Stability of a class of multi-agent tracking systems with unstable subsystems

In this work, we pre-deploy a large number of smart agents to monitor an area of interest. This area could be divided into many Voronoi cells by using the knowledge of Voronoi diagram and every Voronoi site agent is responsible for monitoring and tracking the target in its cell. Then, a cooperative relay tracking strategy is proposed such that during the tracking process, when a target enters a new Voronoi cell, this event triggers the switching of both tracking agents and communication topology. This is significantly different from the traditional switching topologies. In addition, during the tracking process, the topology and tracking agents switch, which may lead the tracking system to be stable or unstable. The system switches either among consecutive stable subsystems and consecutive unstable subsystems or between stable and unstable subsystems. The objective of this paper is to design a tracking strategy guaranteeing overall successful tracking despite the existence of unstable subsystems. We also address extended discussions on the case where the dynamics of agents are subject to disturbances and the disturbance attenuation level is achieved. Finally, the proposed tracking strategy is verified by a set of simulations

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Decentralized Unknown Building Exploration by Frontier Incentivization and Voronoi Segmentation in a Communication Restricted Domain

Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity. Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize the travel distance of each agent in a scenario involving multiple agents to enhance overall system efficiency. To achieve this, we partition the city into subregions. and utilize Voronoi relaxation to optimize the size of postman distances for these subregions. This technique highlights the essential elements of an efficient city exploration. Expanding our exploration techniques to unknown buildings, we develop strategies tailored to this specific domain. After a careful evaluation of various exploration techniques, we introduce another goal selection strategy, Unknown Closest. This strategy combines the advantages of a greedy approach with the improved dispersal of agents, achieved through the randomization effect of a larger goal set. We further assess the exploration techniques in environments with restricted communication, presenting upper coordination mechanisms such as frontier incentivization and area segmentation. These methods enhance exploration performance by promoting independence and implicit coordination among agents. Our simulations demonstrate the successful application of these techniques in various complexity of interiors. In summary, this dissertation offers solutions for multi-robot exploration in unknown domains, paving the way for more efficient, cost-effective, and adaptable exploration strategies. Our findings have significant implications for various fields, ranging from autonomous city-wide monitoring to the exploration of hazardous interiors, where time-efficient exploration is crucial

Analysis of Online-Delaunay Navigation for Time Sensitive Targeting

Given the drawbacks of leaving time-sensitive targeting (TST) strictly to humans, there is value to the investigation of alternative approaches to TST operations that employ autonomous systems. This paper accomplishes five things. First, it proposes a short-hop abbreviated routing paradigm (SHARP) - based on Delaunay triangulations (DT), ad-hoc communication, and autonomous control - for recognizing and engaging TSTs that, in theory, will improve upon persistence, the volume of influence, autonomy, range, and situational awareness. Second, it analyzes the minimum timeframe need by a strike (weapons enabled) aircraft to navigate to the location of a TST under SHARP. Third, it shows the distribution of the transmission radius required to communicate between an arbitrary sender and receiver. Fourth, it analyzes the extent to which connectivity, among nodes with constant communication range, decreases as the number of nodes decreases. Fifth, it shows the how SHARP reduces the amount of energy required to communicate between two nodes. Mathematica 5.0.1.0 is used to generate data for all metrics. JMP 5.0.1.2 is used to analyze the statistical nature of Mathematica\u27s output

Cooperative Multi Agent Search and Coverage in Uncertain Environments

In this dissertation, the cooperative multi agent search and coverage problem in uncertain environments is investigated. Each agent individually plans its desired trajectory. The agents exchange their positions and their sensors’ measurement with their neighbouring agents through a communication channel in order to maintain the cooperation objective. Different aspects of multi agent search and coverage problem are investigated. Several models for uncertain environments are proposed and the updating rules for the probability maps are provided. Each of this models is appropriate for a specific type of problems. The cooperative search mission is first converted to a decentralized multi agent optimal path planning problem, using rolling horizon dynamic programming approach which is a mid-level controller. To make cooperation between agents possible, two approximation methods are proposed to modify the objective function of agents and to take into the account the decision of other agents. The simulation results show the proposed methods can considerably increase the performance of mission without significantly increasing the computation burden. This approach is then extended for the case with known communication delay between mobile agents. The simulation results show the proposed methods can compensate for the effect of known communication delay between mobile agents. A Voronoi-based search strategy for a team of mobile agents with limited range sensors is also proposed which combines both mid-level and low-level controllers. The strategy includes the short-term objective of maximizing the uncertainty reduction in the next step, the long-term objective of distributing the agents in the environment with minimum overlap in their sensory domain, and the collision avoidance constraint. The simulation results show the proposed control law can reduce the value of uncertainty in the environment below any desired threshold. For the search and coverage problem, we first introduce a framework that includes two types of agents; search agents and coverage agents. The problem is formulated such that the information about the position of the targets is updated by the search agents. The coverage agents use this information to concentrate around the more important areas in the environment. The proposed cooperative search method, along with a well-known Centroidal Voronoi Configuration method for coverage, is used to solve the problem. The effectiveness of the proposed algorithm is demonstrated by simulation and experiment. We then introduce the “limited turn rate Voronoi diagram” and formulate the search and coverage problem as a multi-objective optimization problem with different constraints which is able to consider practical issues like minimum fuel consumption, refueling, obstacle avoidance, and collision avoidance. In this approach, there is only one type of agents which performs both search and coverage tasks. The “multi agent search and coverage problem” is formulated such that the “multi agent search problem” and “multi agent coverage problem” are special cases of this problem. The simulation results show the effectiveness of the proposed method

