Matthew E. Argyle

Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles

As the need for autonomous reconnaissance and surveillance missions in cluttered urban environments has been increasing, this paper describes a cooperative path planning algorithm for tracking a moving target in urban environments using both unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). The novelty of the algorithm is that it takes into account vision occlusions due to obstacles in the environment. The algorithm uses a dynamic occupancy grid to model the target state, which is updated by sensor measurements using a Bayesian filter. Based on the current and predicted target behavior, the path planning algorithm for a single vehicle (UAV/UGV) is first designed to maximize the sum of the probability of detection over a finite look-ahead horizon. The algorithm is then extended to multiple vehicle collaboration scenarios, where a decentralized planning algorithm relying on an auction scheme is designed to plan finite look-ahead paths that maximize the sum of the joint probability of detection over all vehicles.

Probabilistic path planning for cooperative target tracking using aerial and ground vehicles

In this paper, we present a probabilistic path planning algorithm for tracking a moving ground target in urban environments using UAVs in cooperation with UGVs. The algorithm takes into account vision occlusions due to obstacles in the environments. The target state is modeled using the dynamic occupancy grid and the probability of the target location is updated using Bayesian Altering. Based on the probability of the targets current and predicted locations, the path planning algorithm is designed to generate paths for a single UAV or UGV maximizing the sum of probability of detection over a finite look-ahead. For target tracking using multiple vehicle collaboration, a decentralized planning algorithm using an auction scheme generates paths maximizing the sum of joint probability of detection over the finite look ahead horizon. Simulation results show the proposed algorithm is successful in solving the target tracking problem in urban environments.

Chain-based path planning for multiple UAVs

This paper presents a UAV path planning strategy for optimizing the return over a finite-time horizon, where the return is specified by a bounded differentiable reward function. We represent paths using a simulated chain in a force field, where the forces are influenced by the reward function. The chain adapts continuously to changes in the return function, and produces good paths with minimal computational overhead. We compare the chain-based path planner to a look-ahead planner and extend this approach to multiple UAVs in a centralized manner.

The Vertical Bat tail-sitter: Dynamic model and control architecture

Over the years many aircraft designs have been developed that attempt to merge the endurance and speed of fixed wing aircraft with the flexibility and vertical takeoff and landing abilities of rotor craft. The tail-sitter is one such design. Unlike most other designs, a tail-sitter requires no additional moving parts. However, it trades this mechanical simplicity for increased control complexity. This paper presents the kinematic and dynamic model for the Vertical Bat, a tail-sitter that is currently being developed, as well as vector field based control schemes covering all the flight regimes. In addition, the level to hover transition controller presented is able to control the ending horizontal position of the aircraft.

Tailsitter attitude control using resolved tilt-twist

The tailsitter aircraft merges the endurance and speed of fixed-wing aircraft with the flexibility and VTOL abilities of rotorcraft. Because of the requirement to be functional at a full range of attitudes, quaternions are typically employed to calculate attitude error. Attitude control is then accomplished by using the vector component of the error quaternion to drive flight control surfaces. This paper demonstrates that this method of driving the flight control surfaces can be suboptimal for tailsitter type aircraft and can lead to undesired vehicle movement. An alternate method of calculating attitude error called resolved tilt-twist is improved and validated. The resolved-tilt twist method is implemented in hardware and hardware-in-loop simulation results are presented.

3D path planning for small UAS operating in low-altitude airspace

A two step path planning algorithm with collision detection logic for small UAS is proposed. The path planning approach taken in this work is carried out in two steps. In the first step, an initial suboptimal path is generated using A* search. In the second step, a chain of unit masses connected by springs and dampers evolves in a simulated force field, using the A* solution as an initial condition. The chain is described by a set of ordinary differential equations that is driven by virtual forces to find the steady-state equilibrium. A unique strength of this approach is the ability to use dynamic chain analogy to smooth path into a fiyable shape while mainlining a safe distance from intruders. The simulation results show that the proposed approach produces collision-free plans while minimizing the path length.

Nonlinear Total Energy Control for the Longitudinal dynamics of an aircraft

Many common altitude and airspeed control schemes for an aircraft assume that the altitude and airspeed dynamics are decoupled which leads to errors. The Total Energy Control System (TECS) is an approach that controls the altitude and airspeed by manipulating the total energy rate and energy distribution rate, of the aircraft, in a manner which accounts for the dynamic coupling. In this paper, a nonlinear controller based on the TECS principles are derived. Simulation results show that the nonlinear controller has better performance than the standard PI type TECS control schemes.

Hierarchical Distributed Task Assignment for UAV Teams

In this paper, we discuss four impediments to distributed UAV architectures: poor UAV to UAV communication, inadequate processing capability (applies to both computation and sensors), operators’ desire for a continuous stream of video and images, and pilot safety in airspace with both unmanned and manned aircraft. These impediments motivate centralized architectures for multi-UAV planning and coordination. The key problem stems from the need for a human to continuously monitor the UAVs’ data streams. We discuss a hierarchical distributed task assignment algorithm, called the team consensus based bundle algorithm, that keeps these four impediments in check. Furthermore, a multi-socket manager (MSM) is presented, which was designed with these four impediments in mind. The MSM is an interface that allows testing of hierarchical and distributed planning architectures that keep these fours factors in check, e.g., the team consensus based bundle algorithm. Currently deployed unmanned air vehicles (UAV) are controlled by a group of operators that actively manage different components of the UAV. One operator might control the motion of the vehicle, either directly through “stick-and-rudder” inputs or through a waypoint navigation mode. Simultaneously, a different group of operators is remotely controlling the onboard sensors and evaluating any received data. Recent research has been focused on inverting this ratio of operators per UAV to have a single operator manage a team of UAVs. 1 Planning algorithms have been developed to aid the operator with the management of a team of UAVs, and current research is improving on these algorithms. 2‐4 To date, planners are generally implemented

Quaternion based attitude error for a tailsitter in hover flight

The tailsitter is a promising airframe that can take off and land on its tail and transition to level flight. While this ability provides vertical takeoff and landing capabilities with no additional moving parts, it introduces interesting control challenges. In this paper, we look at the attitude control system of a tailsitter in hover flight and show that the behaviour of the aircraft relies on the method used to compute the attitude error. We investigate three different methods of computing the attitude error, quaternion feedback, resolved tilt twist, and the resolved Euler angles, and compare them through simulated hover flight.

Tailsitter heading estimation using a magnetometer

The tailsitter aircraft merges the endurance and speed of fixed-wing aircraft with the flexibility and VTOL abilities of rotorcraft. Typical control and estimation schemes make assumptions about the maximum attitude an aircraft will experience that are not valid for tailsitters. This paper discusses the limitations of a typical EKF magnetometer measurement update that uses Euler angles. It is shown how to use a second set of Euler angles to avoid gimbal lock. A method is given that bypasses the use of Euler angles altogether and directly uses the quaternion to determine heading error and update the attitude estimate. This method highlights the EKF limitations in estimating a quaternion. A multiplicative EKF is briefly explored to overcome these limitations. Hardware results on an actual tailsitter aircraft are presented.