Toward multi-target self-organizing pursuit in a partially observable Markov game

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve intelligent agents' search and pursuit capabilities. We model a self-organizing system as a partially observable Markov game (POMG) with the features of decentralization, partial observation, and noncommunication. The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that distributed noncommunicating multi-agent coordination with partial observations in all three subtasks are effective, and 2048 FSC2 agents can perform efficient multi-target SOP with almost 100% capture rates

Distributed Model Predictive Control for Cooperative Multirotor Landing on Uncrewed Surface Vessel in Waves

Heterogeneous autonomous robot teams consisting of multirotor and uncrewed surface vessels (USVs) have the potential to enable various maritime applications, including advanced search-and-rescue operations. A critical requirement of these applications is the ability to land a multirotor on a USV for tasks such as recharging. This paper addresses the challenge of safely landing a multirotor on a cooperative USV in harsh open waters. To tackle this problem, we propose a novel sequential distributed model predictive control (MPC) scheme for cooperative multirotor-USV landing. Our approach combines standard tracking MPCs for the multirotor and USV with additional artificial intermediate goal locations. These artificial goals enable the robots to coordinate their cooperation without prior guidance. Each vehicle solves an individual optimization problem for both the artificial goal and an input that tracks it but only communicates the former to the other vehicle. The artificial goals are penalized by a suitable coupling cost. Furthermore, our proposed distributed MPC scheme utilizes a spatial-temporal wave model to coordinate in real-time a safer landing location and time the multirotor's landing to limit severe tilt of the USV

A Mediator-Based Architecture for Capability Management

Colloque avec actes et comité de lecture. internationale.International audienceThe capture, the structuring and the exploitation of the expertise or the capabilities of an ``object'' (like a business partner, an employee, a software component, a Web site, etc.) are crucial problems in various applications, like cooperative and distributed applications or e-business and e-commerce applications. The work we describe in this paper concerns the advertising of the capabilities or the know-how of an object. The capabilities are structured and organized in order to be used when searching for objects that satisfy a given objective or that meet a given need. One of the originality of our proposal is in the nature of the answers the intended system can return. Indeed, the answers are not Yes/No answers but they may be cooperative answers in that sense that when no single object meets the search criteria, the system attempts to find out what a set of ``complementary'' objects that do satisfy the whole search criteria, every object in the resulting set satisfying part of the criteria. In this approach, Description Logics is used as a knowledge representation formalism and classification techniques are used as search mechanisms

Graph-Based Distributed Control for Adaptive Multi-Robot Patrolling through Local Formation Transformation

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real- world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity

Cooperative Control, Learning and Sensing in Mobile Sensor Networks

Mobile sensor networks (MSNs) have great potential in many applications including environment exploring and monitoring; search and rescue; cooperative detection of toxic chemicals, etc. Motivated by the broad and important applications of MSNs and inspired by the cooperative ability and the intelligence of fish schools and bird flocks, this dissertation develops cooperative control, learning and sensing algorithms in a distributed fashion for MSNs to realize coordinated motion control and intelligent situational awareness. The proposed algorithms can allow MSNs to track a moving target efficiently in cluttered environments and even when only a very small subset of the sensor nodes know the information of the target; adjust their size (shrink/recover) in order to adapt to complex environments while maintaining the network connectivity and topology; form a lattice structure and maintain the cohesion even when the measurements are corrupted by noise; track multiple moving targets simultaneously and efficiently in a dynamic fashion; learn to evade the enemy (predators) in a distributed fashion while maintaining the network connectivity and topology; estimate and build the map of a scalar field. We conducted several experiments using both simulation and real mobile robots to show the effectiveness of the proposed algorithms. We also extended our framework to cooperative and active sensing in which the mobile sensors have the ability to adjust their movements to adapt to the environments in order to improve the sensing performance in a distributed fashion.School of Electrical & Computer Engineerin

Coordination of Mobile Mules via Facility Location Strategies

In this paper, we study the problem of wireless sensor network (WSN) maintenance using mobile entities called mules. The mules are deployed in the area of the WSN in such a way that would minimize the time it takes them to reach a failed sensor and fix it. The mules must constantly optimize their collective deployment to account for occupied mules. The objective is to define the optimal deployment and task allocation strategy for the mules, so that the sensors' downtime and the mules' traveling distance are minimized. Our solutions are inspired by research in the field of computational geometry and the design of our algorithms is based on state of the art approximation algorithms for the classical problem of facility location. Our empirical results demonstrate how cooperation enhances the team's performance, and indicate that a combination of k-Median based deployment with closest-available task allocation provides the best results in terms of minimizing the sensors' downtime but is inefficient in terms of the mules' travel distance. A k-Centroid based deployment produces good results in both criteria.Comment: 12 pages, 6 figures, conferenc