1,269 research outputs found
Assessment of genetic algorithm based assignment strategies for unmanned systems using the multiple traveling salesman problem with moving targets
Title from PDF of title page, viewed March 1, 2023Thesis advisor: Travis FieldsVitaIncludes bibliographical references (pages 100-106)Thesis (M.S.)--Department of Civil and Mechanical Engineering. University of Missouri--Kansas City, 2021The continuous and rapid advancements in autonomous unmanned systems technologies presents increasingly sophisticated threats to military operations. These threats necessitate the prioritization of improved strategies for military resources and air base defense. In many scenarios, it is necessary to combat hostile unmanned systems before they reach the defensible perimeters of existing fixed-base defense systems. One solution to this problem is weaponizing friendly unmanned systems to hunt and kill hostile unmanned systems. However, the assignment and path planning of these âHunter-Killerâ systems to incoming hostile unmanned systems, in a multiple friendly versus multiple enemy scenario, presents a major challenge and can be represented by the Multiple Traveling Salesmen Problem with Moving Targets (MTSPMT). The MTSPMT is a combinatorial optimization problem and an extension of the classical Traveling Salesman Problem whereby the number of salesmen is increased and targets (cities) move with respect to time. The objective of the MTSPMT, for the application of military defense using a squadron of Hunter-Killer unmanned systems, is to determine a path that minimizes the cost required for multiple Hunter-Killer unmanned systems to successfully intercept all incoming threats. In this study, an assessment of genetic algorithm based assignment strategies for unmanned systems using the MTSPMT is performed. A number of scenarios were constructed using up to 50 hostile unmanned systems and the generated solutions were compared based on their resulting time to converge, solution fitness, and number of generations required. Findings indicate that under certain conditions genetic based algorithms provide better results on average and converge more rapidly than brute force searching and existing assignment and path planning solutions.Introduction -- Literature review -- Problem formulation and model design -- Methodology -- Results and Discussion -- Conclusio
Integrating forecasting in metaheuristic methods to solve dynamic routing problems: evidence from the logistic processes of tuna vessels
The multiple Traveling Salesman Problem (mTSP) is a widespread phenomenon in real-life scenarios, and in fact it has been addressed from multiple perspectives in recent decades. However, mTSP in dynamic circumstances entails a greater complexity that recent approaches are still trying to grasp. Beyond time windows, capacity and other parameters that characterize the dynamics of each scenario, moving targets is one of the underdeveloped issues in the field of mTSP. The approach of this paper harnesses a simple prediction method to prove that integrating forecasting within a metaheuristic evolutionary-based method, such as genetic algorithms, can yield better results in a dynamic scenario than their simple non-predictive version. Real data is used from the retrieval of Fish Aggregating Devices (FADs) by tuna vessels in the Indian Ocean. Based on historical data registered by the GPS system of the buoys attached to the devices, their trajectory is firstly forecast to feed subsequently the functioning of a genetic algorithm that searches for the optimal route of tuna vessels in terms of total distance traveled. Thus, although valid for static cases and for the Vehicle Routing Problem (VRP), the main contribution of this method over existing literature lies in its application as a global search method to solve the multiple TSP with moving targets in many dynamic real-life optimization problems.Ministerio de EconomĂa y Competitividad | Ref. ECO2016-76625-RXunta de Galicia | Ref. GRC2014/02
Collaborative search on the plane without communication
We generalize the classical cow-path problem [7, 14, 38, 39] into a question
that is relevant for collective foraging in animal groups. Specifically, we
consider a setting in which k identical (probabilistic) agents, initially
placed at some central location, collectively search for a treasure in the
two-dimensional plane. The treasure is placed at a target location by an
adversary and the goal is to find it as fast as possible as a function of both
k and D, where D is the distance between the central location and the target.
This is biologically motivated by cooperative, central place foraging such as
performed by ants around their nest. In this type of search there is a strong
preference to locate nearby food sources before those that are further away.
Our focus is on trying to find what can be achieved if communication is limited
or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed
making communication difficult. Furthermore, if agents do not commence the
search in synchrony then even initial communication is problematic. This holds,
in particular, with respect to the question of whether the agents can
communicate and conclude their total number, k. It turns out that the knowledge
of k by the individual agents is crucial for performance. Indeed, it is a
straightforward observation that the time required for finding the treasure is
(D + D 2 /k), and we show in this paper that this bound can be matched
if the agents have knowledge of k up to some constant approximation. We present
an almost tight bound for the competitive penalty that must be paid, in the
running time, if agents have no information about k. Specifically, on the
negative side, we show that in such a case, there is no algorithm whose
competitiveness is O(log k). On the other hand, we show that for every constant
\epsilon \textgreater{} 0, there exists a rather simple uniform search
algorithm which is -competitive. In addition, we give
a lower bound for the setting in which agents are given some estimation of k.
