Micro Air Vehicles (MAVs) will unlock their true potential once they can operate in groups. To this end, it is essential for them to estimate on-board the relative location of their neighbors. The challenge lies in limiting the mass and processing burden needed to enable this. We developed a relative localization method that only requires the MAVs to communicate via their wireless transceiver. Communication allows the exchange of on-board states (velocity, height, and orientation), while the signal-strength provides range data. These quantities are fused to provide a full relative location estimate. We used our method to tackle the problem of collision avoidance in tight areas. The system was tested with a team of AR.Drones flying in a 4mx4m area and with miniature drones of 50g in a 2mx2m area. The MAVs were able to track their relative positions and fly several minutes without collisions. Our implementation used Bluetooth to communicate between the drones. This featured significant noise and disturbances in signal-strength, which worsened as more drones were added. Simulation analysis suggests that results can improve with a more suitable transceiver module.
Autopilots for small UAVs are generally equipped with low fidelity sen- sors that make state estimation challenging. In addition, the sensor suite does not include units that measure angle-of-attack and side-slip angles. The achievable flight performance is directly related to the quality of the state estimates. Unfortunately, the computational resources on-board a small UAV are generally limited and pre- clude large state Kalman filters that estimate all of the states and sensor biases. In this chapter we describe simple models for the sensors typically found on-board small UAVs. We also describes a simple cascaded approach to state estimation that has been extensively flight tested using the Kestrel autopilot produced by Procerus Technologies. Our intention is to provide a tutorial of continuous-discrete Kalman filtering with application to state estimation for small UAVs. High fidelity estimates of the position, velocity, attitude, and angular rates are critical for successful guidance and control of intelligent UAVs. The achiev- able fidelity of the state estimates depends upon the quality of the sensors on-board the UAV. Unfortunately, high quality sensors are usually heavy and expensive. This is particularly true for sensors that directly measure the atti- tude of the UAV. In this chapter we focus on the problem of state estimation using light weight, inexpensive, low quality sensors. In doing so, our target platforms are small and micro air vehicles with limited payload capacity. In recent years, several autopilots for small UAVs have appeared on the commercial market. These include the Procerus Kestrel (4), the Cloudcap Piccolo (2), and the Micropilot MP2028 (3). Each of these autopilots use the following sensors: • rate gyros, • accelerometers, • pressure sensors, and • GPS.