In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle's private zone, i.e., the pre-allocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups-a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.

In autonomous robot exploration, the frontier is the border in the world map dividing the explored and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We consider a modular control system pipeline for autonomous exploration where a 2-D graph simultaneous localization and mapping (SLAM) algorithm based on occupancy grid submaps performs map building and localization, and frontier detection is one of the key system components. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a fast specialized frontier detection method which is efficiently constrained to active submaps, yet robust to graph SLAM loop closures.

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.