Path Elongation for UAV Task Assignment

Schumacher, Corey; Chandler, Phillip; Rasmussen, Steven J.; Walker, David H.

doi:10.2514/6.2003-5585

Cited by 13 publications

(7 citation statements)

References 4 publications

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“…In such scenarios, a target can be visited by a vehicle only if a specific task had first been performed on the target and a timing constraint was fulfilled. A method to elongate minimum distance paths for constant speed vehicles to meet the target timing constraints is presented in [ 25 ]. The presented works account for the vehicles’ constraints, but they simplify the problem by assuming that the environment is obstacle free.…”

Section: Introductionmentioning

confidence: 99%

UAVs Task and Motion Planning in the Presence of Obstacles and Prioritized Targets

Gottlieb

Shima

2015

Sensors

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The intertwined task assignment and motion planning problem of assigning a team of fixed-winged unmanned aerial vehicles to a set of prioritized targets in an environment with obstacles is addressed. It is assumed that the targets’ locations and initial priorities are determined using a network of unattended ground sensors used to detect potential threats at restricted zones. The targets are characterized by a time-varying level of importance, and timing constraints must be fulfilled before a vehicle is allowed to visit a specific target. It is assumed that the vehicles are carrying body-fixed sensors and, thus, are required to approach a designated target while flying straight and level. The fixed-winged aerial vehicles are modeled as Dubins vehicles, i.e., having a constant speed and a minimum turning radius constraint. The investigated integrated problem of task assignment and motion planning is posed in the form of a decision tree, and two search algorithms are proposed: an exhaustive algorithm that improves over run time and provides the minimum cost solution, encoded in the tree, and a greedy algorithm that provides a quick feasible solution. To satisfy the target’s visitation timing constraint, a path elongation motion planning algorithm amidst obstacles is provided. Using simulations, the performance of the algorithms is compared, evaluated and exemplified.

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Section: Introductionmentioning

confidence: 99%

UAVs Task and Motion Planning in the Presence of Obstacles and Prioritized Targets

Gottlieb

Shima

2015

Sensors

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“…Note that we denote the cost of the optimal assignment as J opt i , where J opt i = min J i ; i = 1, 2. Equation (14) ensures that at each stage exactly one task on a target j ∈ T is assigned to exactly one vehicle i ∈ U ; Equation (15) ensures that on each target j ∈ T exactly N m tasks are performed; and Equation (16) ensures that the total resource requirement of the tasks from each vehicle i ∈ U does not exceed its capacity.…”

Section: Combinatorial Optimization Problemmentioning

confidence: 99%

“…The heading at the end of each task segment is calculated based on the task requirements. These calculations are based on an heading optimization using the methods described in [16]. The end conditions of one task path segment are used as the initial conditions for the next.…”

Section: Path Planningmentioning

confidence: 99%

“…before the time that the last task is to be performed, a path elongation algorithm is used, thus satisfying the timing constraints. For the path elongation, we use the algorithm described in [16] where l,i, j for l 2 is dependent on the tasks performed by vehicle i prior to step l, since those tasks effect its path and the current prerequisite task times. Thus, c X l−1 l,i, j cannot be computed a priori and is different for every assignment.…”

Section: Tree Structurementioning

confidence: 99%

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Tree search algorithm for assigning cooperating UAVs to multiple tasks

Rasmussen

Shima

2007

Intl J Robust & Nonlinear

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SUMMARYThis paper describes a tree search algorithm for assigning cooperating homogeneous uninhabited aerial vehicles to multiple tasks. The combinatorial optimization problem is posed in the form of a decision tree, the structure of which enforces the required group coordination and precedence for cooperatively performing the multiple tasks. For path planning, a Dubin's car model is used so that the vehicles' constraint, of minimum turning radius, is taken into account. Due to the prohibitive computational complexity of the problem, exhaustive enumeration of all the assignments encoded in the tree is not feasible. The proposed optimization algorithm is initialized by a best-first search and candidate optimal solutions serve as a monotonically decreasing upper bound for the assignment cost. Euclidean distances are used for estimating the path length encoded in branches of the tree that have not yet been evaluated by the computationally intensive Dubin's optimization subroutine. This provides a lower bound for the cost of unevaluated assignments. We apply these upper and lower bounding procedures iteratively on active subsets within the feasible set, enabling efficient pruning of the solution tree. Using Monte Carlo simulations, the performance of the search algorithm is analyzed for two different cost functions and different limits on the vehicles' minimum turn radius. It is shown that the selection of the cost function and the limit have a considerable effect on the level of cooperation between the vehicles. The proposed deterministic search method can be applied on line to different sized problems. For small-sized problems, it provides the optimal solution. For large-sized problems, it provides an immediate feasible solution that improves over the algorithm's run time. When the proposed method is applied off line, it can be used to obtain the optimal solution, which can be used to evaluate the performance of other sub-optimal search methods.

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“…If the path cannot be extended smoothly (e.g. UAV attack paths cannot be extended without discontinuity in some cases [11]) then there are separate intervals in which the task can be done with a period of infeasibility in between. A MILP formulation of the problem allows these constraints to be addressed 1 .…”

Section: Time-dependent Assignment Methodsmentioning

confidence: 99%

Time-dependent cooperative assignment

Kingston

Schumacher

Proceedings of the 2005, American Control Conference, 2005.

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Abstract-The problem of assigning multiple agents to timedependent cooperative tasks is addressed using a MixedInteger Linear Program. A time-dependent cooperative task is a task requiring multiple agents to perform separate subtasks 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.

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Path Elongation for UAV Task Assignment

Cited by 13 publications

References 4 publications

UAVs Task and Motion Planning in the Presence of Obstacles and Prioritized Targets

UAVs Task and Motion Planning in the Presence of Obstacles and Prioritized Targets

Tree search algorithm for assigning cooperating UAVs to multiple tasks

Time-dependent cooperative assignment

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