In this paper, we study a cooperative aerial-ground robotic team and its application to the task of automated construction. We propose a solution for planning and coordinating the mission of constructing a wall with a predefined structure for a heterogeneous system consisting of one mobile robot and up to three unmanned aerial vehicles. The wall consists of bricks of various weights and sizes, some of which need to be transported using multiple robots simultaneously. To that end, we use hierarchical task representation to specify interrelationships between mission subtasks and employ effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We evaluate the performance of the method under different optimization criteria and validate the solution in the realistic Gazebo simulation environment.

No abstract

This paper investigates the use of LiDAR SLAM as a pose feedback for autonomous flight. Cartographer, LOAM and hdl graph SLAM are tested for this role. They are first compared offline on a series of datasets to see if they are capable of producing high-quality pose estimations in agile and long-range flight scenarios. The second stage of testing consists of integrating the SLAM algorithms into a cascade PID UAV control system and comparing the control system performance on step excitation signals and helical trajectories. The comparison is based on step response characteristics and several time integral performance criteria as well as the RMS error between planned and executed trajectory.

Notwithstanding intensive research and many scientific advances, diagnosing autism spectrum disorders remains a slow and tedious process. Due to the absence of any physiological tests, the outcome depends solely on the expertise of the clinician, which takes years to acquire. Complicating the matter further, research has shown that inter-rater reliability can be very low, even among experienced clinicians. As an attempt to facilitate the diagnostic process and make it more objective, this paper proposes a robot-assisted diagnostic protocol. The expected benefit of using a robot is twofold: the robot always performs its actions in a predictable and consistent way, and it can use its sensors to catch aspects of a child's behavior that a human examiner can miss. In this paper, we describe four tasks from the widely accepted ADOS protocol, that have been adapted to make them suitable for the Aldebaran Nao humanoid robot. These tasks include evaluating the child's response to being called by name, symbolic and functional imitation, joint attention and assessing the child's ability to simultaneously communicate on multiple channels. All four tasks have been implemented on the robot's onboard computer and are performed autonomously. As the main contribution of the paper, we present the results of the initial batch of four clinical trials of the proposed robot assisted diagnostic protocol, performed on a population of preschool children. The results of the robot's observations are benchmarked against the findings of experienced clinicians. Emphasis is placed on evaluating robot performance, in order to assess the feasibility of a robot eventually becoming an assistant in the diagnostic process. The obtained results indicate that the use of robots as autism diagnostic assistants is a promising approach, but much work remains to be done before they become useful diagnostic tools.

No abstract