We present a novel 3D reconstruction-based SLAM (Simultaneous Localization and Mapping) approach for robots that leverage multimodal sensory input data, including a camera and a 2D lidar. By integrating these inputs with the gaussian splatting technique, our method significantly enhances performance over traditional SLAM approaches. Traditional SLAM techniques often struggle with the limitations of monocular vision and fail to accurately map and locate objects in dynamic and cluttered environments. Purely relying on camera to localize the robot and map creation is challenging in the presence of dynamic obstacles in the scene. To address this, we proposed a multimodal sensor fusion-based 3D reconstruction. Our approach employs lidar-based localization to achieve precise positioning of both the camera and the robot, while utilizing the gaussian splatting technique for robust environmental mapping and 3D reconstruction. This approach is robust to dynamic obstacles in the scene. We have conducted extensive experiments in various real-world and simulated environments, demonstrating that our method not only outperforms traditional monocular SLAM approaches but also achieves higher accuracy in terms of localization and constructed map. Our results demonstrate substantial improvements in 3D reconstruction for mobile robots, achieving reduced computational load, higher FPS and enhanced scaling accuracy.

In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the receiver, Bob, who utilizes either homodyne or heterodyne detection. The latter node applies the least-squares approach to estimate the effective MIMO channel gain matrix prior to receiving the secret key, and this estimation is made available to Alice via an error-free feedback channel. An eavesdropper, Eve, is assumed to employ either a collective Gaussian entanglement attack or an individual attack on the feedback channel to obtain the estimated channel state information. We present novel closed-form expressions for the secret key rate (SKR) performance of the proposed RIS-assisted THz CV-QKD system. An optimization framework to obtain the optimal phase shifts of the RIS to maximize the SKR is proposed, and a particle-swarm optimization (PSO)-based algorithm is deployed to solve the optimization problem. The effect of various system parameters, such as the number of RIS elements and their phase configurations, the channel estimation error, and the detector noise, on the SKR performance is studied via numerical evaluation of the derived formula. It is demonstrated that the RIS contributes to larger SKR for larger link distances, and that heterodyne detection is preferable over homodyne at lower pilot symbol powers.

Heart rate and respiratory rate are critical vital signs in infants, used as non-invasive indicators to assess health status and address potential issues. However, many healthcare settings face a limitation: the inability to measure these parameters simultaneously, as most non-invasive medical-grade instruments track either heart rate or respiratory rate, not both. Addressing this gap, the study investigates the use of machine learning techniques for the bi-directional estimation of infant heart and respiratory rates. It evaluates the performance of several machine learning models, including Convolutional Neural Network, Multilayer Perceptron, Gated Recurrent Units, Long Short-Term Memory, and the gradient boosting models Extreme Gradient Boosting and Light Gradient Boosting Machine, in predicting these vital signs using the Medical Information Mart for Intensive Care II dataset. Numerical results show that the gradient boosting models Extreme Gradient Boosting and Light Gradient Boosting Machine outperformed the neural network-based approaches in accuracy.

Scheduling tasks for multiple heterogeneous robots is challenging, especially with human supervision in demand-aware environments. This research aims to understand human decision-making and its impact on scheduling through two studies, the first is a mixed reality based user study to explore how human perception of the scheduling environment influences task scheduling and facilitates personalized resource allocation. Our findings indicate that human task schedulers exhibit enhanced performance when assisted by autonomous agents, compared to scenarios with limited autonomy in robotic systems. To explore the impact of robot planning on human decision-making and scheduling, we conducted the second study which employed a mixed reality-based warehouse environment, where two users controlled different robots with shared objectives. Results showed that visual aids like collision cones improved collision-aware scheduling without compromising demand-aware capabilities.

COVID-19, being declared a pandemic, has affected human lives, economics, healthcare, and education in multiple geographic locations worldwide. The outbreak of this pandemic has demonstrated the necessity of efficient decision making to handle supply chain disruptions, especially for medical resources. The work in this article focuses on creating a decision support system (DSS) for the hierarchical allocation of critical medical resources among different geographical locations. The methodology adopted in this article relies on the concept of noncooperative games and designs a single-stage, multiplayer game to allocate medical resources among the affected locations while considering them as the self-interested players of the game. All the players or the affected locations involved in the game are imposed by different penalties derived from a nonmonetary cost function. The solution of the resource allocation game is given by one of the Nash equilibria of the game chosen by using different selection methods. This article proves analytically that the proposed game-theoretic model always admits pure strategy Nash equilibria (PSNE). Realistic case studies are demonstrated to validate the results given in this article.

