Task 5. Goals and Plan of Action.
Describe briefly your goals and plan of action between now and the final flight demonstrations. Include any diagrams / schedule as needed.
We have split up our group into subteams, each with a specific area they are in charge of. However, these teams are not rigid, and members can freely move around to support others when needed.
Hardware Leads: Ben & Leila
Software Leads: Aaron & Ianis
Strategy Leads: Matt & Gael
Hardware Leads: Ben & Leila
- End Goals:
- Optimise efficiency & determine power penalty of physical design modifications
- Realisation of physical modification
- Optimise efficiency & determine power penalty of physical design modifications
- Short-Term Goals/Work:
- Determining performance & limitations (i.e. all the parameters needed to educate the strategy)
- Set design constraints on the wing at Re = uL/ν = 14m/s × 0.14m / 1.568e-5 m2/s = 125,000
- Choose starting airfoil, test in XFLR5 for our design parameters
- Preliminary modification designs
- Determining performance & limitations (i.e. all the parameters needed to educate the strategy)
Software Leads: Aaron & Ianis
- End Goals:
- Coupling of ride notification interface, algorithm backend (on Windows), mobile software (on Android), and on-board software
- Optimise flight control system accounting for physical modifications & strategy
- Coupling of ride notification interface, algorithm backend (on Windows), mobile software (on Android), and on-board software
- Short-Term Goals/Work:
- Set up SDK and learn APIs (Android + Windows)
- Modify mobile software and use DJI Assistant 2 on computer to permit access and collection of all available on-board data
- Set up SDK and learn APIs (Android + Windows)
Strategy Leads: Matt & Gael
- End Goals:
- Optimise path-planning & bidding strategy
- Generalise strategy to allow for unknown parameters, i.e. outputs = f(inputs)
- Optimise path-planning & bidding strategy
- Short-Term Goals/Work:
- ID parameters, variables (inputs), outputs, and optimisation quantities (and determine what sort of performance data is needed, i.e. parameter inputs to the cost function)
- G(P(u), Etakeoff, Elanding, fride,wspeed,dpassenger, ddestination, uwind, … ) = β1P(u) + β2Etakeoff + … where βi are the parameter weights for the cost function
- Research of various path-planning, cost-benefit, and bidding algorithms or mathematical models in existence (such as in Taxis, Uber, Travelling-Salesman contexts)
- ID parameters, variables (inputs), outputs, and optimisation quantities (and determine what sort of performance data is needed, i.e. parameter inputs to the cost function)