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Dynamic Heuristics for Surveillance Mission Scheduling with Unmanned Aerial Vehicles in Heterogeneous Environments

Authors:
Dylan Machovec, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins
James A. Crowder, Colorado Engineering Inc.
Howard Jay Siegel, Department of Electrical and Computer Engineering and Department of Computer Science, Colorado State University, Fort Collins
Sudeep Pasricha, Department of Electrical and Computer Engineering and Department of Computer Science, Colorado State University, Fort Collins
Anthony A. Maciejewski, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins

Abstract:
In this study, our focus is on the design of mission scheduling techniques capable of working in dynamic environments with unmanned aerial vehicles, to determine effective mission schedules in real-time. The effectiveness of mission schedules for unmanned aerial vehicles is measured using a surveillance value metric, which incorporates information about the amount and usefulness of information obtained from surveilling targets. We design a set of dynamic heuristic techniques, which are compared and evaluated based on their ability to maximize surveillance value in a wide range of scenarios generated by a randomized model. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. The novel metaheuristic is shown to find effective solutions that are the best on average as all other techniques that we evaluate in all scenarios that we consider.

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Dynamic-Heuristics-for-Surveillance.pdf (5 downloads)

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Surveillance Mission Planning: Model, Performance Measure, Bi-Objective Analysis, Partial Surveils

Authors:
Ryan Friese, Pacific Northwest National Laboratory (PNNL)
Dr. James Crowder, Colorado Engineering Inc.
Howard Jay Siegel, Elect. and Computer Engineering and Computer Science Depts., Colorado State University
John Carbone, Computer Science Dept., Southern Methodist University

Abstract:
We examine the trade-offs between energy and performance when conducting surveillance mission planning in a multi-vehicle, multi-target, multi-sensor environment. The vehicles are heterogeneous UAVs (unmanned aerial vehicles) that must surveil heterogeneous targets across a geographically distributed area within a given period of time (here, 24 hours). We design a new model for surveilling heterogeneous targets by heterogeneous UAVs. Based on this new model, we define a new system-wide surveillance performance measure that includes the targets surveilled, the number of times each target is surveilled, the UAV used for each surveil, the sensor type on the UAV that is used for each surveil, the priority of each target, and the allowance of partial surveil times. Then we implemented a genetic algorithm (GA) for a bi-objective analysis of energy versus surveillance performance for a set of realistic system parameters. The fitness function for this GA is based on our new model and our new performance measure. We construct a Pareto front of mappings of UAVs and sensors to targets to use to study trade-offs between the two conflicting objectives of maximizing surveillance performance and minimizing energy consumed. We also examine how allowing partial surveils of targets can impact system performance.

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Surveillance Mission Planning: Model, Performance Measure, Bi-Objective Analysis, Partial Surveils (104 downloads)

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Fuzzy Collision Avoidance Algorithm for UAVs

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Dr. John Carbone, Department of Electrical and Computer Engineering, Southern Methodist University

Abstract:
Multi-vehicle, multi-route conflict (collision) avoidance is an issue that will become prevalent over the next decade. With the dramatic increase in the number of UAV drones and number of separate companies planning to employ UAVs for everything from aerial reconnaissance to delivery to search and rescue operations, understanding how to simply and effectively keep UAVs from coming in conflict with each other is a major concern. One of the issues associated with conflict avoidance is to not have to tax the computational, memory, and energy usage of the UAV to employ a conflict resolution strategy. Here we present a fuzzy-based algorithm for detection and implementation of conflict avoidance for UAVs/ drones that will not adversely affect the power, weight, or size of the individual units.

What we propose is a simple, fuzzy logic-driven algorithm for collision/conflict avoidance for UAVs or small drones. The use of a random, fuzzy-based algorithm allows for fast, efficient computation. There would be a software agent on the drones/UAVs that would manage the collision/conflict avoidance system and send information to the flight control system for speed/directional change, depending on the output of the collision/conflict avoidance algorithms. Included will be a discussion of the ISCAN© radar system capable of implanting these algorithms on small drones.

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Fuzzy Collision Avoidance Algorithm for UAVs (97 downloads)

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Bi-Objective Study for the Assignment of Unmanned Aerial Vehicles to Targets

Authors:
Ryan D. Friese, Pacific NW National Laboratory and U.S. Department of Energy
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
Howard Jay Siegel, Electrical and Computer Engineering and Computer Science Departments, Colorado State University
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
Historically, research shows that multi-vehicle, multiconstraint, surveillance problems require a combinatorial optimization solution. In many of these surveillance missions, the overall objective is to provide plans for surveillance tasks for unmanned aerial vehicles (UAVs) visiting or “surveilling” targets across geographically distributed areas.

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Bi-Objective Study for the Assignment of Unmanned Aerial Vehicles to Targets (8 downloads)

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Autonomous Mission Planner and Supervisor (AMPS) for UAVs

Authors:
Dr. James A. Crowder, Systems Fellow, Colorado Engineering Inc.
John N. Carbone, Electrical and Computer Engineering Department, Southern Methodist University

Abstract:
To reduce mission manning and increase adaptability and evolvability for managing current operations of Unmanned Aerial Vehicle (UAV), Miniature Air-Launched Decoy (MALD) and future systems, an Autonomous Mission Planner and Supervisor (AMPS), based upon an Intelligent Information Agent (I2A) architecture for real-time, adaptive, decision making is proposed. AMPS will use a naturalistic decision-making approach to comparing sensor inputs to a priori situational “scripts” and previously collected data to improve determination/decision and execution time of appropriate actions thereby, enhancing quality and minimizing time to achieve each related mission goal….

Full access to this whitepaper requires submitting the form on the following page. Click the link below to continue.

Autonomous Mission Planner and Supervisor (AMPS) for UAVs (13 downloads)