Index modulation (IM) schemes for reconfigurable intelligent surfaces (RIS)-assisted systems are envisioned as promising technologies for fifth-generation-advanced and sixth-generation (6G) wireless communication systems to enhance various system capabilities such as coverage area and network capacity. In this paper, we consider a receive diversity RIS-assisted wireless communication system employing IM schemes, namely, space-shift keying (SSK) with binary modulation and spatial modulation (SM) with M-ary modulation for data transmission. The RIS lies in close proximity to the transmitter, and the transmitted data is subjected to a fading environment with a prominent line-of-sight component statistically modeled by a Rician distribution. A receiver structure based on a greedy detection rule is employed to select the receive diversity branch with the highest received signal energy for demodulation. The performance of the considered system is evaluated by obtaining a series-form expression for the probability of erroneous index detection (PED) of the considered target antenna using a characteristic function approach. In addition, closed-form and asymptotic expressions at high and low signal-to-noise ratios (SNRs) for the bit error rate (BER) of the SSK-based system, and the SM-based system employing M-ary phase-shift keying and M-ary quadrature amplitude modulation schemes are derived. The dependencies of the system performance on various parameters are corroborated via numerical results. The asymptotic expressions and results of PED and BER at high and low SNR values lead to the observation of a performance saturation and the presence of an SNR value as a point of inflection, which is attributed to the greedy detector.

A rotated quadrature phase-shift keying (QPSK) based semi-orthogonal multiple access (SOMA) data transmission is considered for a visible light communication (VLC) system with two users. The rotation of the QPSK constellation at the transmitter followed by a data pre-processing technique at the receiver of each user ensures the elimination of the successive interference cancellation unit. An optimal maximum likelihood receiver is proposed for the system under consideration using which, the closed-form expressions for the symbol error probability (SEP) for both the users in the VLC system are derived. The optimization problem to obtain the optimal angle of rotation of the QPSK constellation which minimizes the SEP of the users is formulated and solved. The dependency of the optimality of the QPSK rotation angle on various VLC system parameters is studied via numerical results which lead to the observation of a value of the signal-to-noise ratio of the system around 8.8 dB about which the dependency of the optimal rotation angle on the other VLC system parameters interchange, thus providing design aspects for a SOMA-VLC system.

This paper presents one of the first explorations of transparency and explainability of Heart Rate Variability (HRV) based deep learning models designed for stress detection. We employed Shapley additive explanations (SHAP) as an explainable AI (XAI) method, and cross-validated the results with saliency maps, which provides valuable insights into the main contributing factors for decision-making process of these deep models.

This paper addresses the problem of multi-robot task scheduling by estimating the demand for the tasks in a real-world scenario. Scheduling tasks for multiple robots becomes complex when a human is involved in allocating limited resources. We propose a stochastic multi-agent multi-armed bandit based task scheduler which prioritizes the tasks based on the estimated demand for the tasks. To gain insight into the varying priorities of a human task allocator in a multi-armed bandit scenario, we conducted a user study in a Mixed Reality environment which can be used to customize the resource allocation process. We observe that the users consistently made sub-optimal choices due to their preference to minimize other parameters (such as cumulative distance travelled) of the real world scenario rather than strictly adhering to the optimal strategy. Our proposed method uses the Thompson sampling bandit algorithm with ϵ-greedy approach to solve the multi-agent multi-armed bandit problem. The approach outperformed other methods such as first-come-first-serve, rate monotonic scheduling, and heuristic-based Min-interference approaches in terms of the demand aware performance index.

