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

Experimental Evaluation of the Projection-based Force Reflection Algorithms for Haptic Interaction with Virtual Environment

Haptic interaction with virtual environments is currently a major and growing area of research with a number of emerging applications, particularly in the field of robotics. Digital implementation of the virtual environments, however, introduces errors which may result in instability of the haptic displays. This thesis deals with experimental investigation of the Projection-Based Force Reflection Algorithms (PFRAs) for haptic interaction with virtual environments, focusing on their performance in terms of stability and transparency. Experiments were performed to compare the PFRA in terms of performance for both non-delayed and delayed haptic interactions with more conventional haptic rendering methods, such as the Virtual Coupling (VC) and Wave Variables (WV). The results demonstrated that the PFRA is more stable, guarantees higher levels of transparency, and is less sensitive to decrease in update rates

Posture-Dependent Projection-Based Force Reflection Algorithms for Bilateral Teleoperators

It was previously established that the projection-based force reflection (PBFR) algorithms improve the overall stability of a force reflecting teleoperation system. The idea behind the PBFR algorithms is to identify the component of the reflected force which is compensated by interaction with the operator\u27s hand, and subsequently attenuate the residual component of the reflected force. If there is no a priori information regarding the behaviour of the human operator, the PBFR gain is selected equal to sufficiently small constant in order to guarantee stability for a wide range of human operator responses. Small PBRF gains, however, may deteriorate the transparency of a teleoperator system. In this thesis, a new method for selecting the PBFR gain is introduced which depends on human postures. Using an online human posture estimation, the introduced posture-dependent PBFR algorithm has been applied to a teleoperation system with force feedback. It is experimentally demonstrated that the developed method for selection of the PBFR gain based on human postures improves the transparency of the teleoperator system while the stability is preserved. Finally, preliminary results that deal with an extension of the developed methods towards a more realistic model of the human arm with 4 degrees of freedom and three dimensional movements are presented

Quality-Latency Trade-Off in Bilateral Teleoperation

The purpose of this thesis is to investigate how the latency in mobile networks affect the quality of highly demanding and sensitive applications running on it. Furthermore, this thesis will provide some information to what is going on in the field of Cloud Computing and the Internet of Things. It will hopefully spark a discussion about what possibilities will come with the development of the Cloud and Internet of Things. The application chosen was a bilateral teleoperation, with force feedback, controlled in 6 dimensions. To investigate how the quality depends on network latency, different network models were simulated as the communication channel. The networks chosen to be simulated were a 3G, 4G, and a 5G cellular network along with a wired network chosen as a baseline. On this setup two main experiments were done. The first one was a collision test and the second one a dexterity test, where a user was supposed to pick up a small wooden brick and put it into a box. The results from the experiments showed that there was indeed a difference in behavior when having a network delay larger than 20 ms

Control of Cooperative Haptics-Enabled Teleoperation Systems with Application to Minimally Invasive Surgery

Robot-Assisted Minimally Invasive Surgical (RAMIS) systems frequently have a structure of cooperative teleoperator systems where multiple master-slave pairs are used to collaboratively execute a task. Although multiple studies indicate that haptic feedback improves the realism of tool-tissue interaction to the surgeon and leads to better performance for surgical procedures, current telesurgical systems typically do not provide force feedback, mainly because of the inherent stability issues. The research presented in this thesis is directed towards the development of control algorithms for force reflecting cooperative surgical teleoperator systems with improved stability and transparency characteristics. In the case of cooperative force reflecting teleoperation over networks, conventional passivity based approaches may have limited applicability due to potentially non-passive slave-slave interactions and irregular communication delays imposed by the network. In this thesis, an alternative small gain framework for the design of cooperative network-based force reflecting teleoperator systems is developed. Using the small gain framework, control algorithms for cooperative force-reflecting teleoperator systems are designed that guarantee stability in the presence of multiple network-induced communication constraints. Furthermore, the design conservatism typically associated with the small-gain approach is eliminated by using the Projection-Based Force Reflection (PBFR) algorithms. Stability results are established for networked cooperative teleoperator systems under different types of force reflection algorithms in the presence of irregular communication delays. The proposed control approach is consequently implemented on a dual-arm (two masters/two slaves) robotic MIS testbed. The testbed consists of two Haptic Wand devices as masters and two PA10-7C robots as the slave manipulators equipped with da Vinci laparoscopic surgical instruments. The performance of the proposed control approach is evaluated in three different cooperative surgical tasks, which are knot tightening, pegboard transfer, and object manipulation. The experimental results obtained indicate that the PBFR algorithms demonstrate statistically significant performance improvement in comparison with the conventional direct force reflection algorithms. One possible shortcoming of using PBFR algorithms is that implementation of these algorithms may lead to attenuation of the high-frequency component of the contact force which is important, in particular, for haptic perception of stiff surfaces. In this thesis, a solution to this problem is proposed which is based on the idea of separating the different frequency bands in the force reflection signal and consequently applying the projection-based principle to the low-frequency component, while reflecting the high-frequency component directly. The experimental results demonstrate that substantial improvement in transient fidelity of the force feedback is achieved using the proposed method without negative effects on the stability of the system

A Stable and Transparent Framework for Adaptive Shared Control of Robots

In mixed-initiative haptic shared control of robots, humans and automatic control system work in parallel. The commands to the robot are a weighted sum of forces from these two agents. This thesis develops control methods to improve the force feedback performance for mixed-initiative shared teleoperation and to adapt the control authority between human and automatic control system in a stable manner even in the presence of communication delays. All methods are validated on real robotic hardware