Goldie Nejat

Robotic Urban Search and Rescue: A Survey from the Control Perspective

Robotic urban search and rescue (USAR) is a challenging yet promising research area which has significant application potentials as has been seen during the rescue and recovery operations of recent disaster events. To date, the majority of rescue robots used in the field are teleoperated. In order to minimize a robot operator’s workload in time-critical disaster scenes, recent efforts have been made to equip these robots with some level of autonomy. This paper provides a detailed overview of developments in the exciting and challenging area of robotic control for USAR environments. In particular, we discuss the efforts that have been made in the literature towards: 1) developing low-level controllers for rescue robot autonomy in traversing uneven terrain and stairs, and perception-based simultaneous localization and mapping (SLAM) algorithms for developing 3D maps of USAR scenes, 2) task sharing of multiple tasks between operator and robot via semi-autonomous control, and 3) high-level control schemes that have been designed for multi-robot rescue teams.

The search for survivors: Cooperative human-robot interaction in search and rescue environments using semi-autonomous robots

Current applications of mobile robots in urban search and rescue (USAR) environments require a human operator in the loop to help guide the robot remotely. Although human operation can be effective, the unknown cluttered nature of the environments make robot navigation and victim identification highly challenging. Operators can become stressed and fatigued very quickly due to a loss of situational awareness, leading to the robots getting stuck and not being able to find victims in the scene during this time-sensitive operation. In addition, current autonomous robots are not capable of traversing these complex unpredictable environments. To address this challenge, a balance between the level of autonomy of the robot and the amount of human control over the robot needs to be addressed. In this paper, we present a unique control architecture for semi-autonomous navigation of a robotic platform utilizing sensory information provided by a novel real-time 3D mapping sensor. The control system provides the robot with the ability to learn and make decisions regarding which rescue tasks should be carried out at a given time and whether an autonomous robot or a human controlled robot can perform these tasks more efficiently without compromising the safety of the victims, rescue workers and the rescue robot. Preliminary experiments were conducted to evaluate the performance of the proposed collaborative control approach for a USAR robot in an unknown cluttered environment.

Acceptance and Attitudes Toward a Human-like Socially Assistive Robot by Older Adults

Recent studies have shown that cognitive and social interventions are crucial to the overall health of older adults including their psychological, cognitive, and physical well-being. However, due to the rapidly growing elderly population of the world, the resources and people to provide these interventions is lacking. Our work focuses on the use of social robotic technologies to provide person-centered cognitive interventions. In this article, we investigate the acceptance and attitudes of older adults toward the human-like expressive socially assistive robot Brian 2.1 in order to determine if the robot’s human-like assistive and social characteristics would promote the use of the robot as a cognitive and social interaction tool to aid with activities of daily living. The results of a robot acceptance questionnaire administered during a robot demonstration session with a group of 46 elderly adults showed that the majority of the individuals had positive attitudes toward the socially assistive robot and its intended applications.

Brian 2.1: A socially assistive robot for the elderly and cognitively impaired

As the worlds elderly population continues to grow, so does the number of individuals diagnosed with cognitive impairments. It is estimated that 115 million people will have age-related memory loss by 2050 [1]. The number of older adults who have difficulties performing self-care and independent-living activities increases significantly with the prevalence of cognitive impairment. This is especially true for the population over 70 years of age [2]. Cognitive impairment, as a result of dementia, severely affects a persons ability to independently initiate and perform daily activities, as cognitive abilities can be diminished [3]. If a person is incapable of performing these activities, continuous assistance from others is necessary. In 2010, the total worldwide cost of dementia (including medical, social, and informal care costs) was estimated to be US

Meal-time with a socially assistive robot and older adults at a long-term care facility

604 billion [1].