As a special case, this lower bound implies that for any constant \epsilon
\textgreater{} 0, if each agent is given a (one-sided)
-approximation to k, then the competitiveness is (log k).
Informally, our results imply that the agents can potentially perform well
without any knowledge of their total number k, however, to further improve,
they must be given a relatively good approximation of k. Finally, we propose a
uniform algorithm that is both efficient and extremely simple suggesting its
relevance for actual biological scenarios
Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies
We study the problem of multi-robot target assignment to minimize the total
distance traveled by the robots until they all reach an equal number of static
targets. In the first half of the paper, we present a necessary and sufficient
condition under which true distance optimality can be achieved for robots with
limited communication and target-sensing ranges. Moreover, we provide an
explicit, non-asymptotic formula for computing the number of robots needed to
achieve distance optimality in terms of the robots' communication and
target-sensing ranges with arbitrary guaranteed probabilities. The same bounds
are also shown to be asymptotically tight.
In the second half of the paper, we present suboptimal strategies for use
when the number of robots cannot be chosen freely. Assuming first that all
targets are known to all robots, we employ a hierarchical communication model
in which robots communicate only with other robots in the same partitioned
region. This hierarchical communication model leads to constant approximations
of true distance-optimal solutions under mild assumptions. We then revisit the
limited communication and sensing models. By combining simple rendezvous-based
strategies with a hierarchical communication model, we obtain decentralized
hierarchical strategies that achieve constant approximation ratios with respect
to true distance optimality. Results of simulation show that the approximation
ratio is as low as 1.4
Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction
When conducting autonomous scanning for the online reconstruction of unknown
indoor environments, robots have to be competent at exploring scene structure
and reconstructing objects with high quality. Our key observation is that
different tasks demand specialized scanning properties of robots: rapid moving
speed and far vision for global exploration and slow moving speed and narrow
vision for local object reconstruction, which are referred as two different
scanning modes: explorer and reconstructor, respectively. When requiring
multiple robots to collaborate for efficient exploration and fine-grained
reconstruction, the questions on when to generate and how to assign those tasks
should be carefully answered. Therefore, we propose a novel asynchronous
collaborative autoscanning method with mode switching, which generates two
kinds of scanning tasks with associated scanning modes, i.e., exploration task
with explorer mode and reconstruction task with reconstructor mode, and assign
them to the robots to execute in an asynchronous collaborative manner to highly
boost the scanning efficiency and reconstruction quality. The task assignment
is optimized by solving a modified Multi-Depot Multiple Traveling Salesman
Problem (MDMTSP). Moreover, to further enhance the collaboration and increase
the efficiency, we propose a task-flow model that actives the task generation
and assignment process immediately when any of the robots finish all its tasks
with no need to wait for all other robots to complete the tasks assigned in the
previous iteration. Extensive experiments have been conducted to show the
importance of each key component of our method and the superiority over
previous methods in scanning efficiency and reconstruction quality.Comment: 13pages, 12 figures, Conference: SIGGRAPH Asia 202
Particle swarm optimization for cooperative multi-robot task allocation: a multi-objective approach
This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.benchmark functions to evaluate the performance of findingthe true Pareto fronts in comparison with four existing wellknownalgorithms in continuous spaces. Afterwards, we use sixdatasets to investigate the task allocation mechanisms in dealingwith the CMRTA problem in discrete spaces
An Optimal Control Theory for the Traveling Salesman Problem and Its Variants
We show that the traveling salesman problem (TSP) and its many variants may
be modeled as functional optimization problems over a graph. In this
formulation, all vertices and arcs of the graph are functionals; i.e., a
mapping from a space of measurable functions to the field of real numbers. Many
variants of the TSP, such as those with neighborhoods, with forbidden
neighborhoods, with time-windows and with profits, can all be framed under this
construct. In sharp contrast to their discrete-optimization counterparts, the
modeling constructs presented in this paper represent a fundamentally new
domain of analysis and computation for TSPs and their variants. Beyond its
apparent mathematical unification of a class of problems in graph theory, the
main advantage of the new approach is that it facilitates the modeling of
certain application-specific problems in their home space of measurable
functions. Consequently, certain elements of economic system theory such as
dynamical models and continuous-time cost/profit functionals can be directly
incorporated in the new optimization problem formulation. Furthermore, subtour
elimination constraints, prevalent in discrete optimization formulations, are
naturally enforced through continuity requirements. The price for the new
modeling framework is nonsmooth functionals. Although a number of theoretical
issues remain open in the proposed mathematical framework, we demonstrate the
computational viability of the new modeling constructs over a sample set of
problems to illustrate the rapid production of end-to-end TSP solutions to
extensively-constrained practical problems.Comment: 24 pages, 8 figure
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