Distributed navigation of multi-robot systems for sensing coverage

A team of coordinating mobile robots equipped with operation specific sensors can perform different coverage tasks. If the required number of robots in the team is very large then a centralized control system becomes a complex strategy. There are also some areas where centralized communication turns into an issue. So, a team of mobile robots for coverage tasks should have the ability of decentralized or distributed decision making. This thesis investigates decentralized control of mobile robots specifically for coverage problems. A decentralized control strategy is ideally based on local information and it can offer flexibility in case there is an increment or decrement in the number of mobile robots. We perform a broad survey of the existing literature for coverage control problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. These locally computed nearest neighbour rules are used to develop decentralized control algorithms for coverage control problems. We investigate this extensively used nearest neighbour rule-based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal importance to every neighbour robot coming under its communication range. We develop our control approach by making some of the mobile robots playing a more influential role than other members of the team. We develop the control algorithm based on nearest neighbour rules with weighted average functions. The approach based on this control strategy becomes efficient in terms of achieving a consensus on control inputs, say heading angle, velocity, etc. The decentralized control of mobile robots can also exhibit a cyclic behaviour under some physical constraints like a quantized orientation of the mobile robot. We further investigate the cyclic behaviour appearing due to the quantized control of mobile robots under some conditions. Our nearest neighbour rule-based approach offers a biased strategy in case of cyclic behaviour appearing in the team of mobile robots. We consider a clustering technique inside the team of mobile robots. Our decentralized control strategy calculates the similarity measure among the neighbours of a mobile robot. The team of mobile robots with the similarity measure based approach becomes efficient in achieving a fast consensus like on heading angle or velocity. We perform a rigorous mathematical analysis of our developed approach. We also develop a condition based on relaxed criteria for achieving consensus on velocity or heading angle of the mobile robots. Our validation approach is based on mathematical arguments and extensive computer simulations

Collision Free Navigation of a Multi-Robot Team for Intruder Interception

In this report, we propose a decentralised motion control algorithm for the mobile robots to intercept an intruder entering (k-intercepting) or escaping (e-intercepting) a protected region. In continuation, we propose a decentralized navigation strategy (dynamic-intercepting) for a multi-robot team known as predators to intercept the intruders or in the other words, preys, from escaping a siege ring which is created by the predators. A necessary and sufficient condition for the existence of a solution of this problem is obtained. Furthermore, we propose an intelligent game-based decision-making algorithm (IGD) for a fleet of mobile robots to maximize the probability of detection in a bounded region. We prove that the proposed decentralised cooperative and non-cooperative game-based decision-making algorithm enables each robot to make the best decision to choose the shortest path with minimum local information. Then we propose a leader-follower based collision-free navigation control method for a fleet of mobile robots to traverse an unknown cluttered environment where is occupied by multiple obstacles to trap a target. We prove that each individual team member is able to traverse safely in the region, which is cluttered by many obstacles with any shapes to trap the target while using the sensors in some indefinite switching points and not continuously, which leads to saving energy consumption and increasing the battery life of the robots consequently. And finally, we propose a novel navigation strategy for a unicycle mobile robot in a cluttered area with moving obstacles based on virtual field force algorithm. The mathematical proof of the navigation laws and the computer simulations are provided to confirm the validity, robustness, and reliability of the proposed methods

Marsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이범희.This dissertation presents time-efficient approaches to path planning for initial deployment and collection of a heterogeneous marsupial robot team consists of a large-scale carrier robot and multiple mobile robots. Although much research has been conducted about task allocation and path planning of multi-robot systems, the path planning problems for deployment and collection of a marsupial robot team have not been fully addressed. The objectives of the problems are to minimize the duration that mobile robots require to reach their assigned task locations and return from those locations. Taking the small mobile robot's energy constraint into account, a large-scale carrier robot, which is faster than a mobile robot, is considered for efficient deployment and collection. The carrier robot oversees transporting, deploying, and retrieving of mobile robots, whereas the mobile robots are responsible for carrying out given tasks such as reconnaissance and search and rescue. The path planning methods are introduced in both an open space without obstacles and a roadmap graph which avoids obstacles. For the both cases, determining optimal path requires enormous search space whose computational complexity is equivalent to solving a combinatorial optimization problem. To reduce the amount of computation, both task locations and mobile robots to be collected are divided into a number of clusters according to their geographical adjacency and their energies. Next, the cluster are sorted based on the location of the carrier robot. Then, an efficient path for the carrier robot can be generated by traveling to each deploying and loading location relevant to each cluster. The feasibility of the proposed algorithms is demonstrated through several simulations in various environments including three-dimensional space and dynamic task environment. Finally, the performance of the proposed algorithms is also demonstrated by comparing with other simple methods.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Multi-robot system 1 1.1.2 Marsupial robot team 3 1.2 Contributions of the thesis 9 Chapter 2 Related Work 13 2.1 Multi-robot path planning 14 2.2 Multi-robot exploration 14 2.3 Multi-robot task allocation 15 2.4 Simultaneous localization and mapping 15 2.5 Motion planning of collective swarm 16 2.6 Marsupial robot team 18 2.6.1 Multi-robot deployment 18 2.6.2 Marsupial robot 19 2.7 Robot collection 23 2.8 Roadmap generation 24 2.8.1 Geometric algorithms 24 2.8.2 Sampling-based algorithms 25 2.9 Novelty of the thesis 26 Chapter 3 Preliminaries 27 3.1 Notation 27 3.2 Assumptions 29 3.3 Clustering algorithm 30 3.4 Minimum bounded circle and sphere of a cluster 32 Chapter 4 Deployment of a Marsupial Robot Team 35 4.1 Problem definition 35 4.2 Complexity analysis 37 4.3 Optimal deployment path planning for two tasks 38 4.3.1 Deployment for two tasks in 2D space 39 4.3.2 Deployment for two tasks in 3D space 41 4.4 Path planning algorithm of a marsupial robot team for deployment 42 4.5 Simulation result 49 4.5.1 Simulation setup 49 4.5.2 Deployment scenarios in 2D space 50 4.5.3 Deployment scenarios in 3D space 57 4.5.4 Deployment in a dynamic environment 60 4.6 Performance evaluation 62 4.6.1 Computation time 62 4.6.2 Efficiency of the path 64 Chapter 5 Collection of a Marsupial Robot Team 67 5.1 Problem definition 68 5.2 Complexity analysis 70 5.3 Optimal collection path planning for two rovers 71 5.3.1 Collection for two rovers in 2D space 71 5.3.2 Collection for two rovers in 3D space 75 5.4 Path planning algorithm of a marsupial robot team for collection 76 5.5 Simulation result 83 5.5.1 Collection scenarios in 2D space 83 5.5.2 Collection scenarios in 3D space 88 5.5.3 Collection in a dynamic environment 91 5.6 Performance evaluation 93 5.6.1 Computation time 93 5.6.2 Efficiency of the path 95 Chapter 6 Deployment of a Marsupial Robot Team using a Graph 99 6.1 Problem definition 99 6.2 Framework 101 6.3 Probabilistic roadmap generation 102 6.3.1 Global PRM 103 6.3.2 Local PRM 105 6.4 Path planning strategy 105 6.4.1 Clustering scheme 106 6.4.2 Determining deployment locations 109 6.4.3 Path smoothing 113 6.4.4 Path planning algorithm for a marsupial robot team 115 6.5 Simulation result 116 6.5.1 Outdoor space without obstacle 116 6.5.2 Outdoor space with obstacles 118 6.5.3 Office area 119 6.5.4 University research building 122 Chapter 7 Conclusion 125 Bibliography 129 초록 151Docto

Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things

This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources

Towards reliable geographic broadcasting in vehicular networks

In Vehicular ad hoc Networks (VANETs), safety-related messages are broadcasted amongst cars, helping to improve drivers' awareness of the road situation. VANETs’ reliability are highly affected by channel contention. This thesis first addresses the issue of channel use efficiency in geographical broadcasts (geocasts). Constant connectivity changes inside a VANET make the existing routing algorithms unsuitable. This thesis presents a geocast algorithm that uses a metric to estimate the ratio of useful to useless packet received. Simulations showed that this algorithm is more channel-efficient than the farthest-first strategy. It also exposes a parameter, allowing it to adapt to channel load. Second, this thesis presents a method of estimating channel load for providing feedback to moderate the offered load. A theoretical model showing the relationship between channel load and the idle time between transmissions is presented and used to estimate channel contention. Unsaturated stations on the network were shown to have small but observable effects on this relationship. In simulations, channel estimators based on this model show higher accuracy and faster convergence time than by observing packet collisions. These estimators are also less affected by unsaturated stations than by observing packet collisions. Third, this thesis couples the channel estimator to the geocast algorithm, producing a closed-loop load-reactive system that allows geocasts to adapt to instantaneous channel conditions. Simulations showed that this system is not only shown to be more efficient in channel use and be able to adapt to channel contention, but is also able to self-correct suboptimal retransmission decisions. Finally, this thesis demonstrates that all tested network simulators exhibit unexpected behaviours when simulating broadcasts. This thesis describes in depth the error in ns-3, leading to a set of workarounds that allows results from most versions of ns-3 to be interpreted correctly