Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions

Augmented reality (AR) is used to enhance the perception of the real world by integrating virtual objects to an image sequence acquired from various camera technologies. Numerous AR applications in robotics have been developed in recent years. The aim of this paper is to provide an overview of AR research in robotics during the five year period from 2015 to 2019. We classified these works in terms of application areas into four categories: (1) Medical robotics: Robot-Assisted surgery (RAS), prosthetics, rehabilitation, and training systems; (2) Motion planning and control: trajectory generation, robot programming, simulation, and manipulation; (3) Human-robot interaction (HRI): teleoperation, collaborative interfaces, wearable robots, haptic interfaces, brain-computer interfaces (BCIs), and gaming; (4) Multi-agent systems: use of visual feedback to remotely control drones, robot swarms, and robots with shared workspace. Recent developments in AR technology are discussed followed by the challenges met in AR due to issues of camera localization, environment mapping, and registration. We explore AR applications in terms of how AR was integrated and which improvements it introduced to corresponding fields of robotics. In addition, we summarize the major limitations of the presented applications in each category. Finally, we conclude our review with future directions of AR research in robotics. The survey covers over 100 research works published over the last five years.

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Multi-Agent-Based Production Planning and Control

Multi-Agent-Based Production Planning and Control ◽

10.1002/9781118890073.ch3 ◽

2017 ◽

pp. 55-93

Keyword(s):

Production Planning ◽

Production Planning And Control ◽

Agent Based ◽

Planning And Control ◽

Multi Agent ◽

And Control

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Resilient Control and Safety for Multi-Agent Cyber-Physical Systems

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/753 ◽

2017 ◽

Author(s):

Anna Lukina

Keyword(s):

Winning Strategy ◽

Optimization Technique ◽

Control Algorithms ◽

Stochastic Dynamical Systems ◽

Physical Systems ◽

Planning And Control ◽

Multi Agent ◽

And Control ◽

Non Cooperative Games ◽

Synthesis Approach

I develop novel intelligent approximation algorithms for solving modern problems of CPSs, such as control and verification, by combining advanced statistical methods. it is important for the control algorithms underlying the class of multi-agent CPSs to be resilient to various kinds of attacks, and so it is for my algorithms. I have designed a very general adaptive receding-horizon synthesis approach to planning and control that can be applied to controllable stochastic dynamical systems. Apart from being fast and efficient, it provides statistical guarantees of convergence. The optimization technique based on the best features of Model Predictive Control and Particle Swarm Optimization proves to be robust in finding a winning strategy in the stochastic non-cooperative games against a malicious attacker. The technique can further benefit probabilistic model checkers and real-world CPSs.

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Multi-agent systems in production planning and control: An application to the scheduling of mixed-model assembly lines

International Journal of Production Economics ◽

10.1016/s0925-5273(99)00097-3 ◽

2000 ◽

Vol 68 (1) ◽

pp. 29-42 ◽

Cited By ~ 51

Author(s):

Maria Caridi ◽

Andrea Sianesi

Keyword(s):

Mixed Model ◽

Production Planning And Control ◽

Multi Agent Systems ◽

Assembly Lines ◽

Agent Systems ◽

Planning And Control ◽

Mixed Model Assembly Lines ◽

Multi Agent ◽

And Control ◽

Mixed Model Assembly

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A Framework for Multi-Agent UAV Exploration and Target-Finding in GPS-Denied and Partially Observable Environments

Sensors ◽

10.3390/s20174739 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4739

Author(s):

Ory Walker ◽

Fernando Vanegas ◽

Felipe Gonzalez

Keyword(s):

Remote Sensing ◽

Reinforcement Learning ◽

Search Problem ◽

Planning Problem ◽

Planning And Control ◽

Points Of Interest ◽

Multi Agent ◽

The Individual ◽

Partially Observable ◽

And Control

The problem of multi-agent remote sensing for the purposes of finding survivors or surveying points of interest in GPS-denied and partially observable environments remains a challenge. This paper presents a framework for multi-agent target-finding using a combination of online POMDP based planning and Deep Reinforcement Learning based control. The framework is implemented considering planning and control as two separate problems. The planning problem is defined as a decentralised multi-agent graph search problem and is solved using a modern online POMDP solver. The control problem is defined as a local continuous-environment exploration problem and is solved using modern Deep Reinforcement Learning techniques. The proposed framework combines the solution to both of these problems and testing shows that it enables multiple agents to find a target within large, simulated test environments in the presence of unknown obstacles and obstructions. The proposed approach could also be extended or adapted to a number of time sensitive remote-sensing problems, from searching for multiple survivors during a disaster to surveying points of interest in a hazardous environment by adjusting the individual model definitions.

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