Mixed-initiative Variable Autonomy for Remotely Operated Mobile Robots

This article presents an Expert-guided Mixed-initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster-response-inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: (a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; (b) in the context of the simulated robot experiment, Mixed-initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Last, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: (a) the design of context-aware MI control systems; and (b) the conflict for control between the robot’s MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed.

A Real-Time Distance Measurement System for a Digital Twin Using Mixed Reality Goggles

This paper presents a new system architecture for controlling industrial devices using Mixed Reality (MR) applications and a new method based upon them for measuring the distance between real and virtual points. The research has been carried out using a physical robot and its Digital Twin (DT). The possibility of controlling them using gestures recognized by Mixed Reality goggles has been presented. The extension of the robot’s environment with a 3D model capable of following its movements and positions was also analyzed. The system was supervised by an industrial Programmable Logic Controller (PLC) serving as an end point for the data sent by the goggles and controlling the movements of the real robot by activating the corresponding control. The results of the preliminary measurements presented here concerned the responsiveness of the system and showing the influence of system parameters in the accuracy of distance estimation between measured points.

LEGO MINDSTORMS EV3 Robot in TRICK Studio environment as a new performer for teaching algorithmization and the basics of robotics in a school informatics course

The article discusses the methodological aspects of teaching algorithmization and the basics of robotics using the educational constructor LEGO MINDSTORMS EV3 in the TRIK Studio environment. The advantages of using the TRIK Studio programming environment to control both a real robot "live" and a virtual one in simulation mode are shown. Based on the methodology for introducing basic algorithmic constructions and types of algorithms by I.G. Semakin, examples of solving a system of problems using commands to control the EV3 robot in the TRIK Studio environment are given. The LEGO MINDSTORMS EV3 Robot is seen as a new performer in the TRIK Studio environment. The article highlights directions and provides examples of tasks for further mastering the basics of robotics and algorithmization. The parallel study of the basics of robotics with the study of the algorithmization section based on the task approach allows you to organize the learning process in the informatics course based on interdisciplinary and STEM approaches, to implement the requirements of the Approximate Basic Educational Program of Basic General Education (2015).

Development of a Mobile Robot That Plays Tag with Touch-and-Away Behavior Using a Laser Range Finder

The development of robots that play with humans is a challenging topic for robotics. We are developing a robot that plays tag with human players. To realize such a robot, it needs to observe the players and obstacles around it, chase a target player, and touch the player without collision. To achieve this task, we propose two methods. The first one is the player tracking method, by which the robot moves towards a virtual circle surrounding the target player. We used a laser range finder (LRF) as a sensor for player tracking. The second one is a motion control method after approaching the player. Here, the robot moves away from the player by moving towards the opposite side to the player. We conducted a simulation experiment and an experiment using a real robot. Both experiments proved that with the proposed tracking method, the robot properly chased the player and moved away from the player without collision. The contribution of this paper is the development of a robot control method to approach a human and then move away safely.

O2A: One-Shot Observational Learning with Action Vectors

We present O2A, a novel method for learning to perform robotic manipulation tasks from a single (one-shot) third-person demonstration video. To our knowledge, it is the first time this has been done for a single demonstration. The key novelty lies in pre-training a feature extractor for creating a perceptual representation for actions that we call “action vectors”. The action vectors are extracted using a 3D-CNN model pre-trained as an action classifier on a generic action dataset. The distance between the action vectors from the observed third-person demonstration and trial robot executions is used as a reward for reinforcement learning of the demonstrated task. We report on experiments in simulation and on a real robot, with changes in viewpoint of observation, properties of the objects involved, scene background and morphology of the manipulator between the demonstration and the learning domains. O2A outperforms baseline approaches under different domain shifts and has comparable performance with an Oracle (that uses an ideal reward function). Videos of the results, including demonstrations, can be found in our: project-website.

Secure and secret cooperation in robot swarms

The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method, operators can provide the “blueprint” of the swarm’s mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate toward mission completion, have to “prove” their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission’s high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role.

Following A Dynamic Object Through A Transient Response Adjustable MPC (Extended Version)

We developed an MPC motion controller for a mobile robot to follow a dynamic object. The main contribution is to find an adjustable transient response characteristic in a special formulation of MPC and apply to a real robot platform.

Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms

Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.