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Robotics is a disruptive technology that has already revolutionized patient healthcare globally. This technology is presently helping to perform various essential tasks such as conducting operations via numerous specializations and managing the entire operating room. Robot surgery is, in reality, available worldwide for knee substitution, correction of the hernia, and colon resection. Surgical robots entered the operating theatres far before entering other medicine-related robotics applications and now facilitate better outcomes for a whole range of healthcare products. In the COVID-19 pandemic, some robots were used in hospitals to deliver medicines, screen, perform odd jobs, and maintain hygienic conditions. This paper provides an overview about robotics and its various applications useful for healthcare. Significant enhancement, quality services, and advancements in healthcare services are also discussed. Here, we have identified the role of robotics in healthcare as a technology that dramatically changes the healthcare field. An artificial intelligence robot can duplicate creativity via algorithms, and its programming too plays a crucial role. Hospitals can now save time and money by removing the need for physical chores for different jobs. It is helpful for surgical training, exoskeletons, intelligent prostheses and bionics, robotic nurses, treatment, medicines, logistics, telepresence, and cleaning services. Robotics technologies such as gesture control, machine view, voice recognition, and touch sensor technology are also available. The future is bright with lower installation and maintenance costs.

During last decades, Cellular Automata (CAs) as bio-inspired parallel computational tools have been proven rather efficient and robust on modeling and simulating many different physical processes and systems and solving scientific problems, in which global behavior arises from the collective effect of simple components that interact locally. Among others of most renowned and well established CA applications, crowd evacuation and pedestrian dynamics are considered ones of the most timely and lively topics. Numerous models and computational paradigms of CAs either as standalone models or coupled with other theoretical and practical modeling approaches have been introduced in literature. All these crowd models are taking advantage of the fact that CA show evidence of a macroscopic nature with microscopic extensions, i.e. they provide adequate details in the description of human behavior and interaction, whilst they retain the computational cost at low levels. In this aspect, several CA models for crowd evacuation focusing on different modeling principles, like potential fields techniques, obstacle avoidance, follow the leader principles, grouping and queuing theory, long memory effects, etc. are presented in this paper. Moreover, having in mind the inherent parallelism of CA and their straightforward implementation in hardware, some anticipative crowd management systems based on CAs are also shown when operating on medium density crowd evacuation for indoor and outdoor environments. Real world cases and different environments were examined proving the efficiency of the proposed CA based anticipative systems. The proposed hardware implementation of the CAs-based crowd simulation models is advantageous in terms of low-cost, high-speed, compactness and portability features. Finally, robot guided evacuation with the help of CAs is also presented. The proposed framework relies on the well established CAs simulation models, while it employs a real-world evacuation implementation assisted by a mobile robotic guide, which in turn guides people towards a less congestive exit at a time.

We consider the problem of evacuating some people from an unknown convex region. The people do neither have information about the region boundary nor their positions. We seek competitive strategy that achieves a competitive ratio of the evacuation path over the shortest path. In the scenario of general plane, we propose a strategy SOP for one group, and prove that its competitive ratio is 19.64. And we propose a 14.37-competitive strategy STP for two groups. Also, we present efficient strategies in the scenario of grid network. Furthermore, our strategies can be used for guiding the robot to search the boundary of an unknown region.

A new method recognizing and locating partially occluded two-dimensional parts is presented. The objects are described by a set of segments derived form the polygonal coding of their contours, and by the geometrical relationships between the segments. Rewriting rules are used to improve the stability of the polygonal coding. The identification process utilizes a robust hypothesis generator, from which the segment research assisted by the spatial relationship is propagated into the scene. The originality of this method relies mainly on the use of structural relationships between the segments to select the robust initialization hypotheses, and the use of structural research to achieve hypothesis propagation. These last two points with the use of a hash-coding technique to improve the location of predicted segments, greatly reduce the combinatories and make the algorithm particularly rapid and effective. This approach is integrated within a vision system of a Flexible Assembly Workcell to accomplish the automatic assembly of partially overlapping parts.

The robot path planning problem involves planning optimal paths for a robot to follow while ensuring it will not hit any obstacles or itself. In a state or perfectly known world, this has been addressed using the configuration space representation and the A* search algorithm. However, when movement, changes, or unexpected obstacles occur in the environment, a new method, Differential A*, can adapt the solution to the current situation. It updates only the fraction of space that is critically affected. This technique can provide significant speed improvements, with the same desired results, compared to complete space regeneration.

This paper presents the design and development of a real-time eye-in-hand stereo-vision system to aid robot guidance in a manufacturing environment. The stereo vision head comprises a novel camera arrangement with servo-vergence, focus, and aperture that continuously provides high-quality images to a dedicated image processing system and parallel processing array. The stereo head has four degrees of freedom but it relies on the robot end-effector for all remaining movement. This provides the robot with exploratory sensing abilities allowing it to undertake a wider variety of less constrained tasks. Unlike other stereo vision research heads, the overriding factor in the Surrey head has been a truly integrated engineering approach in an attempt to solve an extremely complex problem. The head is low cost, low weight, employs state-of-the-art motor technology, is highly controllable and occupies a small-sized envelope. Its intended applications include high-accuracy metrology, 3-D path following, object recognition and tracking, parts manipulation and component inspection for the manufacturing industry.

