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Abstract Mobile robots with manipulation capability are a key technology that enables flexible robotic interactions, large area covering and remote exploration. This paper presents a novel class of actuation-coordinated mobile parallel robots (ACMPRs) that utilize parallel mechanism configurations and perform hybrid moving and manipulation functions through coordinated wheel actuators. The ACMPRs differ with existing mobile manipulators by their unique combination of the mobile wheel actuators and the parallel mechanism topology through prismatic joint connections. Common motion of the wheels will provide mobile function while their relative motion will actuate the parallel manipulation function. This new concept reduces actuation requirement and increases manipulation accuracy and mobile motion stability through coordinated and connected wheel actuators comparing with existing mobile parallel manipulators. The relative wheel location on the base frame also enables a reconfigurable base size with variable moving stability on the ground. The basic concept and general type synthesis are introduced and followed by kinematics and inverse dynamics analysis of a selected three limb ACMPR. A numerical simulation also illustrates the dynamics model and the motion property of the new mobile parallel robot (MPR) followed by a prototype-based experimental validation. The work provides a basis for introducing this new class of robotsmore »for potential applications in surveillance, industrial automation, construction, transportation, human assistance, medical applications, and other operations in extreme environment such as nuclear plants, Mars, etc.« less

This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using the Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data were collected using the latest commercial wearable sensors, behavioral data were collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data were stored in single ROSbag files rather than in conventional Comma-Separated Values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and the size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be a great resource for many researchers in the fields of affective computing, Human-Computer Interaction (HCI), and Human-Robot Interaction (HRI).

In multi-agent systems, limited resources must be shared by individuals during missions to maximize the group utility of the system in the field. In this paper, we present a generalized adaptive self-organization process for multi-agent systems featuring fast and efficient distribution of a consumable and refillable on-board resource throughout the group. An adaptive inter-agent spacing (AIS) controller based on individual resource levels is proposed that spaces out high resource bearing agents throughout the group including the group boundary extrema, and allows low resource bearing agents to adaptively occupy the in-between spaces receiving resource from the high resource bearing agents without over-crowding. Experimental results for cases with and without the proposed AIS controller validate faster convergence of individual resource levels to the group mean resource level using the proposed AIS controller. The generalized approach of the self-organizing process allows flexibility in adapting the proposed AIS controller for various multi-agent applications.