Coordinated Standoff Tracking of Moving Target Groups Using Multiple UAVs

This paper presents a methodology for coordinated standoff tracking of moving target groups using multiple unmanned aerial vehicles (UAVs). The vector field guidance approach for a single UAV is first applied to track a group of targets by defining a variable standoff orbit to be followed, which can keep all targets within the field-of-view of the UAV. A new feedforward term is included in the guidance command considering variable standoff distance, and the convergence of the vector field to the standoff orbit is analyzed and enhanced by adjusting radial velocity using two active measures associated with vector field generation. Moreover, for multiple group tracking by multiple UAVs, a two-phase approach is proposed as a suboptimal solution for a Non-deterministic Polynomial-time hard (NP-hard) problem, consisting of target clustering/assignment and cooperative standoff group tracking with online local replanning. Lastly, localization sensitivity to the group of targets is investigated for different angular separations between UAVs and sensing configurations. Numerical simulations are performed using randomly moving ground vehicles with multiple UAVs to verify the feasibility and benefit of the proposed approach.clos

Coordinated standoff tracking of in- and out-of-surveillance targets using constrained particle filter for UAVs

This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results

Information-driven persistent sensing of a non-cooperative mobile target using UAVs

This paper addresses the persistent sensing problem of moving ground targets of interest using a group of fixed wing UAVs. Especially, we aim to overcome the challenge of physical obscuration in complex mission environments. To this end, the persistent sensing problem is formulated under an optimal control framework, i.e. deploying and managing UAVs in a way maximising the visibility to the non-cooperative target.The main issue with such a persistent sensing problem is that it generally requires the knowledge of future target positions, which is uncertain. To mitigate this issue, a probabilistic map of the future target position is widely utilised. However, most of the probabilistic models use only limited information of the target. This paper proposes an innovative framework that can make the best use of all available information, not only limited information. For the validation of the feasibility, the performance of the proposed framework is tested in a Manhattan-type controlled urban environment. All the simulation tests use the same framework proposed, but utilise different level of information. The simulation results confirm that the performance of the persistent sensing significantly improves, up to 30%, when incorporating all available target information

Optimal UAV Path Planning for Tracking a Moving Ground Vehicle with a Gimbaled Camera

This research develops a path planning algorithm that autonomously controls a UAV to provide convoy overwatch. The optimization algorithm determines the best path to y through developing a cost function that minimizes the control effort of the UAV and the deviation from a desired slant range. A heuristic-based algorithm was developed and implemented on the autopilot to approximate the optimal solution. In flight test, the UAV successfully tracked a moving ground vehicle by continually placing the UAV\u27s loiter point directly above the ground vehicle\u27s current location. This method was called the \follow-me mode and provided the baseline for real-world UAV convoy overwatch. The follow-me mode resulted in a cost function value that was 113 times greater than the optimal path. Through an in-depth analysis, the heuristic-based approach reduced this ratio down to only 7.5 times greater than the optimal path. The data collected shows tremendous promise for improving autonomous UAV performance through optimal control techniques

Autonomous aircraft initiative study

The results of a consulting effort to aid NASA Ames-Dryden in defining a new initiative in aircraft automation are described. The initiative described is a multi-year, multi-center technology development and flight demonstration program. The initiative features the further development of technologies in aircraft automation already being pursued at multiple NASA centers and Department of Defense (DoD) research and Development (R and D) facilities. The proposed initiative involves the development of technologies in intelligent systems, guidance, control, software development, airborne computing, navigation, communications, sensors, unmanned vehicles, and air traffic control. It involves the integration and implementation of these technologies to the extent necessary to conduct selected and incremental flight demonstrations

Communication-Aware Multi-Target Tracking Guidance for Cooperative UAVs with Gimbaled Vision Sensors in Urban Environments

Department of Mechanical Enginering (Mechanical Engineering)This paper proposes the unified cooperative multi-target tracking algorithm, which considers the sensing range and communication in an urban environment. The objective function of the proposed algorithm is composed of two terms. The first-term is formulated by using FIM. Since Fisher information matrix can be utilized to quantify the information gathered by the sensors, we can formulate an objective function that reflects the constraints like the sensor field of view(FOV). Also, by reflecting parameters related to communication, communication with the ground station can be considered. However, if the target is outside the sensing range or occluded by the building continuously, UAVs cannot capture this target in the prediction step of receding horizon method when the first-term is used only. To solve this problem, the second-term, which is made up of relative distance between targets and UAVs, is proposed. In this situation, the uncertainty increases because the target information cannot be obtained. As the uncertainty increases, the increasing weight is multiplied by the second-term to generate a path to reduce the distance to this target. If the distance to the target is within the sensing range by using this term, the target can be tracked again by using the first-term because the uncertainty decreases by the sensing. The main contributions of this thesis are as follows. First, UAVs can create a path and a gimbal command to get useful information by considering the limited sensing capability. Second, by considering communication, the communication stability has been improved and the amount of information in the ground station has been increased. Lastly, in the prediction step of the receding horizon method, the target can be tracked even when information about the target is not gathered.ope