Search using multiple UAVs with flight time constraints

Sujit, P. B.; Ghose, Debasish

doi:10.1109/taes.2004.1310000

Cited by 121 publications

(54 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we propose a method whereby the volume of airspace to be observed is discretised to form a three dimensional matrix, where each point in the matrix contains a scalar describing how uncertain the system is about the environment at that point 7 and how interesting that point is, based on concentrations of an measurable atmospheric quantity.…”

Section: A Objectives and Evaluating Fitnessmentioning

confidence: 99%

An Intelligent, Heuristic Path Planner for Multiple Agent Unmanned Air Systems

Crispin

Sóbester

2015

AIAA Infotech @ Aerospace

View full text Add to dashboard Cite

In situ observations of atmospheric variables are currently obtained with radiosondes, collecting data along uncontrolled trajectories. As an alternative, we propose an unmanned air system comprising a swarm of unmanned aerial vehicles, released from high altitude weather balloons. Their trajectories are optimised for efficient sampling, with an objective function measuring the space-filling properties of the entire swarm. The dynamics of the aircraft swarm are captured in a number of primitive manoeuvres simulated with JSBSim. A combination of these paths forms a set of moves available to a greedy heuristic algorithm, which determines which of the flyable paths is optimal according to the objective function. At each heuristic optimisation step a decision is made upon the next single move and, once the move is complete, the heuristic repeated, resulting in a stitching together of optimal moves from the flyable set. The proposed path planner comprises of a centralised algorithm, which executes offline, thus, each aircraft executes the heuristic synchronously and the result is a cloud of waypoints to be flown by the swarm. A case study based on orographic flows over South Georgia is used to test the performance of the algorithm in windy environments. The results indicate good performance of the algorithm, even in high, unsteady wind fields. I. MotivationMost commonly, balloon and aircraft launched radiosondes are utilised to collect physical, chemical and biological measurements in the atmosphere. These are predominantly used to assist the modelling of atmospheric processes from large global systems to small localised phenomena. Although relatively easy to deploy, balloon and aircraft launched radiosondes have disadvantages. The sonde is usually lost, which incurs an expense and only data transmitted during the flight can be recovered. The most notable disadvantage is the sparsity of the data collected. The sondes collect their information on an uncontrolled trajectory, determined by the flight of the (on the way up) balloon or the parachute (on the way down), which provides for limited control of the sampling locations.

show abstract

Section: A Objectives and Evaluating Fitnessmentioning

confidence: 99%

An Intelligent, Heuristic Path Planner for Multiple Agent Unmanned Air Systems

Crispin

Sóbester

2015

AIAA Infotech @ Aerospace

View full text Add to dashboard Cite

show abstract

“…The agents communicate with each other regarding formation of teams to undertake the task collectively. Other search and surveillance procedures involve communication of search routes ( [32]), locations of observed targets ( [30], [29]) and results of sensor actions in addition to UAV locations [56].…”

Section: A Knowledgementioning

confidence: 99%

“…Another approach allows the agents to learn the policies of other "neighboring" agents in addition to their own to bring about a non-direct interaction and optimal behavior of the system as a whole ( [27], [15] and [13]). Several systems such as in [28], [29], [30], [31], [32] and [8] involve explicit communication between the different agents in the system to bring about coordination.…”

Section: Introductionmentioning

confidence: 99%

An adaptive-learning framework for semi-cooperative multi-agent coordination

Boukhtouta

Berger

Powell

et al. 2011

2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

View full text Add to dashboard Cite

Abstract-Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent.

show abstract

“…At present, many research focus on the UAV real-time online planning. UAV research focuses on multi UAV cooperative search method description [4], multi UAV search path optimization [5][6], the exchange of information between planning and search of multi UAV [7][8][9] and so on. These research made the assumption for the distribution of the targets that designated search area at first, and then find regional goals.…”

Section: Introductionmentioning

confidence: 99%

Research on Aerospace Cooperative Continuation Observation Strategy for Maritime Moving Target

et al. 2016

View full text Add to dashboard Cite

Abstract. Maritime moving targets continuation observation has important implications to maintain the safety of the navigation barrier. Existing methods either rely on satellite observations, or only rely on UAV observations. Even though the two cooperative observations, it confined to a simple task allocation. They did not solve the problem of maritime moving targets continuation observation. On the basis of difference between satellites observation and UAV observation, we constructed the aerospace collaborative continuation observation model for maritime moving target, and put forward aerospace collaborative continuation observation strategy (ACCOS) to reduce the complexity of solving the model. According to the spatial and temporal characteristics of the task, ACCOS extracted five sub problems to solve the model: Satellite planning, UAV flight plan, UAV observation sequence, target potential area and distribution probability density prediction, UAV path planning. In order to realize the optimization goal of the model, the five sub problems are modelled and solved in turn. Finally, the simulation results show that the proposed method can greatly reduce the average observation period of the target, and effectively solve the problem of the continuous observation of maritime moving target.

show abstract

Search using multiple UAVs with flight time constraints

Cited by 121 publications

References 15 publications

An Intelligent, Heuristic Path Planner for Multiple Agent Unmanned Air Systems

An Intelligent, Heuristic Path Planner for Multiple Agent Unmanned Air Systems

An adaptive-learning framework for semi-cooperative multi-agent coordination

Research on Aerospace Cooperative Continuation Observation Strategy for Maritime Moving Target

Contact Info

Product

Resources

About