With the increasing adoption of robots across industries, it is crucial to focus on developing advanced algorithms that enable robots to anticipate, comprehend, and plan their actions effectively in collaboration with humans. We introduce the Robot Autonomous Motion (RoAM) video dataset, which is collected with a custom-made turtlebot3 Burger robot in a variety of indoor environments recording various human motions from the robot’s ego-vision. The dataset also includes synchronized records of the LiDAR scan and all control actions taken by the robot as it navigates around static and moving human agents. The unique dataset provides an opportunity to develop and benchmark new visual prediction frameworks that can predict future image frames based on the action taken by the recording agent in partially observable scenarios or cases where the imaging sensor is mounted on a moving platform. We have benchmarked the dataset on our novel deep visual prediction framework called ACPNet where the approximated future image frames are also conditioned on action taken by the robot and demonstrated its potential for incorporating robot dynamics into the video prediction paradigm for mobile robotics and autonomous navigation research.

Over recent years, reflective intelligent surface (RIS) and index modulation (IM) technologies have proven to be potential technologies for improving the performance of the fifth generation (5G) and beyond 5G wireless communication systems. In this paper, a space-shift keying (SSK) (an IM scheme)-based receive diversity and Nakagami-m faded wireless system is considered, which utilizes a RIS system in between the transmitter and the receiver. A greedy detector is proposed for the system, which chooses a target receiver antenna based on the maximum received energy at the diversity branches. An analytical framework is proposed based on a characteristic function approach to obtain a closed-form expression of the probability of erroneous detection of the target antenna. Asymptotic expressions at high and low average signal-to-noise ratios lead to the observation of a performance saturation, attributed to the structure of the greedy detector. Furthermore, numerical results are presented to show the dependency of the system’s performance on the various system parameters.

This paper considers a receive diversity wireless communication system in which the transmitter uses a one-sided multi-level amplitude shift keying (ASK) modulation scheme to transmit data symbols over Rician fading channels. A channel magnitude-based receiver structure having partial knowledge of the instantaneous channel gains is employed to detect the received data symbols. The system’s error performance is analyzed by deriving a series-form expression for the symbol error probability (SEP). Further, an optimization framework is proposed to minimize the SEP under the average system energy constraint for obtaining the optimal ASK constellation. Numerical results are presented to corroborate the analytical framework where the superior performance of the optimal ASK constellation over the traditional equally-spaced ASK scheme is studied.

This study investigates the application of ensemble learning techniques for automatic modulation classification (AMC) in a reflective intelligent surface (RIS)-aided wireless communication system. The transmitter is considered to utilize five possible phase-shift keying and quadrature-amplitude modulation schemes for data transmission. The receiver extracts cumulant-based and spectral-based features from the received data, and employs three ensemble classifiers, namely XGBoost, LightGBM, and Random Forest for AMC. Furthermore, the important features for each classifier are identified, and their performance is computed and compared with the classifiers using all the features. Numerical results show that the LightGBM classifier performs the best in terms of AMC for the considered system and effectively classifies the modulation schemes at low signal-to-noise ratio values.

A severe natural disaster causes concurrent emergencies in separate geographical locations. However, tackling all the emergencies simultaneously with the limited available resources is challenging. A novel single-stage, non-cooperative multiplayer game-based solution approach for resource allocation is proposed in this paper, where the crisis locations in the demand of resources are treated as the players. The game-based decision support system (DSS) is intended to be implemented by the concerned disaster management authority to obtain a practical strategy for the allocation of indivisible and divisible resources among individual players in a limited resource environment. Any feasible allocation is associated with some cost on the basis of a non-monetary cost function. A discrete strategic game is formulated to tackle indivisible resource allocation and a continuous-kernel game for divisible resources. A mathematical analysis establishes that the game with the proposed cost function possesses at least one pure strategy Nash equilibrium (PSNE) for both types of games. Based on different selection criteria, a particular PSNE is chosen from the collection of multiple PSNEs and used as a desirable resource allocation strategy. A complexity analysis of the proposed algorithm is also carried out. Case studies are given in this paper to demonstrate the results developed. Note to Practitioners—The cost function proposed in this paper to formulate the non-cooperative game-based DSS will be useful to disaster management authorities in resource allocation problems after any natural disaster. While distributing a limited quantity of essential resources, the cost function maintains an overall balance so that none of the disaster locations is deprived or favoured unreasonably. The feasible allocations of the existing resources are determined by the solutions of a multiplayer game. The case study indicates that the disaster management authority can choose different post-disaster ground truths to compute the possible resource demands of the disaster locations. Using the demand vector (a vector comprising the demands of the players as elements) and the number of available resources as the inputs, the game-theoretic model generates the allocation vector, according to which the disaster locations can be allocated resources.

