Conference Paper

State-space search for improved autonomous UAVs assignment algorithm

Air Vehicles Directorate, Air Force Res. Lab., Wright-Patterson AFB, OH, USA
DOI: 10.1109/CDC.2004.1428908 Conference: Decision and Control, 2004. CDC. 43rd IEEE Conference on, Volume: 3
Source: IEEE Xplore


This paper describes an algorithm that generates vehicle task assignments for autonomous uninhabited air vehicles in cooperative missions. The algorithm uses a state-space 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.

1 Follower
4 Reads
  • Source
    • "A similar problem, where a set of agents must perform a fixed number of different tasks on a set of targets, has been addressed by several authors. The methods developed include exhaustive enumeration [8], branch-and-bound [7], network models [9] [10], and dynamic programming [5] "
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a dynamic programming approach to address the problem of determining how a structured formation of autonomous undistinguishable agents can be reorganized into another, eventually non-rigid, formation based on changes in the environment, perhaps unforeseeable. The methodology can also be used to correctly position the agents into a particular formation from an initial set of random locations. Given the information of the current agents location and the final locations, there are n! possible permutations for the n agents, and we seek the one that minimizes a total relative measure, e.g. distance traveled by the agents during the switching. Possible applications can be found amongst surveillance, damage assessment, chemical or biological monitoring, among others, where the switching to another formation, not necessarily predefined, may be required due to changes in the environment.
  • Source
    • "During generation of the tree all of the requirements of the mission are met, but since enumeration of all of the feasible assignments is needed, direct use of this approach is only practical for relatively low dimensional problems and off-line applications. For on-line applications a branch and bound algorithm has been proposed (Rasmussen et al. 2004). This deterministic search method has desirable qualities such as providing feasible solutions, that monotonically improves and, eventually, converges to the optimal solution. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes an algorithm for autonomous strategic mission planning of missions where multiple unhabitated underwater vehicles (UUVs) cooperate in order to solve one or more mission tasks. Missions of this type include multi-agent reconnaissance missions and multi-agent mine sweeping missions. The mission planning problem is posed as a receding horizon mixed–integer constrained quadratic optimal control problem. This problem is subsequently partitioned into smaller subproblems and solved in a parallel and decentralized manner using a distributed Nash-based game approach. The paper presents the development of the proposed algorithm and discusses its properties. An application example is used to further demonstrate the main characteristics of the proposed method.
    International Journal of Control 08/2007; 80(7):1169-1179. DOI:10.1080/00207170701253363 · 1.65 Impact Factor
  • Source
    • "However, since it requires an enumeration of all the feasible assignments, direct use of this approach is only reasonable for relatively low-dimensional scenarios and off-line applications. For an on-line application, a best first search (BFS) algorithm that uses the branch and bound concept has been proposed [10]. This deterministic greedy search method has desirable qualities such as providing immediately a feasible solution, that improves monotonically and, eventually, converges to the optimal solution. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A problem of assigning cooperating uninhabited aerial vehicles to perform multiple tasks on multiple targets is posed as a new combinatorial optimization problem. A genetic algorithm for solving such a problem is proposed. The algorithm allows us to efficiently solve this NP-hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows us to take into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and trajectory limitations. A matrix representation of the genetic algorithm chromosomes simplifies the encoding process and the application of the genetic operators. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm by showing that it consistently and quickly provides good feasible solutions. This makes the real time implementation for high-dimensional problems feasible.
    Computers & Operations Research 11/2006; 33(11-33):3252-3269. DOI:10.1016/j.cor.2005.02.039 · 1.86 Impact Factor
Show more


4 Reads
Available from