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Approximate multi-agent planning in dynamic and uncertain environments
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Collaborative Task Allocation and Motion Planning for Multi-Agent Systems in the Presence of Adversaries
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Offline Learning for Stochastic Multi-Agent Planning in Autonomous Driving
Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and revolutionize how people travel and how we transport goods. Many of the major challenges for autonomous driving systems emerge from the numerous traffic situations that require complex interactions with other agents. For the foreseeable future, autonomous vehicles will have to share the road with human drivers and pedestrians, and thus cannot rely on centralized communication to address these interactive scenarios. Therefore, autonomous driving systems need to be able to negotiate and respond to unknown agents that exhibit uncer?tain behavior. To tackle these problems, most commercial autonomous driving stacks use a modular approach that splits perception, agent forecasting, and planning into separately engineered modules. However, fully separating prediction and planning makes it difficult to reason how other vehicles will respond to the planned trajectory for the controlled ego-vehicle. So to maintain safety, many modular approaches have to be overly conservative when interacting with other agents. Ideally, we want autonomous vehicles to drive in a natural and confident manner, while still maintaining safety.
Thus, in this thesis, we will explore how we can use deep learning and offline reinforce?ment learning to perform joint prediction and planning in highly interactive and stochastic multi-agent scenarios in autonomous driving. First, we discuss our work in using deep learning for joint prediction and closed-loop planning in an offline reinforcement learning (RL) framework (Chapter 2). Second, we discuss our work that directly tackles the difficulties of using learned models to do planning in stochastic multimodal settings (Chapter 3). Third, we discuss how we can scale to more complicated multi-agent driving scenarios like merging in dense traffic by using a Transformer-based traffic forecasting model as our world model (Chapter 4). Finally, we discuss how we can draw from offline model-based RL to learn a high-level policy that selects over a discrete set of pre-trained driving skills to perform effective control without additional online planning (Chapter 5).
Degree Type
- Dissertation
- Robotics Institute
Degree Name
- Doctor of Philosophy (PhD)
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- Adaptive Agents and Intelligent Robotics
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A stochastic process approach for multi-agent path finding with non-asymptotic performance guarantees
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The Multi-Agent Path Finding problem aims to find a set of collision-free paths that minimizes the total cost of all paths. The problem is extensively studied in artificial intelligence due to its relevance to robotics, video games and ...
- The MAPF problem finds minimal-cost collision-free paths for cooperating agents.
MET-MAPF: A Metamorphic Testing Approach for Multi-Agent Path Finding Algorithms
The Multi-Agent Path Finding (MAPF) problem, i.e., the scheduling of multiple agents to reach their destinations, has been widely investigated. Testing MAPF systems is challenging, due to the complexity and variety of scenarios and the agents’ ...
Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while improving the solution quality. The state-of-the-art anytime MAPF algorithm is based on Large Neighborhood Search (...
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Elsevier Science Publishers Ltd.
United Kingdom
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- Multi-agent path finding
- Non-asymptotic performance
- Stochastic process
- Multi-armed bandit problem
- Research-article
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- Corpus ID: 67107135
Multi-agent planning using HTN and GOAP
- Glenn Wissing
- Published 2007
- Computer Science
6 Citations
Goal-oriented action planning in partially observable stochastic domains, avoiding threats using multi agent system planning for web based systems, maspta-o: multiagent system planning to avoid threats optimally, extracting plans from plans, layered security architecture for threat management using multi-agent system, mitigating multi-threats optimally in proactive threat management, 6 references, temporal enhancements of an htn planner, hierarchical plan representations for encoding strategic game ai, applying goal-oriented action planning to games, hierarchical task network planning, generating parallel execution plans with a partial-order planner, related papers.
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Multi agent strategy planning using GraWolf method (thesis for MSc thesis)
peterkisfaludi/thesis-multi-agent-strategy-planning
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Thesis: multi agent strategy planning.
Multi agent strategy planning using Reinforcemet Learning (with gradient descent optimization) This was the thesis project for MSc
- MATLAB 98.4%
- Objective-C 0.5%
Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. We propose a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches. The videos and supplementary material are available at https://sites.google.com/view/maner-supplementary
Degree Type
- Master of Science
- Computer Science
Campus location
- West Lafayette
Advisor/Supervisor/Committee Chair
Advisor/supervisor/committee co-chair, additional committee member 2, usage metrics.
- Computer vision
- Intelligent robotics
IMAGES
VIDEO
COMMENTS
Multi-agent control and path-planning strategies are broken into two categories: centralized and decentralized (De-wangan, Shukla, and Godfrey,2017). The centralized approach models the multi-agent team or swarm as a singular unit. The decentralized approach, often also referred to as a distributed approach, calculates each agent's control ...
BELIEF SPACE HIERARCHICAL PLANNING IN THE NOW Caris Moses A thesis submitted for the degree of Master of Science December 2015. Contents 1 Introduction 4 ... In general, the complexity of centralized multi-agent planning (MAP) is exponential in the number of agents. Therefore, a decentralized planning approach is taken to enable UAV teaming ...
This thesis presents real-time robust distributed planning strategies that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. One class of distributed combinatorial planning algorithms involves using auction algorithms augmented with consensus protocols to allocate tasks amongst a team of ...
