Derek Kingston

Cooperative forest fire surveillance using a team of small unmanned air vehicles

The objective of this paper is to explore the feasibility of using multiple low-altitude, short endurance (LASE) unmanned air vehicles (UAVs) to cooperatively monitor and track the propagation of large forest fires. A real-time algorithm is described for tracking the perimeter of fires with an on-board infrared sensor. Using this algorithm, we develop a decentralized multiple-UAV approach to monitoring the perimeter of a fire. The UAVs are assumed to have limited communication and sensing range. The effectiveness of the approach is demonstrated in simulation using a six degree-of-freedom dynamic model for the UAV and a numerical propagation model for the forest fire. Salient features of the approach include the ability to monitor a changing fire perimeter, the ability to systematically add and remove UAVs from the team, and the ability to supply time-critical information to fire fighters.

Autonomous Vehicle Technologies for Small Fixed-Wing UAVs

Autonomous unmanned air vehicle ∞ight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous flxed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle’s route without regard for the dynamic constraints of the vehicle. This results in a signiflcant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfled using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.

Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs

Numerous applications require aerial surveillance. Civilian applications include monitoring forest fires, oil fields, and pipelines and tracking wildlife. Applications to homeland security include border patrol and monitoring the perimeter of nuclear power plants. Military applications are numerous. The current approach to these applications is to use a single manned vehicle for surveillance. However, manned vehicles are typically large and expensive. In addition, hazardous environments and operator fatigue can potentially threaten the life of the pilot. Therefore, there is a critical need for automating aerial surveillance using unmanned air vehicles (UAVs). This paper gives an overview of a cooperative control strategy for aerial surveillance that has been successfully flight tested on small (48-in wingspan) UAVs. Our approach to cooperative control problems can be summarized in four steps: 1) the definition of a cooperation constraint and cooperation objective; 2) the definition of a coordination variable as the minimal amount of information needed to effect cooperation; 3) the design of a centralized cooperation strategy; and 4) the use of consensus schemes to transform the centralized strategy into a decentralized algorithm. The effectiveness of the solution will be shown using both high-fidelity simulation and actual flight tests

Multi-agent Kalman consensus with relative uncertainty

In this paper, we propose discrete-time and continuous-time consensus update schemes motivated by the discrete-time and continuous-time Kalman filters. With certainty information encoded into each agent, the proposed consensus schemes explicitly account for relative confidence/reliability of information states from each agent in the team. We show mild sufficient conditions under which consensus can be achieved using the proposed consensus schemes in the presence of switching interaction topologies.

Real-Time Attitude and Position Estimation for Small UAVs Using Low-Cost Sensors

Small unmanned air vehicles (UAVs) and micro air vehicles (MAVs) have payload and power constraints that prohibit heavy sensors and powerful processors. This paper presents real-time attitude and position estimation solutions that use small, inexpensive sensors and low-power microprocessors. In connection with an Extended Kalman Filter attitude estimation scheme, a novel method for dealing with latency in real-time is presented using a distributed-in-time architecture. Essential to small UAV or MAV missions is the ability to navigate precisely. To reduce computational overhead and to simplify design, a cascaded filter approach to position estimation is used. The design is insensitive to noise and to loss of GPS lock. Simulation and hardware tests show that the algorithms operate in real-time and are suitable for control, stabilization, and navigation.

Consensus algorithms are input-to-state stable

In many cooperative control problems, a shared knowledge of information provides the basis for cooperation. When this information is different for each agent, a state of noncooperation can result. Consensus algorithms ensure that after some time the agents would agree on the information critical for coordination, called the coordination variable. In this paper we show that if the coordination algorithm is input-to-state stable where the input is considered to be the discrepancy between the coordination variable known to each vehicle, then cooperation is guaranteed when a consensus scheme is used to synchronize information. A coordinated timing example is shown in simulation to illustrate the notions of stability when a coordination algorithm is augmented with a consensus strategy.

Time-dependent cooperative assignment

The problem of assigning multiple agents to time-dependent cooperative tasks is addressed using a mixed-integer linear program. A time-dependent cooperative task is a task requiring multiple agents to perform separate sub tasks simultaneously or within some predetermined margin where agent availability to perform a subtask is limited to specific intervals in time. By separating the underlying calculation of agent availability and cost from the mechanism of assignment, a method to solve complex cooperative assignment problems can be formulated. A cooperative UAV target tracking/target prosecution scenario is presented to illustrate the assignment method.

Cooperative forest fire monitoring using multiple UAVs

This paper addresses the problem of using a team of UAVs to cooperatively monitor multiple forest fires (called hotspots) in a region. We deploy two types of agents: service agents that locally monitor the state of the hotspots and detector agents that search for hotspots and assign service agents to those that are detected. We develop an assignment scheme based on auctions to perform the assignment of a hotspot to a group of service agents. While monitoring a hotspot, service agents are required to be equally spaced around the perimeter. In order to achieve equal spacing around the hotspot perimeter, we develop a splay state controller that ensures the convergence of the service agents to the equally spaced configuration. We also address the problems associated with tracking hotspots that expand in size and possibly combine together. Simulation results are presented to validate the assignment and control algorithms.

UAV Splay State Configuration for Moving Targets in Wind

Cooperative surveillance problems require members of a team to spread out in some fashion to maximize coverage. In the case of single target surveillance, a team of UAVs angularly spaced (i.e. in the splay state configuration) provides the best coverage of the target in a wide variety of circumstances. In this chapter we propose a decentralized algorithm to achieve the splay state configuration for a team of UAVs tracking a moving target. We derive the allowable bounds on target velocity to generate a feasible solution as well as show that, near equilibrium, the overall system is exponentially stable. Monte Carlo simulations indicate that the surveillance algorithm is asymptotically stable for arbitrary initial conditions. We conclude with high fidelity simulation tests to show the applicability of the splay state controller to actual unmanned air systems.

Autonomous Vehicle Technologies for Small Fixed Wing UAVs

Autonomous unmanned air vehicle flight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous fixed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle’s route without regard for the dynamic constraints of the vehicle. This results in a significant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfied using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.