Human behavior plays a vital role in spreading contagious diseases, and understanding its influence on the outbreak could be the key to devising better mitigatory strategies. The varied individual behaviors give rise to different patterns in the evolution of the disease. Such trends in the disease outbreak that arise due to microscopic behaviors of heterogeneous agents in a populace are typical of agent-based models. Therefore, we propose an agent-based framework equipped with a population mixing algorithm, and stochastic disease transmission and evolutionary dynamics. The mixing algorithm incorporates different mobility trends in a synthetic populace to generate contact patterns among them. On the other hand, the disease model characterizes the health condition of agents using three crucial traits of the disease: 1) infection status; 2) severity; and 3) awareness, endowed with age-dependent probabilities for transmission, progress, and recovery of the disease. Together with different mobility patterns, we demonstrate the impact of mitigatory measures, such as social distancing, vaccination, testing, isolation, and containment zones, on the spread of disease. The proposed model is used to demonstrate trends in the COVID-19 outbreak rather than predict its course of evolution. The results indicate that enhanced mobility of agents facilitates contagion, and a combination of mitigation strategies needs to be adopted to combat the spread of the disease effectively.

A practical visible light communications (VLC) system accounting for the effect of the random orientation and the random location of the photo-detector receiver is considered in this paper. The characterizations of the random orientation of the receiver, modeled as the receiver employing wide field of view (FOV) for data detection, and the random location of the receiver are utilized to derive the statistics for the scenarios in which (i) only the orientation of the receiver is random, (ii) only the location of the receiver is random, and (iii) both the orientation and location of the receiver are random. Furthermore, the receiver structures for a fully coherent VLC system and a VLC system employing the least-squares technique for channel estimation are proposed. Employing the receiver structures, the closed/series-form expressions for the symbol error probabilities are derived for all three scenarios of practical VLC channel models. The numerical results particularly show the crucial role of the ratio of the distance of the location of the receiver from the center of the receiving plane to the distance from the light-emitting diode (LED) to the center of the receiving plane in the performance of all the considered VLC systems.

In this work, we mimic interactions among agents on social networks and examine their impact on the evolution of agents’ opinions. Each agent is entitled to an opinion represented by unit vectors of varying orientation. Agents are also characterized based on their inclination to alter their opinion and their susceptibility to the influence of peers with contrasting opinions. The agents constitute the nodes of a directed and time-varying influence network whose edges form their respective familiarity neighborhoods and govern their interactions. We also devise an interpersonal influence score to quantify the strength of the influence and the probability of acquisition, retention, or loss of a tie. This characterization of agents’ behavior and their interpersonal relationships differs from traditional bounded-confidence models based on the agreement in opinions alone. The agents with different traits form groups—liberal and conservative—distinguished by the distribution in opinions of their constituents. We analyze their interactions with different initial opinions, group compositions, network structures, and network evolution rates through simulations. The results reveal that flexible agents facilitate consensus among group members, while stubborn agents are instrumental in forming opinion clusters. This is substantiated by observing the average connectedness of the influence network and the average number of opinion clusters; groups with mostly flexible agents have better connectedness and fewer clusters than groups with stubborn agents in a majority. The simulations with stubborn agents reveal that new ties among agent pairs do not facilitate group consensus.

In wake-up radio (WuR) enabled Internet of things (IoT) networks, a data communication occurs in a synchronous or asynchronous manner initiated either by a transmitter or receiver. A synchronous transmission is triggered when multiple devices report an event simultaneously, or by a common wake-up call. In this paper, we focus on synchronous transmissions and propose a multicast triggered${s}$ynchronous${t}$ransmission protocol, abbreviated as MURIST, which enables contention based and coordinated data transmissions among distributed devices in order to reduce transmissions latency and energy consumption. Furthermore, we develop a novel analytical model based on an absorbing Markov chain to evaluate the performance of MURIST in a network cluster. Unlike existing models that are merely targeted at the behavior of a single device, the novelty of our model resides in a generic framework to assess the behavior of a cluster of devices for synchronous data transmissions. Based on the analytical model, we obtain closed-form expressions for the distributions of successful and discarded transmissions, number of collisions, and delay, as well as for energy consumption. Extensive simulations are performed to validate the accuracy of the analytical model and evaluate the performance of our scheme versus that of two other schemes.