Can I be of assistance? The intelligence behind an assistive robot

As people get older, their ability to perform basic self-maintenance activities can be diminished due to the prevalence of cognitive and physical impairments or as a result of social isolation. The objective of our work is to design socially assistive robots capable of providing cognitive assistance, targeted engagement, and motivation to elderly individuals, in order to promote participation in self-maintenance activities of daily living. In this paper, we present the design and implementation of the expressive human-like robot, Brian 2.1, as a social motivator for the important activity of eating meals. An exploratory study was conducted at an elderly care facility with the robot and eight individuals, aged 82--93, to investigate user engagement and compliance during meal-time interactions with the robot along with overall acceptance and attitudes towards the robot. Results of the study show that the individuals were both engaged in the interactions and complied with the robot during two different meal-eating scenarios. A post-study robot acceptance questionnaire also determined that, in general, the participants enjoyed interacting with Brian 2.1 and had positive attitudes towards the robot for the intended activity.

A Learning-Based Semi-Autonomous Controller for Robotic Exploration of Unknown Disaster Scenes While Searching for Victims

The social interaction, guidance and support that a socially assistive robot can provide a person can be very beneficial to patient-centered care. However, there are a number of conundrums that must be addressed in designing such a robot. This work addresses two main limitations in the development of intelligent task-driven socially assistive robots: (i) recognition and identification of human gesticulation as a source of determining the affective state of a person, and (ii) robotic control architecture design and implementation with explicit social and assistive task functionalities. In this paper, the development of a unique task-driven robotic system capable of quantitatively interpreting human body language and in turn, effectively responding via task-driven behavior during assistive social interaction is presented. In particular, a novel gesture identification and classification technique is proposed capable of interpreting human gestures as semantically meaningful commands for inputs into a multi-layer decision making control architecture. The learning-based control architecture is then utilized to determine the effective and appropriate assistive behavior of the robot.

Finding Disaster Victims: A Sensory System for Robot-Assisted 3D Mapping of Urban Search and Rescue Environments

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing a human operator to cooperate and share such tasks with a rescue robot as navigation, exploration, and victim identification. In this paper, we present a unique hierarchical reinforcement learning-based semi-autonomous control architecture for rescue robots operating in cluttered and unknown urban search and rescue (USAR) environments. The aim of the controller is to enable a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A direction-based exploration technique is integrated in the controller to expand the search area of the robot via the classification of regions and the rubble piles within these regions. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed HRL-based semi-autonomous controller to unknown cluttered scenes with different sizes and varying types of configurations.

Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

In this paper the first application of utilizing a unique 3D real-time mapping sensor for sequential 3D map building within a visual simultaneous localization and mapping (SLAM) framework in unknown cluttered urban search and rescue (USAR) environments is proposed. The sensor utilizes a digital fringe projection and phase shifting technique to provide real-time 2D and 3D sensory information of the environment. The proposed sensor is unique over current technologies, in that it can directly map rubble in 3D and in real-time at a frame rate of up to 60 fps. Furthermore, we propose the development of a novel 3D visual SLAM method utilizing both 2D and 3D images taken by the sensor for robust and reliable landmark identification, mapping and localization algorithms utilizing a scale invariant feature transform (SIFT)-based approach. Preliminary experiments show the potential of the proposed 3D real-time sensory system for such unknown cluttered USAR environments.

The Design of an Expressive Humanlike Socially Assistive Robot

A novel breed of robots known as socially assistive robots is emerging. These robots are capable of providing assistance to individuals through social and cognitive interaction. However, there are a number of research issues that need to be addressed in order to design such robots. In this paper, we address one main challenge in the development of intelligent socially assistive robots: The robot’s ability to identify human non-verbal communication during assistive interactions. In particular, we present a unique non-contact and non-restricting automated sensor-based approach for identification and categorization of human upper body language in determining how accessible a person is to the robot during natural real-time human-robot interaction (HRI). This classification will allow a robot to effectively determine its own reactive task-driven behavior during assistive interactions. Human body language is an important aspect of communicative nonverbal behavior. Body pose and position can play a vital role in conveying human intent, moods, attitudes and affect. Preliminary experiments show the potential of integrating the proposed body language recognition and classification technique into socially assistive robotic systems partaking in HRI scenarios.