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

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.

Benchmarking and Robust Multi-Agent-Based Production Planning and Control

2003 ◽  
Vol 36 (3) ◽  
pp. 249-254
Author(s):  
Daniel Frey ◽  
Jens Nimis ◽  
Heinz Wörn ◽  
Peter Lockemann
Keyword(s):  
Production Planning ◽  
Production Planning And Control ◽  
Agent Based ◽  
Planning And Control ◽  
Multi Agent ◽  
And Control

scholarly journals Augmented Reality for Robotics: A Review

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Zhanat Makhataeva ◽  
Huseyin Varol
Keyword(s):  
Augmented Reality ◽  
Human Robot Interaction ◽  
Multi Agent Systems ◽  
Planning And Control ◽  
Robot Swarms ◽  
Wearable Robots ◽  
Camera Localization ◽  
Recent Developments ◽  
Multi Agent ◽  
And Control

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.

Fall on Backpack: Damage Minimizing Humanoid Fall on Targeted Body Segment Using Momentum Control

2011 ◽  
Author(s):  
Sung-Hee Lee ◽  
Ambarish Goswami
Keyword(s):  
Impact Damage ◽  
Humanoid Robots ◽  
Control Algorithms ◽  
Body Segment ◽  
Dynamic Coupling ◽  
Planning And Control ◽  
Critical Issues ◽  
Absorbing Material ◽  
And Control ◽  
The Impact

Safety and robustness will become critical issues when humanoid robots start sharing human environments in the future. In physically interactive human environments, a catastrophic fall is the main threat to safety and smooth operation of humanoid robots, and thus it is critical to explore how to manage an unavoidable fall of humanoids. This paper deals with the problem of reducing the impact damage to a robot associated with a fall. A common approach is to employ damage-resistant design and apply impact-absorbing material to robot limbs, such as the backpack and knee, that are particularly prone to fall related impacts. In this paper, we select the backpack to be the most preferred body segment to experience an impact. We proceed to propose a control strategy that attempts to re-orient the robot during the fall such that it impacts the ground with its backpack. We show that the robot can fall on the backpack even when it starts falling sideways. This is achieved by utilizing dynamic coupling, i.e., by rotating the swing leg aiming to generate spin rotation of the trunk (backpack), and by rotating the trunk backward to drive the trunk to touch down with the backpack. The planning and control algorithms for fall are demonstrated in simulation.

Minimum jerk for trajectory planning and control

Robotica ◽  
1994 ◽  
Vol 12 (2) ◽  
pp. 109-113 ◽  
Author(s):  
K.J. Kyriakopoulos ◽  
G.N. Saridis
Keyword(s):  
Trajectory Planning ◽  
Tracking Control ◽  
Optimization Problem ◽  
Control Algorithms ◽  
Planning Problem ◽  
Cartesian Space ◽  
Planning And Control ◽  
Minimum Jerk ◽  
And Control ◽  
Trajectory Planning And Control

SUMMARYIt has been experimentally verified that the jerk of the desired trajectory adversely affects the performance of the tracking control algorithms for robotic manipulators. In this paper, we investigate the reasons behind this effect, and state the trajectory planning problem as an optimization problem that minimizes a norm of joint jerk over a prespecified Cartesian space trajectory. The necessary conditions are derived and a numerical algorithm is presented.

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

Sensors ◽  
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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