This work focuses on the challenges associated with autonomous robot guidance, navigation, and control in multi-robot systems. This study provides an affordable solution by utilizing a group of small unmanned ground vehicles and quadrotors that collaborate on monitoring and inspection missions. The proposed system utilizes a potential fields path planning algorithm to allow a robot to track a moving target while avoiding obstacles in a dynamic environment. To achieve the required performance and provide robust tracking against wind disturbances, a backstepping controller is used to solve the essential stability problem and ensure that each robot follows the specified path asymptotically. Furthermore, the performance is also compared with a proportional-integral-derivative (PID) controller to ensure the superiority of the control system. The system combines a low-cost inertial measurement unit (IMU), a GNSS receiver, and a barometer for UAVs to generate a navigation solution (position, velocity, and attitude estimations), which is then used in the guidance and control algorithms. A similar solution is used for UGVs by integrating the IMU, a GNSS receiver, and encoders. Non-linear complementary filters integrate the measurements in the navigation system to produce high bandwidth estimates of the state of each robotic platform. Experimental results of several scenarios are discussed to prove the effectiveness of the approach.

This paper describes the results obtained in the implementation of a solution implementing real-time precise and accurate attitude and position estimation algorithms designed for control applications based on an Android smartphone. The topic of the paper is in particular the application of this solution to dynamics sensing in a quadrotor Unmanned Aerial Vehicle (UAV). The attitude estimation is based on an Explicit Complementary Filter (ECF). The position estimation is implemented through a complementary filter using acceleration readings provided by the smartphone sensors and the position derived by a vision based motion capture system. The parameters of the algorithms are tuned through the minimisation of the estimation error for sets of sample acquisitions. Moreover, these settings are chosen in order to minimise the issues related with the fact that Android is not a Real Time Operating System (RTOS). Finally, it was verified that the chosen solution provides performances adequate to the quadrotor stabilisation. The results of a set of experimental tests are used to show the performances of the proposed solution. For the attitude estimation algorithm, a comparison is moreover provided in order to highlight the difference with standard Android orientation estimation algorithm.

New Control Approaches for Trajectory Tracking and Motion Planning of Unmanned Tracked Robot