Insects such as ants and bees are capable of surprisingly good navigation, despite the small size and relative simplicity of their brains. Recent experimental research in our laboratory, summarized in this chapter, indicates that honeybees estimate the distance travelled to a food site in terms of the image motion that they experience en route. This finding has inspired us to design and build a robot that navigates using a visually-driven odometer.

This article offers a case study of how to teach image computation in an upper level elective course on robotics with a significant number of non-Computer Science majors. The MACS 415 course at the Colorado School of Mines is required for the popular interdisciplinary undergraduate minor in Robotics and AI. It is mandated to provide a broad survey of the artificial intelligence tools available to roboticists, including image computation.

Teaching image computation in a robotics elective is challenging both because of the limited time that can be spent on computer vision, and because of the attributes of the students. Non-CS majors typically do not have enough programming experience to program DSP algorithms, yet the students' preferred learning style is "hands-on." In order to reconcile this dilemma, we (1) cover a broad set of topics in class, (2) have several laboratory assignments using khoros, and (3) require the students to complete a robot project involving computer vision. The article summarizes the lessons learned to date, which are expected to be applicable to any course with non-majors involving image computation.

We discuss the experimental implementation of a chemical controller for a robotic hand. In the present case study, we have designed a closed system where a Belousov–Zhabotinsky (BZ) thin layer chemical reactor was linked to the robotic hand via an array of photo-sensors and the fingers of the hand were able to stimulate the excitation dynamics in the BZ medium via the local addition of an activator species. A principal working loop of the chemo-robotic system is that oxidation wave fronts traveling in the medium are detected by photo-sensors and cause (via a microcontroller) the fingers of the hand to bend. When a finger bends, it is set up to apply a small quantity of colloid silver to the reaction and thus causes an additional excitation wave. The traveling and interacting waves stimulate further movements of the fingers and patterns of behavior are observed. These patterns of behavior have been modeled using a cellular automaton. In the paper, we offer an experimental set-up, including algorithms and interfacing, for an experimental chemical robotic hand controller, which contributes to the fields of nonclassical computation, nonlinear physics, and unconventional robotics.

In this communication the emergent behavior of clusters of robots with chaotic kinematics attractors is shown. Even if the aim of the research started from the study of the cooperative behavior of inspection robots, the reported results (including also the role of chaotic synchronization in the generation of the kinematics trajectory), show new aesthetic features of the motion in mechanical control systems.

We present an algorithm for a single pursuer with one flashlight searching for an unpredictable, moving target in a 2D environment. The algorithm decides whether a simple polygon with n edges and m concave regions can be cleared by the pursuer, and if so, constructs a search schedule of length O(m) in time O(m²+mlogn+n). The key ideas in this algorithm include a representation called "visibility obstruction diagram" and its "skeleton": a combinatorial decomposition based on a number of critical visibility events. An implementation is presented along with a computed example.

We present an algorithm for a pair of pursuers, each with one flashlight, searching for an unpredictable, moving target in a 2D environment (simple polygon). Given a polygon with n edges and m concave regions, the algorithm decides in time O(n² + nm² + m⁴) whether the polygon can be cleared by the two 1-searchers, and if so, constructs a search schedule. The pursuers are allowed to move on the boundary and in the interior of the polygon. They are not required to maintain mutual visibility throughout the pursuit.

The main concern of this paper is the detection of double contact configurations for some polygons moving in translation in a polygonal environment. We first establish some general properties about such configurations and give conditions of existence of double contacts for two or three objects.

For three convex polygons moving in a polygonal environment or three simple polygons moving in a rectangle there always exists a double contact. Two examples without possibility of double contacts are given, one with three polygons (not convex) moving in a polygonal environment, and one with four convex polygons moving in a rectangle. We deduce an algorithm detecting a double contact position in time O(n²) (resp. O(n³)) for two (resp. three) convex polygons of constant sizes moving in a non-convex polygon of size n.

The problem of designing a k-link robot arm confined in a convex polygon that can reach any point in the polygon starting from a fixed initial configuration is considered. The links of an arm are assumed to be all of the same length. We present a necessary condition and a sufficient condition on the shape of the given polygon for the existence of such a k-link arm for various values of k, as well as necessary and sufficient conditions for rectangles, triangles and diamonds to have such an arm. We then study the case k=2, and show that, for an arbitrary n-sided convex polygon, in O(n²) time we can decide whether there exists a 2-link arm that can reach all inside points, and construct such an arm if it exists. Finally, we prove a lower bound and an upper bound on the number of links needed to construct an arm that can reach every point in a general n-sided convex polygon, and show that the two bounds can differ by at most one. The constructive proof of the upper bound thus provides a simple method for designing a desired arm having at most k+1 links when a minimum of k links are necessary, for any k≥3. The method can be implemented to run in O(n²) time.

