At present, most of the current Simultaneous Localization and Mapping (SLAM) algorithms are limited to work in static environments. However, the world is not static and dynamic objects are typically present in the environments, leading to the failure of the general SLAM. In this paper, a dynamic object removal method combining semantic detection with depth image segmentation is proposed and applied in the real-time SLAM library for cameras ORB-SLAM2 system to achieve robust RGB-D SLAM for dynamic environments. In the proposed method, the potential dynamic regions are captured via YOLOv4 and K-means image segmentation. Different from the general purposes of processing dynamic regions, the potential dynamic regions are redetected by dynamic outliers rejection, which improves the reliability of dynamic object removal. Experiments using the TUM RGB-D dataset demonstrate that the proposed method performs with increased robustness and accuracy when compared to the original ORB-SLAM2 without dynamic object removal in dynamic environments.

This paper presents a kinetostatic framework to analyze the grasping and in-hand object manipulation abilities of two-finger underactuated hands. The framework includes a procedure to compute the Grasp Matrix and the Hand Jacobian for objects and fingertips of arbitrary shape considering rolling contacts without slipping. The usefulness of the proposed approach is illustrated in a case study of a pair of underactuated fingers driven by a tendon-pulley differential transmission mechanism and capable of performing in-hand object manipulation. The manipulability region for different object and fingertip shapes is computed and the results are discussed.

This paper proposes a method of tire force estimation designed for use in navigation decision making for Unmanned Ground Vehicles (UGVs) operating on off-road terrains. This method (called the Centroid Method) uses a 3D point cloud representation of the terrain to determine tire-ground interaction, and a linear spring to determine the corresponding tire forces. Simulation results of tire force estimation of the Centroid Method are compared against the experimentally validated MF-Swift model, including on a fractal terrain surface which simulates a rough off-road terrain. The results show that the Centroid Method performs well for longitudinal and vertical tire force estimation especially for lower frequencies up to about 8-21Hz. Although the Centroid Method does not provide the same detail as the advanced tire models such as MF-Swift, it is significantly less complex and simpler to implement for enabling efficient on-board UGV navigation decision making.

This paper describes a method of vehicle state prediction for an autonomous high-speed off-road Unmanned Ground Vehicle (UGV). Effective vehicle state prediction will allow a UGV to plan its navigation such that states (such as vertical acceleration induced by the terrain roughness) never exceed a desired threshold. In this paper a model of an n-wheeled generic vehicle is used determine its dynamics. Using a known terrain input profile the vehicle's output states are predicted using the developed n-wheel model. Simulated results of this vehicle state prediction approach are presented, as well as experimental tests using a UGV platform called Loc8. The experimental results used a 3D point cloud to determine the terrain input profile. Methods from the literature are tested against the developed n-wheeled vehicle state prediction method. Results show this n-wheel technique presents both advantages and disadvantages in comparison with existing techniques. The proposed approach predicts the average absolute acceleration much closer to the measured average absolute acceleration than existing approaches.

No abstract available.

Current Unmanned Vehicle (UV) navigation systems are capable of autonomous navigation among disperse obstacles. However, these systems may fail to guide vehicles through highly confined environments because they do not explicitly consider the geometry of the vehicle in the navigation task. This paper presents a methodology that enables the navigation of Unmanned Vehicles (UVs) in such 3D environments. The proposed approach uses a hybrid navigation architecture which employs a global path planner and a local obstacle avoidance methodology in parallel and combines them utilizing an improved Model Predictive Control (MPC) approach that incorporates the geometry of the UV in the cost function. Using MPC enables the UV to generate complex maneuvering trajectories while avoiding obstacles, respecting the dynamic characteristics of the UV and preventing state and input saturation. Simulations in 2D and 3D demonstrate the effectiveness of the proposed method for the navigation of a highly maneuverable Rotary Unmanned Aerial Vehicle (RUAV) in a highly confined environment.

This study examined the interactions between children and robots by observing whether children help a robot complete a task under five conditions to determine which elicited the most help. Each condition had an experimental and control group, with 20-32 children (even number of boys and girls) in each group. Visitors to a science centre located in a major Western Canadian city were invited to participate in an experiment set up at the centre. Their behaviors with a robot, a small 5 degree of freedom robot arm, were observed. Results of chi-square analyses indicated that children are most likely to help a robot after being introduced to it, X2(1) = 4.15, p = .04. This condition was the only one of five tested that demonstrated a significant increase in children's helping behaviors. These results suggest that an adult's demonstrated positive introduction to a robot impacts children's helping behaviors towards it.