Path planning and smooth trajectory generation are critical capabilities for efficient navigation of mobile robots operating in challenging and cluttered environments. For real time and autonomous operations of mobile robots, intelligent algorithms, efficient and light-weight compute, and smooth trajectory are key components. In this work, we propose an intelligent, probabilistic Gaussian mixture model driven Bi-RRT (PG-RRT) algorithm which generates nodes in the most probable regions for faster convergence. The proposed algorithm is tested in various simulated environments including highly cluttered obstacles. The experimental results of PG-RRT are compared with state-of-the-art path planning algorithms. The results show significant improvement in the number of iterations (up to 26X) and runtime (up to 17.5X) demonstrating the superiority of the proposed PG-RRT algorithm.

This work analyses significant cases in which applications of molecular communication systems to nano/bio-hybrid medical field represent an ideal solution for medical therapies (e.g., for the treatment of diseases such as cancer). A review of the literature reveals that biocompatibility jointly with nanocommunication can be exploited to provide effective treatment of diseases and reduce side effects considerably when compared to conventional therapy. Biocompatibility avoids the immune response rejecting drugs and does not stimulate nerves, whereas nanocommunication is a promising technology that allows accessing small and delicate body sites non-invasively.

Wireless sensor networks (WSNs) are one of the key enabling technologies for the Internet of things (IoT). In such networks, wake-up radio (WuR) is gaining its popularity thanks to its on-demand transmission feature and overwhelming energy consumption superiority. Despite this advantage, overhearing still occurs when a wake-up receiver decodes the address of a wake-up call (WuC) which is not intended to it, causing a certain amount of extra energy waste in the network. Moreover, long latency may occur due to WuC address decoding since WuCs are transmitted at a very low data rate. In this paper, we propose two schemes, i.e., early sleeping (ES) and early data transmission (EDT), to further reduce energy consumption and latency in WuR-enabled IoT/WSNs. The ES scheme decodes and validates an address bit-by-bit, allowing those non-destined devices go to sleep at an earlier stage. The EDT scheme enables a sender to transmit small IoT data together with WuC packets so that the main radio does not have to be in full operation for data reception. We implement both schemes through a WuR testbed. Furthermore, we present a framework based on M/G/1 and assess the performance of the schemes through both theoretical analysis and simulations.

Wake-up Radios (WuRs) are becoming more popular in Internet of Things (IoT) networks owing to their overwhelming advantages such as low latency, high energy saving, and on-demand communication. In a multi-hop WuR-IoT network, route establishment between source and destination prior to actual data communications consumes a significant portion of energy. To reduce energy consumption and protocol overhead for route establishment, we propose a lightweight relay (LR) selection scheme, referred to as LR-WuR, where a lookup table and acknowledgment are used for next hop relay selection. We develop an analytical model to evaluate the performance of LR-WuR. Extensive simulations are performed to validate the accuracy of the analytical model. Furthermore, we present a framework for deriving optimal network parameters.

Wake-up Radio (WuR)-enabled wireless personal area networks (WPANs) are more popular over conventional WPANs thanks to WuR's on-demand transmission feature and overwhelming energy consumption superiority. In a WuRenabled WPAN, overhearing occurs when a wake-up receiver decodes and validates the address of a wake-up call which is not intended to it. However, such overhearing consumes a portion of the required reception energy for unintended nodes. To diminish overhearing thus conserve total reception energy in a network, we propose a bit-by-bit address decoding (BBAD) scheme and compare it with another addressing scheme for WuR that uses a micro-controller unit to decode and match the whole sequence of an address. Instead of decoding and validating a complete address at a time, BBAD decodes and matches an address in a bit- by-bit manner. We implement both address decoding schemes through a WuR testbed. Furthermore, we develop a framework based on M/G/1 and assess the performance of the WuR-enabled WPANs through both analysis and simulations.

