Challenges in Sensing, Communication, Coordination

Group 1

What are the main challenges?

• moving decisions on board
• implicit communication/ without medium
• local communication
• how to control / monitor the distributed process "there is a human in the loop"
• contraints are satisfied. have to be communicated / monitoring has to be achieved
• what information is transmitted to whom. - reasoning
• interpret and how to act on information

• information for mission and for environment reliability versus success

What are the best suitable sensors, communication modules for small scale UAVs? What is available?

Hardware:

• do we need to change hardware?
• apriori knowledge via internet and sensing tool
• learning capability on sensing, depending of weather or environment.
• mesh architecture multi-hop. no wifi. issues with bandwith and reliabily.
• outdoor: hardware for "real time gps", 3G networks for monitoring
• Iridium protocol via email and sms.
• multiple hardware for communication on one device to opportunitically use them.
• multi communication hardware module
• deviding which sensed data for which communication - do self finding on these

• 4 G with the ground station and wifi.
• multiple antenna approach.

UAV swarms are feasible?

• yes feasible,

formation:

• use given shape,
• limitation of self-organization? not only local information.
• technically feasible, no application right now.

• no application yet. creating communication network.

How do we cooordinate the UAV to achieve team behaviour?

• • modest planing, some degree of hierarchy.
  • hierarchy of frequency of processors. corridor of freedom. network with distributed hierarichal freedom
  • change structure for different tasks.

technical limitations:

• • find specitic application areas.
  • we do not know the requirements for self organization
  • enable restricted form of self-organization is to use formation

conclusion

• need for a team
• cooperation from sensing and communication
• interaction between sensing is missing
• architecture is missing to break down the complexity

tight interaction of levels of limited freedom. Network needs ability of learning.

Group 3

Angela Schöllig, Enrico Natalizio, Ashutosh Natraj, Michael Hofbaur, Sascha Einspieler, Daniel Neuhold, Pasquale Grippa, Georg Heppner, Wilfried Elmenreich

What are the main challenges?

• limited payload
• processing power
• data fusion
• (self-)localization
• coordination, synchronization of sensing
• communication (range, loss)
• collision avoidance
  • should work in worst case scenario (multiple UAVs, obstacles, communication outage)
• robustness, handling of every possible situation
• must be fail-operational to maintain safe state (situation-dependent)
• optimizing the number of flying robots
• find simple rules to create desired emergent behavior

What are the best suitable sensors, communication modules for small-scale UAVs? What is available?

• sensors (should be light, low energy-consuming, low signal processing effort)
  • cameras
  • ultrasonic (for indoor, needs to be de-synchronized
  • infrared
  • contactsensors
  • LIDAR
  • IMU
  • GPS
  • wind sensor
  • barometric height sensor
  • sensing earth magnetic field
  • new integrated sensor modules (IMU, camera, processing unit)

• communication modules for real-time interaction
  • radio, e.g., X-Bees
  • IR
  • remote control module (for legal reasons)
  • +ability for multi-hop/mesh networks
  • antenna design, transmission direction

• command interface for autonmous swarms
  • Internet connection
  • UMTS

Is a UAV swarm feasible? What are the limitations?

depends on scenario, loosly coordinated UAVs flying over a field is possible, cooperatively lifting payload is difficult

• cost
• coordination
• precision in localization
  • error progagation

How do we coordinate the UAVs to achieve team behavior?

• central coordination
  • simple UAVs
  • communication
  • complexity, scalability

• decentralized coordination
  • achieving optimal behavior
  • testing the system
  • designing the self-organizing algorithm
  • controllability

• mixture of both
  • high-level goal done by centralized planning
  • local control for collision avoidance, etc.