The following problem appears in robotics. A number of small, circular robots live in a common planar workspace. Each is attached by a flexible cable of finite length to a point on the boundary of this workspace. Each robot has a target point in the workspace it must reach. When a robot has reached its target, its cable will have been dragged out into the workspace and possibly pushed and bent by other robots. The cables remain taut at all times and may not overlap, but may bend around other robots. When only the target points are specified for the robots, their motion can produce arbitrarily complex cable configurations. The more complex a cable configuration is, the more restrictive it is to the motion of the robots. To keep restrictions on the robots' motions at a minimum, it is necessary to specify, in addition to the target points, a configuration of the cables that is as simple as possible but allows all robots to reach their targets. The problem of finding the simplest cable configuration for a given set of target points is shown to reduce to the problem of finding a minimal set of nonintersecting routes in a Euclidean graph whose nodes are the robots' cable line. That no set of edges can intersect any other set of edges is an unusual characteristic of this graph problem. This consideration leads to interesting geometric analysis used to determine which relative placements of the graph edges represent overlapping cable lines. An algorithm is suggested that uses an exhaustive search method with pruning to find a set of nonintersecting routes in the graph that is minimal according to a chosen criterion. The algorithm has been implemented and tested; examples of its performance are given.

A new polygon decomposition problem, the anchored area partition problem, which has applications to a multiple-robot terrain-covering problem is presented. This problem concerns dividing a given polygon P into n polygonal pieces, each of a specified area and each containing a certain point (site) on its boundary or in its interior. First the algorithm for the case when P is convex and contains no holes is presented. Then the generalized version that handles nonconvex and nonsimply connected polygons is presented. The algorithm uses sweep-line and divide-and-conquer techniques to construct the polygon partition. The input polygon P is assumed to have been divided into a set of p convex pieces (p = 1 when P is convex), which can be done in O(v_Ploglog v_P) time, where v_P is the number of vertices of P and p = O(v_P), using algorithms presented elsewhere in the literature. Assuming this convex decomposition, the running time of the algorithm presented here is O(pn²+vn), where v is the sum of the number of vertices of the convex pieces.

The question addressed in this paper is which types of spatial reference human users employ in the interaction with a robot and how a cognitively adequate model of these strategies can be implemented. In experiments we explored how human users approach an artificial communication partner, which was designed to mimic spatial reference among humans. Our findings show that spatial reference in human-robot interaction differs from natural situations in human-human interaction in seveal respects. For instance, many users unexpectedly employed fine-grained, path-based, instructions rather than specifying the intended goal object of the action directly. If instructions were not successful, participants created less and less complex descriptions. Those users who did specify the goal object were found to employ those kinds of spatial reference strategies implemented in our computational model. In particular, they exploited the presence of several similar objects by perceiving and referring to them linguistically as a group.

Recently, many brain modelling efforts attempt to support cognitive abilities of artificial organisms. The present work introduces a computational framework to address brain modelling, emphasizing on the integrative performance of substructures. Specifically, we present an agent-based representation of brain areas, together with a hierarchical cooperative coevolutionary scheme, which is able to highlight both the speciality of brain areas and their cooperative performance. The inherent ability of coevolutionary methods to design cooperative partial structures supports the design of partial brain models and, at the same time, provides a consistent method to achieve their integration. As a result, the proposed approach proceeds in either an incremental or a compound mode. Furthermore, the performance of the model in lesion conditions is considered during the design process to enforce the reliability of the result. Implemented models are embedded in a robotic platform to support its behavioral capabilities.

Sampling-based motion planning had an enormous impact on robot motion planning because of its efficiency and scalability. Many sampling-based motion planners construct a probabilistic roadmap (PRM) that captures the connectivity of the robot's free configuration space. A valid node of a PRM contains a collision-free robot configuration (also known as a sample) and a valid edge of a PRM connects two valid nodes with a collision-free path. Nodes connected by an edge are usually also required to satisfy additional requirements based on the distance between them. PRM planners use PRMs. Increasing the expressive power will allow PRMs to be used for a wider set of motion planning problems. In this paper we report on increasing the expressive power of PRMs by including the following five features in PRMs-nodes with multiple samples that need not be organized as a graph, temporal intervals of validity of nodes and edges, nodes with samples of multiple robots, special edges for the state transitions performed by humans sharing a workspace with robots, and conditional validity of samples and edges. We report on motion planning problems solvable using these new features.

This is a survey of recent domain-specific applications involving IoT (Internet of Things) and various areas of artificial intelligence. The domains and problems include malthouse, zoo management, protection of humans in hot working conditions, factory-floor scheduling, prediction of tool-wear-compensation offset, forecasting flooding, reduction in traffic congestion, prediction of flight delays, alcohol-consumption monitoring, recognition of activities at home, prediction of consumption of hot water, search and rescue, steel manufacturing, stroke-survivor monitoring, preventing overcrowding on beaches, and identifying people who are at risk for falls at homes. These applications show the depth, breadth, and diversity of the synergistic combination of AI and IoT.