VFH+ based shared control for remotely operated mobile robots

This paper addresses the problem of safe and efficient navigation in remotely controlled robots operating in hazardous and unstructured environments; or conducting other remote robotic tasks. A shared control method is presented which blends the commands from a VFH+ obstacle avoidance navigation module with the teleoperation commands provided by an operator via a joypad. The presented approach offers several advantages such as flexibility allowing for a straightforward adaptation of the controller's behaviour and easy integration with variable autonomy systems; as well as the ability to cope with dynamic environments. The advantages of the presented controller are demonstrated by an experimental evaluation in a disaster response scenario. More specifically, presented evidence show a clear performance increase in terms of safety and task completion time compared to a pure teleoperation approach, as well as an ability to cope with previously unobserved obstacles.Comment: 8 pages,6 figure

Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction

Autonomy is a critical construct related to human-robot interaction (HRI) and varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots interact with one another. Thus, there is a need to understand HRI by identifying variables that influence—and are influenced by—robot autonomy. Our overarching goal is to develop a framework for LORA in HRI. To reach this goal, our framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, this framework proposes a process for determining a robot’s autonomy level by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as a guideline for determining autonomy, categorizing the LORA along a qualitative taxonomy and considering HRI variables (e.g., acceptance, situation awareness, reliability) that may be influenced by the LOR

Trust-Based Control of (Semi)Autonomous Mobile Robotic Systems

Despite great achievements made in (semi)autonomous robotic systems, human participa-tion is still an essential part, especially for decision-making about the autonomy allocation of robots in complex and uncertain environments. However, human decisions may not be optimal due to limited cognitive capacities and subjective human factors. In human-robot interaction (HRI), trust is a major factor that determines humans use of autonomy. Over/under trust may lead to dispro-portionate autonomy allocation, resulting in decreased task performance and/or increased human workload. In this work, we develop automated decision-making aids utilizing computational trust models to help human operators achieve a more effective and unbiased allocation. Our proposed decision aids resemble the way that humans make an autonomy allocation decision, however, are unbiased and aim to reduce human workload, improve the overall performance, and result in higher acceptance by a human. We consider two types of autonomy control schemes for (semi)autonomous mobile robotic systems. The first type is a two-level control scheme which includes switches between either manual or autonomous control modes. For this type, we propose automated decision aids via a computational trust and self-confidence model. We provide analytical tools to investigate the steady-state effects of the proposed autonomy allocation scheme on robot performance and human workload. We also develop an autonomous decision pattern correction algorithm using a nonlinear model predictive control to help the human gradually adapt to a better allocation pattern. The second type is a mixed-initiative bilateral teleoperation control scheme which requires mixing of autonomous and manual control. For this type, we utilize computational two-way trust models. Here, mixed-initiative is enabled by scaling the manual and autonomous control inputs with a function of computational human-to-robot trust. The haptic force feedback cue sent by the robot is dynamically scaled with a function of computational robot-to-human trust to reduce humans physical workload. Using the proposed control schemes, our human-in-the-loop tests show that the trust-based automated decision aids generally improve the overall robot performance and reduce the operator workload compared to a manual allocation scheme. The proposed decision aids are also generally preferred and trusted by the participants. Finally, the trust-based control schemes are extended to the single-operator-multi-robot applications. A theoretical control framework is developed for these applications and the stability and convergence issues under the switching scheme between different robots are addressed via passivity based measures

Efficiency based reactive shared control for collaborative human-robot navigation

Autonomous robots are capable of navigating on their own. In some cases, though, it is interesting to allow humans to influence navigation. Shared control is typically achieved either by giving control to the human or the robot at some specific situations. In this work, we propose a method to share control between humans and robots at each point of a given trajectory, so that both have weight in the resulting behavior of the mobile. This is achieved by estimating their respective local efficiencies at each time instant and combining their commands into a single order. In order to achieve a seamless combination, this procedure is integrated into a bottom-up architecture via a reactive layer. The system is meant to be used in wheelchair control for people with disabilities. Thus far, we have tested the proposed method using a Pioneer AT robot driven by several volunteers. Results have been satisfactory both from a quantitative and qualitative point of view. ©2007 IEEE

Flexible robotic control via co-operation between an operator and an ai-based control system

This thesis addresses the problem of variable autonomy in teleoperated mobile robots. Variable autonomy refers to the approach of incorporating several different levels of autonomous capabilities (Level(s) of Autonomy (LOA)) ranging from pure teleoperation (human has complete control of the robot) to full autonomy (robot has control of every capability), within a single robot. Most robots used for demanding and safety critical tasks (e.g. search and rescue, hazardous environments inspection), are currently teleoperated in simple ways, but could soon start to benefit from variable autonomy. The use of variable autonomy would allow Artificial Intelligence (AI) control algorithms to autonomously take control of certain functions when the human operator is suffering a high workload, high cognitive load, anxiety, or other distractions and stresses. In contrast, some circumstances may still necessitate direct human control of the robot. More specifically, this thesis is focused on investigating the issues of dynamically changing LOA (i.e. during task execution) using either Human-Initiative (HI) orMixed-Initiative (MI) control. MI refers to the peer-to-peer relationship between the robot and the operator in terms of the authority to initiate actions and LOA switches. HI refers to the human operators switching LOA based on their judgment, with the robot having no capacity to initiate LOA switches. A HI and a novel expert-guided MI controller are presented in this thesis. These controllers were evaluated using a multidisciplinary systematic experimental framework, that combines quantifiable and repeatable performance degradation factors for both the robot and the operator. The thesis presents statistically validated evidence that variable autonomy, in the form of HI and MI, provides advantages compared to only using teleoperation or only using autonomy, in various scenarios. Lastly, analyses of the interactions between the operators and the variable autonomy systems are reported. These analyses highlight the importance of personality traits and preferences, trust in the system, and the understanding of the system by the human operator, in the context of HRI with the proposed controllers

Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation

Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten. Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen