A Multiple UAV Simulation for Researchers

Rasmussen, Steven J.; Afb, Wright-Patterson; Mitchell, Jason W.; Schulz, Christopher S.; Schumacher, Corey; Chandler, Phillip

doi:10.2514/6.2003-5684

Cited by 35 publications

(33 citation statements)

References 2 publications

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“…To test the SSBFS algorithm a number of different engagements were constructed using the MultiUAV simulation [10]. For each simulation run the vehicles started at the same location and searched a given area with a given search pattern.…”

Section: Resultsmentioning

confidence: 99%

State-space search for improved autonomous UAVs assignment algorithm

Rasmussen

Shima

Mitchell

et al. 2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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Abstract-This paper describes an algorithm that generates vehicle task assignments for autonomous uninhabited air vehicles in cooperative missions. The algorithm uses a statespace best-first search of a tree that incorporates all of the constraints of the assignment problem. Using this algorithm a feasible solution is generated immediately, that monotonically improves and eventually converges to the optimal solution. Using Monte Carlo simulations the performance of the search algorithm is analyzed and compared to the desirable assignment algorithm attributes. It is shown that the proposed deterministic search method can be implemented for given run times, providing good feasible solutions.

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Section: Resultsmentioning

confidence: 99%

State-space search for improved autonomous UAVs assignment algorithm

Rasmussen

Shima

Mitchell

et al. 2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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show abstract

“…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 computational complexity of the problem addressed in this paper is governed by the calculations of Dubin's trajectories using the path optimization function of the MultiU A V2 simulation [15]. As explained in the previous subsection, this function calculates the relevant c used in Equation (12) or (13).…”

Section: Computational Complexitymentioning

confidence: 99%

“…The cost associated with performing a single assignment by each UAV is calculated using the path optimization function of the MultiU A V2 simulation [15]. In order to obtain a flyable trajectory, the Dubin path calculation is performed using R min based on the above equations of motion.…”

Section: Path Planningmentioning

confidence: 99%

“…task times and vehicle states, evolve as the tree is constructed. The cost associated with each node is calculated by using the path optimization function of the MultiU A V2 simulation [15]. This function calculates the relevant c X l−1 l,i, j to be used in Equation (12) or (13).…”

Section: Tree Structurementioning

confidence: 99%

See 2 more Smart Citations

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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Comparison of Tabu/2‐opt heuristic and optimal tree search method for assignment problems

Jackson

Faied

Girard

2011

Intl J Robust & Nonlinear

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SUMMARYA nonlinear cooperative control problem involving several vehicles is detailed and solved. The vehicles must be assigned to perform many tasks such that they obey constraints on the order of task completion and minimize a nonlinear objective function, the total time to finish all tasks. This is an example of a combinatorial task assignment problem. A novel heuristic is introduced that represents a new combination of two combinatorial optimization tools. The quality of the solutions produced by this heuristic is demonstrated through comparison with a branch and bound search method. The branch and bound method is a well-known procedure and finds optimal solutions to the constrained, nonlinear task assignment problem.

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A Multiple UAV Simulation for Researchers

Cited by 35 publications

References 2 publications

State-space search for improved autonomous UAVs assignment algorithm

State-space search for improved autonomous UAVs assignment algorithm

Tree search algorithm for assigning cooperating UAVs to multiple tasks

Comparison of Tabu/2‐opt heuristic and optimal tree search method for assignment problems

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