TY - GEN
T1 - Extended Abstract
T2 - 13th UKACC International Conference on Control, CONTROL 2022
AU - Rajendran, S.
AU - Nordin, M. H.
AU - Sharma, S.
AU - Khan, A.
AU - Gianni, M.
AU - Sutton, R.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - It is challenging to optimize the human-machine control authority allocation for an autonomous marine vessel in general. It is crucial to establish an effective scheme which achieves an optimal coordination between the human operator/crew and the vessel countering any threats to crew or vessel, sensory faults and other hostile operating conditions. An intelligent scheme which in cooperates the potential threats via learning-based modelling which could forecast the restrictions on navigation and control based on redundant sources to execute different level of shared control authority (between a human operator either on-bard or on-shore station and the vessel) with respect to International Maritime Organization (IMO) classification on degrees of autonomy. The scheme systematically constructs a decision-based smooth control allocation based on the potential sensor vulnerabilities (spoofing, interference, GNSS segment errors, jamming, scintillation, solar activity etc.,) and the factors of human-error which causes collision as per the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Hence, this paper presents a scheme that establishes a topology for shared control authority for marine vessels considering the fact that still standards and regulations regarding marine autonomy still lack clarity and evolving. Hence, this would facilitate integration of existing Collision Avoidance Systems with an intelligent operator which regulates the intervention of human-operator in the loop for increased autonomy, safety and optimal cooperation.
AB - It is challenging to optimize the human-machine control authority allocation for an autonomous marine vessel in general. It is crucial to establish an effective scheme which achieves an optimal coordination between the human operator/crew and the vessel countering any threats to crew or vessel, sensory faults and other hostile operating conditions. An intelligent scheme which in cooperates the potential threats via learning-based modelling which could forecast the restrictions on navigation and control based on redundant sources to execute different level of shared control authority (between a human operator either on-bard or on-shore station and the vessel) with respect to International Maritime Organization (IMO) classification on degrees of autonomy. The scheme systematically constructs a decision-based smooth control allocation based on the potential sensor vulnerabilities (spoofing, interference, GNSS segment errors, jamming, scintillation, solar activity etc.,) and the factors of human-error which causes collision as per the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Hence, this paper presents a scheme that establishes a topology for shared control authority for marine vessels considering the fact that still standards and regulations regarding marine autonomy still lack clarity and evolving. Hence, this would facilitate integration of existing Collision Avoidance Systems with an intelligent operator which regulates the intervention of human-operator in the loop for increased autonomy, safety and optimal cooperation.
KW - Artificial Intelligence (AI)
KW - Human-Machine interaction
KW - Marine Autonomous Systems
KW - Shared control authority
UR - http://www.scopus.com/inward/record.url?scp=85132275328&partnerID=8YFLogxK
U2 - 10.1109/Control55989.2022.9781464
DO - 10.1109/Control55989.2022.9781464
M3 - Conference contribution
AN - SCOPUS:85132275328
T3 - 2022 13th UKACC International Conference on Control, CONTROL 2022
SP - 154
EP - 155
BT - 2022 13th UKACC International Conference on Control, CONTROL 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 April 2022 through 22 April 2022
ER -