Joint Optimization of Sampling and Control of Partially Observable Failing Systems

2013 ◽  
Vol 61 (3) ◽  
pp. 777-790 ◽  
Author(s):  
Michael Jong Kim ◽  
Viliam Makis
Author(s):  
Chuande Liu ◽  
Chuang Yu ◽  
Bingtuan Gao ◽  
Syed Awais Ali Shah ◽  
Adriana Tapus

AbstractTelemanipulation in power stations commonly require robots first to open doors and then gain access to a new workspace. However, the opened doors can easily close by disturbances, interrupt the operations, and potentially lead to collision damages. Although existing telemanipulation is a highly efficient master–slave work pattern due to human-in-the-loop control, it is not trivial for a user to specify the optimal measures to guarantee safety. This paper investigates the safety-critical motion planning and control problem to balance robotic safety against manipulation performance during work emergencies. Based on a dynamic workspace released by door-closing, the interactions between the workspace and robot are analyzed using a partially observable Markov decision process, thereby making the balance mechanism executed as belief tree planning. To act the planning, apart from telemanipulation actions, we clarify other three safety-guaranteed actions: on guard, defense and escape for self-protection by estimating collision risk levels to trigger them. Besides, our experiments show that the proposed method is capable of determining multiple solutions for balancing robotic safety and work efficiency during telemanipulation tasks.


Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4739
Author(s):  
Ory Walker ◽  
Fernando Vanegas ◽  
Felipe Gonzalez

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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