In wake-up radio (WuR) enabled wireless sensor networks (WSNs), the envisaged application scenarios are primarily targeted at low traffic load conditions. When applying WuR to medium or heavy traffic load scenarios, however, collisions among wake-up calls (WuCs) may happen, resulting in a lower packet delivery ratio (PDR). In this paper, we propose a media access control protocol for WuR- enabled WSN that is capable of avoiding WuC collisions by activating a contention-based collision avoidance mechanism for WuC transmissions. The performance of the proposed protocol is evaluated by a Markov chain based mathematical model and is compared with a WuR protocol that performs only clear channel assessment for WuC transmissions. Numerical results demonstrate that our proposed protocol achieves better performance in terms of both PDR and network throughput.

In wake-up radio (WuR) enabled wireless sensor networks (WSNs), a node triggers a data communication at any time instant by sending a wake-up call (WuC) in an on-demand manner. Such wake-up operations eliminate idle listening and overhearing burden for energy consumption in duty-cycled WSNs. Although WuR exhibits its superiority for light traffic, it is inefficient to handle high traffic load in a network. This paper makes an effort towards improving the performance of WuR under diverse load conditions with a twofold contribution. We first propose three protocols that support variable traffic loads by enabling respectively clear channel assessment (CCA), backoff plus CCA, and adaptive WuC transmissions. These protocols provide various options for achieving reliable data transmission, low latency, and energy efficiency for ultralow power consumption applications. Then, we develop an analytical framework based on an M/G/1/2 queue to evaluate the performance of these WuR protocols. Discrete-event simulations validate the accuracy of the analytical models.

Many recent studies anticipate that wake-up radio (WuR) will replace traditional duty-cycled (DC) protocols given its overwhelming performance superiority on energy consumption. Meanwhile, the question on whether WuR performs always better than DC protocols has not been answered explicitly. In this paper, we investigate in-depth the energy consumption performance of WuR by considering various levels of traffic load in a wireless sensor network. By comparing SCM-WuR with both synchronous MAC (S-MAC) and asynchronous MAC (X-MAC), we ascertain that SCM-WuR does consume orders of magnitude lowerenergythanDCprotocolswhentrafficloadislow.Howe ver, our numerical results reveal at the same time that SCM-WuR does not have an absolute advantage when traffic load is heavy or saturated, especially when long range wake-up call is targeted.

To ensure quality of service (QoS) for heterogeneous traffic in IEEE 802.15.4 based wireless sensor networks (WSNs) is a challenging task since no traffic prioritization mechanism is defined in such WSNs. In this paper, we propose two priority-oriented multicast transmission schemes to provide QoS for heterogeneous traffic in WSNs. Contrary to the legacy CSMA/CA and FIFO principles, these schemes differentiate self-generated or received traffic and give priority to delay-sensitive traffic with respect to channel access and packet scheduling. Simulations are performed in order to assess the performance of these schemes in terms of end-to-end delay, energy consumption, packet delivery ratio, and collision probability.

To ensure end-to-end reliable multicast or broadcast transmissions in IEEE 802.15.4 based wireless sensor networks WSNs) is a challenging task since no retransmission and acknowledgment mechanisms are defined in such WSNs. In this paper, we propose three retransmission enabled multicast transmission schemes in order to achieve reliable packet transmissions in such networks. Different from the legacy CSMA/CA principle, these schemes allow a sending or forwarding node to retransmit a packet if necessary and enable implicit or/and explicit acknowledgment for multicast services. Simulations are performed in order to assess the performance of these schemes in terms of number of retransmissions, packet loss, energy consumption, collision probability and end-to-end delay.

This paper addresses the problem of automated multiagent search in an unknown environment. Autonomous agents equipped with sensors carry out a search operation in a search space, where the uncertainty, or lack of information about the environment, is known a priori as an uncertainty density distribution function. The agents are deployed in the search space to maximize single step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for the proposed sequential deploy and search strategy. It is shown that with the proposed control law the agent trajectories converge in a globally asymptotic manner to the centroidal Voronoi configuration. Simulation experiments are provided to validate the strategy.

