In recent years autonomous robots, and Unmanned Aerial Vehicles (UAVs) in particular, are becoming always more important in the context of emergency scenarios, being able to anticipate the actions of human operators and to support them during rescue operations. In this context, the investigation of strategies for the autonomous control of UAVs, for the development of Human-Swarm Interfaces and for the coverage of large areas is crucial. All these aspects have been analyzed within the Italian project PRISMA, and they will be here summarized.
The work described in this article has been performed during the PRISMA project, which focusses on the development and deployment of robots and autonomous systems able to operate in emergency scenarios, with a specific reference to monitoring, pre-operative management, and real-time intervention. The work has been focussed on Unmanned Aerial Vehicles (UAVs), being able of monitoring a wide area in a small time, quickly moving and being easily controlled by human operators. In particular, some aspects have been analyzed more in details: techniques for localization and autonomous control, coverage algorithms, strategies for moving in a structured formation and the integration of virtual reality tools for visualization and control.
An indoor experimental setup can be extremely useful when dealing with aerial
robots, in order to speed up the development of model and algorithms. In this
context, the main problem is related to the localization of the robots, since GPS
signal is denied. In the project the problem was solved using a camera
(MatrixVision, mvBlueFox) on board of the esarotor Asctec Firefly [
For improving the accuracy of the localization, a wall of 35 markers has been created (Fig. 1), and a custom algorithm has been developed, based on the elimination of the outliers and the estimation of the average value. The reference in position is then used for the actual control of the robot. Indeed, the UAV calculates the error between the target position (a fixed value when hovering, a series of waypoints in more complex cases) and uses the error in space as input of three PID controllers for the three directions in space. The resulting target accelerations are used to calculate the reference thrust u and the control angles d (pitch) and d (roll), considering the dynamics of the system, the mass m of the UAV and the angle d (yaw): u = mq 2x + 2y + ( z + g)2 d = sin 1
m d = tan 1 x sin d
y cos d u x cos d
y sin d z + g
The control of the orientation of the multirotor ( d) has been achieved with a
proportional controller, directly based on the error between the reference angle
and the actual one. The whole control software, composed of the localization
system, the PID controllers and other modules dedicated to the planning of
the actions (i.e., taking-off, hovering, reaching a waypoint, taking a picture,
landing) and to the interfacing with the user, has been implemented on board of
the esarotor Asctec Firefly, within the ETHNOS framework [
With a similar control approach, but using mainly GPS as localization system,
a monitoring strategy has been implemented and tested outdoor, using two
multirotors (Asctec Pelican and Asctec Firefly). The main idea was to analyze and
compare the performances of some real-time multi-robot coverage algorithms
(i.e., Node Count, Edge Counting, Learning Real-Time A* and PatrolGRAPH* )
[
The four algorithms have been firstly tested in simulation to the end of comparing their performances, considering as indicators the length of the longest path among all the robots and the overall distance travelled by all robots. The results confirmed many of the previous works in literature, suggesting in particular that the Node Count algorithm, despite its simplicity, is the most efficient one. This becomes more evident increasing the number of robots and the size of the grids, mainly because of the update rule of other high-performance algorithms (e.g. LRTA*).
Finally, the algorithms have been practically implemented using the two multirotors, in order to test the whole framework (Fig. 2). A ROS/ETHNOS interface has been developed to implement the communication between the off-board controller (executing the algorithms) and the two robots.
Even if many steps forward have been taken towards the fully autonomous control of UAVs, a human pilot is usually in charge of controlling the robots. However, teleoperating UAVs can become a hard task whenever it is necessary to deploy a swarm of robots instead of a single unit, to the end of increasing the area under observation. In this case, the organization of robots in a structured formation may reduce the effort of the operator.
For all these reasons, a custom Human-Swarm Interface has been built,
allowing human operators to control a team of multirotors in environments filled
with obstacles. The algorithm is mainly based on the work of Balch and Arkin
4
[
Given the necessity of easing the control of the robot from the operator point
of view, the integration of virtual reality tools has also been investigated. In
particular, the Oculus Rift [
More in details, the inertial sensors embedded in the head-mounted display are used to periodically measure the yaw orientation of the user’s head, using it as reference for the yaw control of the multirotor. The ROS/ETHNOS bridge has again been used for implementing the bidirectional communication (angles and video streaming).