In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of new obstacles. After the changes, the team is divided into different groups of robots. The groups have a limited communication range and only a partial information in their field of view about the current scenario. Their objective is to form a chain from a static base station to a goal location. In the proposed distributed replanning approach, the robots predict new plans for the other groups from the new observed information by each robot in the changed scenario, to restore the connectivity with a base station and reach the initial joint objective. If a solution exists, the method achieves the reconnection of all the groups in a unique chain. The proposed method is compared with other two cases: 1) when all the agents have full information of the environment, and 2) when some robots must move to reach other waiting robots for reconnection. Numerical simulations are provided to evaluate the proposed approach in the presence of unpredictable scenario changes.

Recent successes in applying reinforcement learning (RL) for robotics has shown it is a viable approach for constructing robotic controllers. However, RL controllers can produce many collisions in environments where new obstacles appear during execution. This poses a problem in safety-critical settings. We present a hybrid approach, called iKinQP-RL, that uses an Inverse Kinematics Quadratic Programming (iKinQP) controller to correct actions proposed by an RL policy at runtime. This ensures safe execution in the presence of new obstacles not present during training. Preliminary experiments illustrate our iKinQP-RL framework completely eliminates collisions with new obstacles while maintaining a high task success rate.

In this paper, we propose a path re-planning algorithm that makes robots able to work in scenarios with moving obstacles. The algorithm switches between a set of pre-computed paths to avoid collisions with moving obstacles. It also improves the current path in an anytime fashion. The use of informed sampling enhances the search speed. Numerical results show the effectiveness of the strategy in different simulation scenarios.

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.

San Francisco (CNN)College career centers used to prepare students for job interviews by helping them learn how to dress appropriately or write a standout cover letter. These days, they're also trying to brace students for a stark new reality: They may be vetted for jobs in part by artificial intelligence. What worries me about AI is AI can't tell the heart of a person and the drive a person has. "Once I understood the AI interview process, I definitely started thinking about it as a game and how I could optimize for certain qualities or gestures."

San Francisco (CNN)College career centers used to prepare students for job interviews by helping them learn how to dress appropriately or write a standout cover letter. These days, they're also trying to brace students for a stark new reality: They may be vetted for jobs in part by artificial intelligence. At schools such as Duke University, Purdue University, and the University of North Carolina at Charlotte, career counselors are now working to find out which companies use AI and also speaking candidly with students about what, if anything, they can do to win over the algorithms. This shift in preparations comes as more businesses interested in filling internships and entry-level positions that may see a glut of applicants turn to outside companies such as HireVue to help them quickly conduct vast numbers of video interviews. With HireVue, businesses can pose pre-determined questions -- often recorded by a hiring manager -- that candidates answer on camera through a laptop or smartphone.