This paper addresses a search problem with multiple limited capability search agents in a partially connected dynamical networked environment under different information structures. A self assessment-based decision-making scheme for multiple agents is proposed that uses a modified negotiation scheme with low communication overheads. The scheme has attractive features of fast decision-making and scalability to large number of agents without increasing the complexity of the algorithm. Two models of the self assessment schemes are developed to study the effect of increase in information exchange during decision-making. Some analytical results on the maximum number of self assessment cycles, effect of increasing communication range, completeness of the algorithm, lower bound and upper bound on the search time are also obtained. The performance of the various self assessment schemes in terms of total uncertainty reduction in the search region, using different information structures is studied. It is shown that the communication requirement for self assessment scheme is almost half of the negotiation schemes and its performance is close to the optimal solution. Comparisons with different sequential search schemes are also carried out.

The problem of collision prediction in dynamic environments appears in several diverse fields, which include robotics, air vehicles, underwater vehicles, and computer animation. In this paper, collision prediction of objects that move in 3-D environments is considered. Most work on collision prediction assumes objects to be modeled as spheres. However, there are many instances of object shapes where an ellipsoidal or a hyperboloid-like bounding box would be more appropriate. In this paper, a collision cone approach is used to determine collision between objects whose shapes can be modeled by general quadric surfaces. Exact collision conditions for such quadric surfaces are obtained in the form of analytical expressions in the relative velocity space. For objects of arbitrary shapes, exact representations of planar sections of the 3-D collision cone are obtained.

We propose a combined deploy and search strategy for multi-agent systems using Voronoi partition. Agents such as mobile robots (AGVs, UAVs, or USVs) search the space to acquire knowledge about the space. Lack of information about the search space is modeled as an uncertainty density distribution, which is known a priori to all the agents at the beginning of search. It is shown that when the agents are located at the centroid of Voronoi cells, computed with the perceived uncertainty density, reduction in uncertainty density is maximized. While moving toward this optimal configuration, the agents simultaneously perform search acquiring the information about the search space, thereby reducing the uncertainty density. The proposed search strategy is guaranteed to reduce the average uncertainty density to any arbitrary level. Simulation experiments are carried out to validate the proposed search strategy and compare its performance with sequential deploy and search strategy proposed in the literature. The simulation results indicate that the proposed strategy performs better than sequential deploy and search in terms of faster search, and smoother and shorter robot trajectories.

This paper presents a path planning approach for achieving an optimal feasible path satisfying a maximum curvature bound in three dimensional space, given initial and final configurations specified by position and orientation vectors. Based on Dubins strategy two types of solution approaches will be discussed, the first is a numerical technique which is computationally intensive and the second is based on 3D geometry from which we will derive an analytical solution. In the second approach, the computational time is very low and the strategy can be implemented for real-time path planning problems. Unlike the existing iterative methods which yield suboptimal paths and are computationally more intensive, this geometrical method generates an optimal path in lesser time. Due to its simplicity and low computational requirements this approach can be implemented on fixed wing aerial vehicles with constrained turn radius.

We consider the problem of goal seeking by robots in unknown environments. We present a frontier based algorithm for finding a route to a goal in a fully unknown environment, where information about the goal region (GR), the region where the goal is most likely to be located, is available. Our algorithm efficiently chooses the best candidate frontier cell, which is on the boundary between explored space and unexplored space, having the maximum “goal seeking index”, to reach the goal in minimal number of moves. Modification of the algorithm is also proposed to further reduce the number of moves toward the goal. The algorithm has been tested extensively in simulation runs and results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves in bounded as well as unbounded environments. The algorithm is shown to perform as well as a state of the art agent centered search algorithm RTAA*, in cluttered environments if exact location of the goal is known at the beginning of the mission and is shown to perform better in uncluttered environments.

In this paper, we propose load distribution strategies for divisible loads for networked computing environments where computation and communication resource characteristics are unknown and/or vary with time. The principle on which our strategies are formulated is based on using probing loads to estimate the network characteristics and using them to determine the best possible load distribution. This work extends the adaptive strategies proposed in an earlier work to multilevel general networks. These networks, while being more challenging, also offer several opportunities that can be exploited to make the probing phase more efficient. We propose two strategies, one static, which caters to the presence of unknown parameters, and the other dynamic, which caters to both unknown as well as time-varying network parameters. The proposed strategies are robust, resilient, and easily adaptable to network fluctuations. The algorithms are also shown to have a tracking ability, a property that is important in dynamic environments. Examples are presented to illustrate the salient features of these strategies.

