. The resulting information theoretic cost calculation is coupled amongst the vehicles and becomes prohibitive as the size of the UAV team becomes large. Therefore, single vehicle and pairwise approximations to the cost function are used that greatly reduce the computational burden and allow for development of a distributed algorithm for real-time optimization of vehicle trajectories. Simulation results are shown for a bearings-only sensor model with multiple vehicles. Initial flight tests of the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) show the feasibility of implementation of this algorithm on the quadrotor testbed and in real world situations.

Tunnel-MILP: Path Planning with Sequential Convex Polytopes

This paper focuses on optimal path planning for vehicles in known environments. Previous work has presented mixed integer linear programming (MILP) formulations, which suer from scalability issues as the number of obstacles, and hence the number of integer variables, increases. In order to address MILP scalability, a novel three-stage algorithm is presented which rst computes a desirable path through the environment without considering dynamics, then generates a sequence of convex polytopes containing the desired path, and nally poses a MILP to identify a suitable dynamically feasible path. The sequence of polytopes form a safe tunnel through the environment, and integer decision variables are restricted to deciding when to enter and exit each region of the tunnel. Simulation results for this approach are presented and reveal a signicant increase in the size and complexity of the environment that can be solved.

Planning Paths for Package Delivery in Heterogeneous Multirobot Teams

This paper addresses the task scheduling and path planning problem for a team of cooperating vehicles performing autonomous deliveries in urban environments. The cooperating team comprises two vehicles with complementary capabilities, a truck restricted to travel along a street network, and a quadrotor micro-aerial vehicle of capacity one that can be deployed from the truck to perform deliveries. The problem is formulated as an optimal path planning problem on a graph and the goal is to find the shortest cooperative route enabling the quadrotor to deliver items at all requested locations. The problem is shown to be NP-hard. A solution is then proposed using a novel reduction to the Generalized Traveling Salesman Problem, for which well-established heuristic solvers exist. The heterogeneous delivery problem contains as a special case the problem of scheduling deliveries from multiple static warehouses. We propose two additional algorithms, based on enumeration and a reduction to the traveling salesman problem, for this special case. Simulation results compare the performance of the presented algorithms and demonstrate examples of delivery route computations over real urban street maps.

Mutual Information Methods with Particle Filters for Mobile Sensor Network Control

This paper develops a set of methods enabling an information-theoretic distributed control architecture based on particle filters to facilitate search by a mobile sensor network, permitting the use of nonlinear and non-Gaussian sensor models. Given a particular configuration sensors, this technique exploits the structure of the probability distributions of the target state and of the sensor measurements to compute the control inputs to the mobile sensors leading to future observations that minimize, in expectation, the future uncertainty of the target state. We compute the mutual information using a particle set representation of the posterior distribution. In order to control a large number of mobile sensors as a network, single-node and pairwise-node approximation schemes are presented, with analytically bounded error, making the approach scalable to increasing network sizes, while still planning cooperatively. The methods are applied in simulation to bearings-only sensing, and to localizing an avalanche rescue beacon of a buried victim, using transceivers on quadrotor aircraft to measure the magnetic field. Monte Carlo simulations also demonstrate that as network size increases, the sensors more quickly localize the target, and the pairwise-node approximation results in superior performance to the single-node approximation