The aptitude to identify the emotional states of others and response to exposed emotions is an important aspect of human social intelligence. Robots are expected to be prevalent in society to assist humans in various tasks. Human-robot interaction (HRI) is of critical importance in the assistive robotics sector. Smart digital assistants and assistive robots fail quite often when a request is not well defined verbally. When the assistant fails to provide services as desired, the person may exhibit an emotional response such as anger or frustration through expressions in their face and voice. It is critical that robots understand not only the language, but also human psychology. A novel affection-based perception architecture for cooperative HRIs is studied in this paper, where the agent is expected to recognize human emotional states, thus encourages a natural bonding between the human and the robotic artifact. We propose a method to close the loop using measured emotions to grade HRIs. This metric will be used as a reward mechanism to adjust the assistant's behavior adaptively. Emotion levels from users are detected through vision and speech inputs processed by deep neural networks (NNs). Negative emotions exhibit a change in performance until the user is satisfied.

This paper focuses on data fusion, which is fundamental to one of the most important modules in any autonomous system: perception. Over the past decade, there has been a surge in the usage of smart/autonomous mobility systems. Such systems can be used in various areas of life like safe mobility for the disabled, senior citizens, and so on and are dependent on accurate sensor information in order to function optimally. This information may be from a single sensor or a suite of sensors with the same or different modalities. We review various types of sensors, their data, and the need for fusion of the data with each other to output the best data for the task at hand, which in this case is autonomous navigation. In order to obtain such accurate data, we need to have optimal technology to read the sensor data, process the data, eliminate or at least reduce the noise and then use the data for the required tasks. We present a survey of the current data processing techniques that implement data fusion using different sensors like LiDAR that use light scan technology, stereo/depth cameras, Red Green Blue monocular (RGB) and Time-of-flight (TOF) cameras that use optical technology and review the efficiency of using fused data from multiple sensors rather than a single sensor in autonomous navigation tasks like mapping, obstacle detection, and avoidance or localization. This survey will provide sensor information to researchers who intend to accomplish the task of motion control of a robot and detail the use of LiDAR and cameras to accomplish robot navigation.

Formation control has been a popular research topic in recent years. This is primarily due to the the use of formation control to coordinate the movement of a swarm of robots. In this paper, with the primary objective of guaranteeing image overlap, a novel bird flocking inspired formation control algorithm was developed to control a swarm of unmanned aerial vehicles (UAVs). Our approach uses only stereo cameras, global positioning system (GPS), and inertial measurement units (IMUs), and removes the need of feature or pattern matching. By utilizing stereo cameras, virtual tracking points can be calculated. The convergence of these tracking points are chosen as the primary formation control goal because if the virtual tracking points are converged, it is guaranteed that the field of view of the cameras of the various UAVs always have some overlap. This allows for different types of stitching methods to be used to form a larger image with no gaps, depending on the configuration of tracking points. Five different control algorithms were developed: direct method, proportional integral derivative, bang-bang, short window model predictive, and long window model predictive. The formation controller was tested in three simulation environments to verify the robustness and the effectiveness of our algorithm.

Researchers have extensively explored indoor localization in recent years. Robotic applications often require precise positioning, which is difficult when access to a GPS is limited or compromised. Many of the existing approaches require either prior knowledge of the local environment and fixed landmarks or complex and expensive hardware to achieve the necessary degree of accuracy. In this article, we examine the use of the HTC virtual reality hardware along with our novel approach, VIVEPOSE, to perform indoor robotic localization. We then compare the accuracy of our proposed indoor localization system to current approaches that use traditional odometry sensor-based localization. Finally, we present and demonstrate a leader-follower approach using VIVEPOSE and show how the HTC Vive tracker can be successful for indoor localization for a robotics application.

In this article a widely studied consensus algorithm for processing sensor data of multimissile systems is considered. A novelty of algorithms to reach a consensus given time-delay constraints is introduced. In previous work, the author proposed an algorithm addressing the same problem. The primary goal of this article is to evaluate the performance of the proposed algorithm. The performance was evaluated in Monte Carlo runs in a target tracking scenario. A mission with multimissile trying to intercept a single flying threat is assumed in this scenario. The interceptors are using nonhomogeneous sensors to measure the location of the threat continuously. Each missile computes its state prediction and shares it with its neighboring missile only. However, the shared information is applied to arbitrary nonuniform time delay. The entire group of missiles must reach a consensus on the target’s location. The performance is evaluated in terms of overall estimation error, conflict resolution, and time to reach a consensus. Different scenarios are also simulated to examine the effectiveness of the number of missiles performing observation, the delay value, and sensor uncertainty in the overall system stability. Moreover, the tradeoffs of using the algorithm are identified.