This paper presents a glowworm metaphor based distributed algorithm that enables a collection of minimalist mobile robots to split into subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple radiation sources such as nuclear/hazardous chemical spills and fire-origins in a fire calamity. The algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the glowworm algorithm carry a luminescence quantity called luciferin along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luciferin. The key feature that is responsible for the working of the algorithm is the use of an adaptive local-decision domain, which we use effectively to detect the multiple source locations of interest. The glowworms have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. Extensive simulations validate the feasibility of applying the glowworm algorithm to the problem of multiple source localization. We build four wheeled robots called glowworms to conduct our experiments. We use a preliminary experiment to demonstrate the basic behavioral primitives that enable each glowworm to exhibit taxis behavior towards source locations and later demonstrate a sound localization task using a set of four glowworms

This paper presents a glowworm swarm based algorithm that finds solutions to optimization of multiple optima continuous functions. The algorithm is a variant of a well-known ant-colony optimization (ACO) technique, but with several significant modifications. Similar to how each moving region in the ACO technique is associated with a pheromone value, the agents in our algorithm carry a luminescence quantity along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luminescence and have a circular sensor range. The glowworms depend on a local-decision domain to compute their movements. Simulations demonstrate the efficacy of the proposed glowworm based algorithm in capturing multiple optima of a multimodal function. The above optimization scenario solves problems where a collection of autonomous robots is used to form a mobile sensor network. In particular, we address the problem of detecting multiple sources of a general nutrient profile that is distributed spatially on a two dimensional workspace using multiple robots.

Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values using a probing technique and use them to obtain optimal load partitioning. Three algorithms, based on the same strategy, are presented in the paper, incorporating the ability to cope with unknown network parameters. Several illustrative numerical examples are given. Finally, we implement the adaptive algorithms on an actual network of processor nodes using MPI implementation and demonstrate the feasibility of the adaptive approach.

In this paper, we address the modeling and analysis issues associated with a generic theater level campaign where two adversaries pit their military resources against each other over a sequence of multiple engagements. In particular, we consider the scenario of an air raid campaign where one adversary uses suppression of enemy air defense (SEAD) aircraft and bombers (BMBs) against the other adversary's invading ground troops (GTs) that are defended by their mobile air defense (AD) units. The original problem is decomposed into a temporal and a spatial resource allocation problem. The temporal resource allocation problem is formulated and solved in a game-theoretical framework as a multiple resource interaction problem with linear attrition functions. The spatial resource allocation problem is posed as a risk minimization problem in which the optimal corridor of ingress and optimal movement of the GTs and AD units are decided by the adversaries. These two solutions are integrated using an aggregation/deaggregation approach to evaluate resource strengths and distribute losses. Several simulation experiments were carried out to demonstrate the main ideas.

A novel collision cone approach is proposed as an aid to collision detection and avoidance between irregularly shaped moving objects with unknown trajectories. It is shown that the collision cone can be effectively used to determine whether collision between a robot and an obstacle (both moving in a dynamic environment) is imminent. No restrictions are placed on the shapes of either the robot or the obstacle, i.e., they can both be of any arbitrary shape. The collision cone concept is developed in a phased manner starting from existing analytical results that enable prediction of collision between two moving point objects. These results are extended to predict collision between a point and a circular object, between a point and an irregularly shaped object, between two circular objects, and finally between two irregularly shaped objects. Using the collision cone approach, several strategies that the robot can follow in order to avoid collision, are presented. A discussion on how the shapes of the robot and obstacles can be approximated in order to reduce computational burden is also presented. A number of examples are given to illustrate both collision prediction and avoidance strategies of the robot.

The problem of obtaining optimal processing time in a distributed computing system consisting of (N+1) processors and N communication links, arranged in a single-level tree architecture, is considered. It is shown that optimality can be achieved through a hierarchy of steps involving optimal load distribution, load sequencing, and processor-link arrangement. Closed-form expressions for optimal processing time is derived for a general case of networks with different processor speeds and different communication link speeds. Using these closed-form expressions, the paper analytically proves a number of significant results that in earlier studies were only conjectured from computational results. In addition, it also extends these results to a more general framework. The above analysis is carried out for the cases in which the root processor may or may not be equipped with a front-end processor. Illustrative examples are given for all cases considered.>