This thesis aims to address the issues of the issues of scalability and adaptability within teams of such inter-dependent agents while planning, coordinating, and learning in a decentralized environment. In doing so, the first focus is the integration of learning and adaptation algorithms into a multi-agent planning architecture to enable ...
The Eighth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS 2009), May 10 -15, 2009, Budapest, Hungary. 2. J. F. Zhang, X. T. Nguyen, R. Kowalczyk A Graph-based Multi-agent Planning Al-gorithm with QoS guarantees. The 2007 IEEE/WIC/ACM International Conference
2 Optimizing Parallel Task Execution for Multi-Agent Mission Planning Keywords: Multi-Robot Task Allocation, Parallel Task Execution, Integer Linear Programming 1 Introduction Multi-Agent1 Systems (MASs) have been widely present in the robotics domain in the application areas of navigation, cooperation, and planning [1,2]. The
A Hybrid Method for Distributed Multi-Agent Mission Planning System. The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and ...
In this dissertation, we consider a multi-agent area defense game that consists of 1) a swarm of autonomous, adversarial robotic vehicles (called attackers) that aims to reach a safety-critical area, 2) a team of autonomous robotic vehicles (called defenders) that aims to prevent the attackers from reaching the safety-critical area and thereby ...
Ideally, we want autonomous vehicles to drive in a natural and confident manner, while still maintaining safety. Thus, in this thesis, we will explore how we can use deep learning and offline reinforce?ment learning to perform joint prediction and planning in highly interactive and stochastic multi-agent scenarios in autonomous driving.
This thesis deals with the formal definition of two particular Multi-Robot Task Allocation (MRTA) problem configurations used to represent multi-agent mission planning problems. More specifically, the contribution of this thesis can be grouped into three categories.
This thesis proposes a novel multi-agent planning approach which distributively intersects local plans of the agents towards a global solution of the multi-agent plan-ning problem. This core principle builds on local compilation to a classical planning problem and compact representation of the local plans in the form of Finite State Ma-chines.
Multi-agent path finding (MAPF) is a classical NP-hard problem that considers planning collision-free paths for multiple agents simultaneously. ... Iterative refinement for real-time multi-robot path planning, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Republic, September 27-Oct. 1, 2021 ...
the assumptions that (a) the agents' actions are instantaneous and (b) the observations are perfect and reliable. Importantly, the thesis proposes to conceive the computational core of the MAPR problem as an AI planning task, thus enabling the use of existing planning tools. The thesis then introduces di erent AI planning-based computational
Thesis submitted in partial fulfillment ... multi-agent planning & pathfinding (MAPF) and heuristic search, improving my research skills immensely. Their motivation, knowledge, and hard work truly inspire me. A big thank goes to all my friends in Ben-Gurion University of the Negev for their
This thesis presents a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed, and presents the Constraint Manifold Subsearch (CMS) algorithm to solve problems where robots must dynamically form and dissolve teams with other robots to perform cooperative tasks.
tributed Multi-Agent Mission Planning System. Major Professor: Shaoshuai Mou. This thesis presents work concerning a distributed heterogeneous multi-agent robotic team for outdoor applications such as search and rescue or surveillance. The goal of this research is to develop a method of control for a team of unmanned aerial
Malardalen University Doctoral Thesis¨ No.353 Multi-Agent Mission Planning ... Multi-agent-system (MAS) har anv¨ants i olika omgivningar och ramverk och har pa s˚ a s˚ att framg¨ angsrikt till˚ ampats i en m¨ angd applikationer f¨ or att uppn¨ ˚a olika m˚al. Det har visat sig att MAS ¨ar mer kostnadseffektiva j amf¨ ort med att¨
Solving multi‑agent path planning by local search algorithms Wang, Wenjie 2014 Wang, W. (2014). ... agents or the map size increases. This thesis specifically addresses two MAPP optimization problems, namely total energy cost minimized MAPP and makespan minimized MAPP. The
Multi-agent planning using HTN and GOAP. Glenn Wissing. Published 2007. Computer Science. This thesis examines how Hierarchical Task Networks (HTNs) can be used to plan for overall strategies in a game environment where the agents are controlled by GOAP (Goal Oriented Action Plannin ... diva-portal.org.
Multi-Agent Path Planning for On-Orbit Servicing Applications. Download (14.23 MB) thesis. posted on 2024-05-08, 17:09 authored by Ritik K Mishra. The research presented in this thesis presents methods to solve multi-agent task allocation and path planning problems in the application of on-orbit servicing. History.
Multi agent system: concepts, platforms, and applications in power systems. The vital changes are experienced by the power system and. it is advancing from a centralized structure to a decen ...
BACHELOR'S THESIS Multi-agent planning using HTN and GOAP Glenn Wissing Luleå University of Technology BSc Programmes in Engineering BSc programme in Computer Engineering Department of Skellefteå Campus Division of Leisure and Entertainment 2007:16 HIP - ISSN: 1404-5494 - ISRN: LTU-HIP-EX--07/16--SE.
Thesis: Multi agent strategy planning. Multi agent strategy planning using Reinforcemet Learning (with gradient descent optimization) This was the thesis project for MSc. About. Multi agent strategy planning using GraWolf method (thesis for MSc thesis) Resources. Readme Activity. Stars. 0 stars Watchers. 2 watching
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. We propose a comprehensive learning-based framework for multi ...
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