An Introduction to MultiAgent Systems.

Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.

Drone networks: Communications, coordination, and sensing

Application of robotics in offshore oil and gas industry- A review Part II

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics

This paper addresses the problem of cooperative control of a team of distributed agents with decoupled nonlinear discrete-time dynamics, which operate in a common environment and exchange-delayed information between them. Each agent is assumed to evolve in discrete-time, based on locally computed control laws, which are computed by exchanging delayed state information with a subset of neighboring agents. The cooperative control problem is formulated in a receding-horizon framework, where the control laws depend on the local state variables (feedback action) and on delayed information gathered from cooperating neighboring agents (feedforward action). A rigorous stability analysis exploiting the input-to-state stability properties of the receding-horizon local control laws is carried out. The stability of the team of agents is then proved by utilizing small-gain theorem results.

Cooperative Constrained Control of Distributed Agents With Nonlinear Dynamics and Delayed Information Exchange: A Stabilizing Receding-Horizon Approach

This paper considers a heterogeneous team of cooperating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets The locations of some (or all) targets are unknown a priori, requiring them to be located using cooperative search In addition, the tasks to be performed at each target location by the team of cooperative UAVs need to be coordinated The tasks must, therefore, be allocated to UAVs in real time as they arise, while ensuring that appropriate vehicles are assigned to each task Each class of UAVs has its own sensing and attack capabilities, so the need for appropriate assignment is paramount In this paper, an extensive dynamic model that captures the stochastic nature of the cooperative search and task assignment problems is developed, and algorithms for achieving a high level of performance are designed The paper focuses on investigating the value of predictive task assignment as a function of the number of unknown targets and number of UAVs In particular, it is shown that there is a tradeoff between search and task response in the context of prediction Based on the results, a hybrid algorithm for switching the use of prediction is proposed, which balances the search and task response The performance of the proposed algorithms is evaluated through Monte Carlo simulations

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Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams

This paper considers the problem of cooperative search and task response in a heterogeneous team of unmanned aerial vehicles (UAVs) with limited communication. The UAVs are engaged in a mission to search and verifiably destroy targets in an uncertain environment. The UAVs do not have access to centralized information, and each UAV makes its decisions based on subjective information obtained through observation and communication with other UAVs. We present a strategy for information sharing and fusion, and study the impact of this strategy's parameters on the performance of the UAV team.

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Information Sharing in Cooperative Unmanned Aerial Vehicle Teams

In this paper, we consider a heterogeneous team of UAVs drawn from several distinct classes and engaged in a search and destroy mission over a spatially extended battlefield with targets of several types. Some target locations are suspected a priori with a certain probability, while the rest need to be detected gradually through search. The tasks are determined in real-time by the actions of all UAVs and their consequences (e.g., sensor readings), which makes the task dynamics stochastic. The tasks must, therefore, be allocated to UAVs in real-time as they arise. Quick response is more important for known targets, while efficient search is necessary to discover hidden targets. Prediction may help when most targets are known a priori, but could hurt when they are not. In this paper, we study how the benefit of such prediction may depend on the number of targets and UAVs. In particular, we show that there is a trade-off between search and task response in the context of prediction. Based on the results, we propose a hybrid algorithm which balances the search and task response. The performance of proposed algorithms is evaluated through Monte Carlo simulations.

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Balancing search and target response in cooperative UAV teams

This paper focuses on the problem of cooperative search and task response in a heterogeneous team of autonomous mobile agents with limited communication. The agents are engaged in a mission to search for targets, to verify their existence, and to take appropriate action for each verified target in an uncertain environment. Each agent makes its decisions based on a subjective information base (SIB), constructed through observation and communication with other agents. Communication between agents is of two types: type I) Each agent periodically broadcasts recently obtained or generated information over a limited radius; and type II) When two agents come sufficiently close, they exchange and merge their entire SIBs. Type I communication results in the diffusion of information among all the agents, and is, in principle, sufficient for decision-making. However, for a fixed population of agents with specified dynamics, this places a lower limit on the range and frequency of broadcast. We hypothesize that including a small amount of type II communication allows for significant decrease in these limits, and leads to a more efficient system. This hybrid approach represents a general communication strategy for multi-agent systems.

Sporadic pairwise synchronization for decentralized decision-making in mobile agent teams

In an attempt to realize decentralized cooperation and coordination in a heterogeneous team of autonomous mobile agents, we have proposed a platform to model many practical issues encountered in the process. The system includes an unexplored environment and a team of agents with different capabilities. The agents’ movement follows trajectories limited by speed and turning radius, and their actions may be affected by their orientation relative to targets. The stochasticity introduced by task transition makes off-line task assignment inapplicable. A basic distributed real-time assignment algorithm and an intelligent predictive decision making strategy are proposed and compared. The results show that prediction can help improve the performance of cooperative agent teams engaged in search-and-engage missions. These algorithms are implemented with the assumption of perfect communication. The assumption of perfect communication is relaxed in the second part of the work reported, resulting in a truly decentralized system that is simulated in a somewhat simpler mission scenario. Each agent in this decentralized model has its own subjective information base (SIB) based on limited observations and information sharing, and uses a set of information fusion heuristics to maintain this SIB. The performance of the system is evaluated relative to the degree of information sharing. The results indicate that cooperation based on information exchange – even if it is imperfect – can produce excellent performance. A hybrid communication strategy combining occasional limited-range communication and opportunistic peer-to-peer synchronization is then proposed. Using Monte Carlo simulations, it is shown that this strategy can significantly supplement the efficacy of a diffusive information sharing strategy.

Yan Liao

Papers

Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams

Information Sharing in Cooperative Unmanned Aerial Vehicle Teams

Balancing search and target response in cooperative UAV teams

Sporadic pairwise synchronization for decentralized decision-making in mobile agent teams

Decentralized Decision Making and Information Sharing in a Team of Autonomous Mobile Agents