TY  - GEN
N2  - The level of maturity of maritime autonomous vehicles makes the deployment of networks of vehicles costeffective with an increasing number of applications that today require the usage of autonomous robots. To maximise operational effectiveness, one critical decision variable that has a direct impact on both the cost and performance, lies in how to properly place the different robots/sensors in the target environment. The number of assets, the dedicated communication networks, can contribute a significant portion of the overall cost and their placement determines the overall mission time and the associated coverage area. The ability to quickly allocate the available vehicles to one specific area, to monitor the mission performance, and to understand the quality of the data gathered would represent a key capability to speed up the uptake of the technology even more. This paper aims at bridging this gap, and provides a solution to efficiently schedule multi-vehicle, multi-days and large areas campaigns. The output of the campaign planner is a set of goals (e.g. areas to survey, targets to reacquire), which can then be allocated to the vehicles for execution. Finally, to maximise vehicle interoperability this paper also report an automated translator from high-level mission plans to vehicle to vehicle specific commands. Results are reported through simulations.
AB  - The level of maturity of maritime autonomous vehicles makes the deployment of networks of vehicles costeffective with an increasing number of applications that today require the usage of autonomous robots. To maximise operational effectiveness, one critical decision variable that has a direct impact on both the cost and performance, lies in how to properly place the different robots/sensors in the target environment. The number of assets, the dedicated communication networks, can contribute a significant portion of the overall cost and their placement determines the overall mission time and the associated coverage area. The ability to quickly allocate the available vehicles to one specific area, to monitor the mission performance, and to understand the quality of the data gathered would represent a key capability to speed up the uptake of the technology even more. This paper aims at bridging this gap, and provides a solution to efficiently schedule multi-vehicle, multi-days and large areas campaigns. The output of the campaign planner is a set of goals (e.g. areas to survey, targets to reacquire), which can then be allocated to the vehicles for execution. Finally, to maximise vehicle interoperability this paper also report an automated translator from high-level mission plans to vehicle to vehicle specific commands. Results are reported through simulations.
AD  - SeeByte ltd, Edinburgh
AD  - SeeByte ltd, Edinburgh
AD  - SeeByte ltd, Edinburgh
AD  - SeeByte ltd, Edinburgh
AD  - SeeByte ltd, Edinburgh
AD  - SeeByte ltd, Edinburgh
T1  - Vehicle agnostic area coverage maximisation for campaign planning
DA  - 2020-10-05
AU  - Munafo, F
AU  - Laskov, A
AU  - Opgenoorth, P
AU  - Whetton, B
AU  - Mignotte, P
AU  - Reed, S
L1  - https://library.imarest.org/record/7689/files/INEC_2020_Paper_76.pdf
JF  - Conference Proceedings of INEC
VL  - INEC 2020
PY  - 2020-10-05
ID  - 7689
L4  - https://library.imarest.org/record/7689/files/INEC_2020_Paper_76.pdf
KW  - Mission planners
KW  - Autonomous Systems
KW  - Autonomous Underwater Vehicles
KW  - Autonomous Surface Vessels
KW  - Marine systems
TI  - Vehicle agnostic area coverage maximisation for campaign planning
Y1  - 2020-10-05
L2  - https://library.imarest.org/record/7689/files/INEC_2020_Paper_76.pdf
LK  - https://www.imarest.org/events/inec-2020
LK  - https://library.imarest.org/record/7689/files/INEC_2020_Paper_76.pdf
UR  - https://www.imarest.org/events/inec-2020
UR  - https://library.imarest.org/record/7689/files/INEC_2020_Paper_76.pdf
ER  -