Centuries ago, the existence of life was explained by the presence of a soul [1]. Known as animism, this term was re-defined in the 1970s by Piaget as young children's beliefs that inanimate objects are capable of actions and have lifelike qualities. With the development of robots in the 21st century, researchers have yet to examine whether animism is apparent in children's impressions of robots. The purpose of this study was to examine children's perspectives about the cognitive, affective, and behavioral attributes of a robot. Visitors to a science centre located in a major Western Canadian city were invited to participate in an experiment set up at the centre. A total of 198 children ages 5 to 16 years (M = 8.18 years) with an approximate even number of boys and girls participated. Children were interviewed after observing a robot, a small 5 degree of freedom robot arm, perform a block stacking task. Answers to the six questions about the robot were scored according to whether they referenced humanistic qualities. Frequency and content analysis results suggest that a significant proportion of children ascribe cognitive, affective, and behavioral characteristics to robots.

Flexible manufacturing systems (FMSs) may comprise a number of workcells where production resources are shared among the cells for a variety of practical reasons. Although the utilization of independent workcells with sharing resources improves the flexibility of FMSs, avoiding deadlocks is essential for their successful implementation. The paper introduces a methodology for the synthesis of a set of conflict- and deadlock-free supervisors to individually control every workcell within a FMS inter-related by common (shared) resources. The proposed methodology is based on extended Moore automata and controlled-automata theories.

The paper presents a novel and generic PC/PLC-based software/hardware architecture for the control of flexible manufacturing workcells. The proposed implementation methodology is based on the utilization of any one of the available formal discrete-event-system control theories in conjunction with state-of-the-art industrial programmable-logic controllers (PLCs). The specific control theory used is a combination of extended Moore automata and Ramadge-Wonham automata that has been developed by our research group. The modular control software architecture has been developed for MS-Windows environments. The effective graphical user interface provides a transparent programming environment.

Traditionally, the function of a workcell supervisor has been to coordinate the various elements comprising the workcell for the production of a specific a-priori set of parts using a predetermined set of machines. Correspondingly, current workcell supervisor synthesis methodologies assume that the part production mix remains constant. In practice, however, the product mix may change over time. New part types are required to be processed, while other (nominal) part types may be temporarily discontinued. The workcell supervisory control approach proposed in this paper is that of utilizing a pair of non-communicating independent supervisors, working in concert to achieve the production of nominal and a-priori unplanned-for new part types: nominal and complementary supervisors. The nominal supervisor is responsible for controlling the behavior of the nominal workcell, producing the set of a-priori planned part types, while the complementary supervisor controls the flow of the unplanned-for new part types, where each supervisor is synthesized individually.

The utilization of flexible manufacturing workcells (FMCs) to produce families of parts in many possible orders of operations and choices of different machines is advantageous. The modeling and control of such discrete-event systems (DESs) have generally been performed in a hierarchical structure. Despite intensive research on the theoretical control of DESs, however, current techniques such as controlled automata can still primarily be used for the supervisory control of simple cells. In the paper, a modeling and control synthesis technique is presented for FMCs that allow part-routing flexibility. The proposed methodology combines extended Moore automata (EMA) and controlled-automata theories to synthesize supervisors for such FMCs. EMA-based supervisors have the capability to read (receive) multiple pieces of information regarding the behavior of the FMC. Based on this information they can explicitly generate control commands issued to cell devices. In order to significantly reduce the state-space of EMA-based supervisors without affecting the behavior enforced on the (controlled) FMC, a minimization procedure is also outlined.

Flexible-manufacturing systems (FMSs) may comprise a number of workcells where production resources are shared among the workcells for a variety of practical reasons. Although the utilization of independent workcells with sharing resources improves the flexibility of FMSs, avoiding deadlocks is essential for their successful implementation. This paper introduces a novel methodology for the synthesis of a set of conflict- and deadlock-free supervisors to individually control every workcell within a FMS inter-related by common (shared) resources. The proposed methodology is based on Extended Moore Automata (EMA) and Controlled-Automata theories. A new algorithmic procedure to analyze the concurrent operation of supervisors is also introduced in order to check for the existence or absence of deadlock states.

The modeling and control of flexible-manufacturing workcells (FMCs) has generally been performed in a hierarchical structure, where at the highest level they have been modeled as discrete-event systems (DESs). In this paper, a new DES control synthesis technique that combines Moore automata and controlled-automata theories to synthesize supervisors is presented. The obtained supervisors can monitor multiple pieces of information regarding the behavior of the FMC and can explicitly generate control commands issued to individual cell devices.