Autonomous Search Research Articles

Managers' search practices at the front end of radical manufacturing technology innovations

Managers of manufacturing firms have important tasks in choosing novel technology solutions for the firm's production process. The emergence of ideas for radical manufacturing technology innovations and managers' proactive search for radical ideas and concepts for developing production processes have not been well understood. This study concentrates on managers' search practices at the front end of radical manufacturing technology innovations. We analyzed managers' practices in the early phase of nine radical manufacturing technology innovation projects across three firms. Radical manufacturing technology innovations require acknowledging both process innovations for the manufacturer and product innovations for the equipment supplier. The findings of this study revealed alternative patterns regarding the use of directed and autonomous search processes, internal and external information sources and open and closed supplier searches. This study offers new knowledge on the nature of the information processing task that managers face and on the search practices that managers use at the front end of radical manufacturing technology innovations. The study contributes by differentiating the managers' search practices based on the specific innovation scope in terms of the technology, equipment and production concept. Propositions are offered concerning the drivers and use of managers' search practices at the front end of radical manufacturing technology innovations.

Autonomous search of an airborne release in urban environments using informed tree planning

The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

A Spiking Neural Network for Autonomous Search and Contour Tracking Inspired by C. elegans Chemotaxis and the Lévy Walk

A Spiking Neural Network for Autonomous Search and Contour Tracking Inspired by C. elegans Chemotaxis and the Lévy Walk

An Intelligent Robot Detection System of Uncontrolled Radioactive Sources.

In recent years, radioactive sources have been widely used in various fields (e.g., nuclear industry, agriculture, medical industry, environmental protection, and scientific research) and successfully applied to develop scientific projects, such as nuclear power generation, sewage treatment, medical diagnosis, and new material development. However, radiation sources continuously got out of control and even lost. Manual search for uncontrolled radiation sources is inefficient and prone to radiation injuries. Therefore, it is practically significant to design a radiation source detection robot. Against this backdrop, this study designs an intelligent robot detection system of uncontrolled radiation sources and develops an intelligent robot for detecting and disposing of uncontrolled radiation sources. The research results help to realize the autonomous search and disposal of radiation sources.

Review of UAV-based autonomous search algorithms for hazardous sources

Autonomous search plays an important role in a wide range of applications, such as environmental protection, industrial monitoring, and search and rescue. Sensor platforms based on unmanned aerial vehicles (UAVs) have been regarded as one of the most efficient methods for achieving fully autonomous searching because of their superior flexibility and motility. This paper reviews existing algorithms for two important subjects, i.e., source seeking and boundary tracking, based on which their design principles are revealed. Although control-oriented and information-driven approaches are the two mainstream state-of-the-art methods for source seeking, their hybrids have been poorly studied. Regarding boundary tracking, existing solutions can be allocated into two classes: single-agent tracking and multiagent cooperative tracking. From the perspective of dual control for exploration and exploitation, this review outlines the fundamental insights into algorithm design and presents several visions for future research.

A proposal for improving production efficiency of existing machining line through a hybrid monitoring and optimisation process

In a machining line with a Lean manufacturing environment, such as one used for mass production of automotive components, tool life is determined by a tool counter that is set on the safe side to avoid producing a failed product, so the cutting tools are typically underutilised. On the other hand, cutting parameters optimisation, which has a significant impact on tool life, is not feasible due to the rule that prohibits changing the cutting parameters during mass production to maintain the quality standard. To address both issues, this paper proposes a hybrid monitoring and optimisation process that can be carried out concurrently without interfering with production activities. The monitoring process involves two methods, namely threshold limit-based monitoring and machine learning-based monitoring, for ensuring product quality during the optimisation process. Meanwhile, Bayesian optimisation (BO) is used for the optimisation process due to its capability for autonomous search for the optimum condition of an unknown and expensive objective function. The case study demonstrated BO’s ability to find the optimal condition with a small number of samples, while the machine learning method used can monitor tool wear and surface roughness with average errors of 5.5% and 7.7%, respectively.

Finding a novel electrolyte solution of lithium-ion batteries using an autonomous search system based on ensemble optimization

For an autonomous search for electrolyte solutions in electrochemistry, we performed a search task using a parallel Bayesian optimization method with the best composition with maximum lithium (Li) ion conductivity in a mixture of four lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) solutions: propylene carbonate (PC), γ-butyrolactone (GBL), tetraethylene glycol diethyl ether (TGDE), and trimethyl phosphate (TMP). The maximum ionic conductivity was achieved using a mixed solution of 98 vol% GBL and 2 vol% TGDE and showed a high Li ionic conductivity in manual measurements. Furthermore, molecular dynamics simulations of a similar electrolyte implied that the small solvation shell of Li-TGDE caused an increase in the ionic conductivity. The reason for this increase is that the dilute TGDE molecules in the GBL solution can be associated with dilute Li ions.

Autonomous Search for Polymers with High Thermal Conductivity Using a Rapid Green–Kubo Estimation

Autonomous Search for Polymers with High Thermal Conductivity Using a Rapid Green–Kubo Estimation

Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments

We tackle the problem of planning a minimum-time trajectory for a quadrotor over a sequence of specified waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. This problem is crucial for autonomous search and rescue and drone racing scenarios but was, so far, unaddressed by the robotics community <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in its entirety</i> due to the challenges of minimizing time in the presence of the non-convex constraints posed by collision avoidance. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of a quadrotor and, thus, did not aim at minimizing time. We address this challenging problem by using a hierarchical, sampling-based method with an incrementally more complex quadrotor model. Our method first finds paths in different topologies to guide subsequent trajectory search for a kinodynamic point-mass model. Then, it uses an asymptotically-optimal, kinodynamic sampling-based method based on a full quadrotor model on top of the point-mass solution to find a feasible trajectory with a time-optimal objective. The proposed method is shown to outperform all related baselines in cluttered environments and is further validated in real-world flights at over 60 km/h in one of the world’s largest motion capture systems. We release the code open source.

Crystal morphing: Structural interpolation including crystal invariances

Material data are used to accelerate the development of materials. For the crystalline solid domain, however, informatics schemes are challenged by the encoding of crystal structures because embedding the invariances with respect to translation, rotation, and unit-cell choice is intractable by data augmentation schemes. We propose an efficient search space in which identical structures are reduced given such crystal operators. This crystal-invariant search space is created by morphing between the topologies of known crystal structures in accordance with the gradient of a smooth overlap of atomic positions descriptor formulated in reciprocal space as a metric. This crystal morphing is examined using an identification task of the structures from an x-ray diffraction spectrum. By applying Bayesian optimization on the search space, good initial structures for the Rietveld analysis are achieved via automatic determination without expert operations. Our method is applicable in the autonomous search of crystals, various structural analyses, and design of metamaterials.

Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty.

Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.

Acceleration-Aware Path Planning with Waypoints

In this article we demonstrate that acceleration and deceleration of direction-turning drones at waypoints have a significant influence to path planning which is important to be considered for time-critical applications, such as drone-supported search and rescue. We present a new path planning approach that takes acceleration and deceleration into account. It follows a local gradient ascend strategy which locally minimizes turns while maximizing search probability accumulation. Our approach outperforms classic coverage-based path planning algorithms, such as spiral- and grid-search, as well as potential field methods that consider search probability distributions. We apply this method in the context of autonomous search and rescue drones and in combination with a novel synthetic aperture imaging technique, called Airborne Optical Sectioning (AOS), which removes occlusion of vegetation and forest in real-time.

Dual Control for Exploitation and Exploration (DCEE) in autonomous search

This paper proposes an optimal autonomous search framework, namely Dual Control for Exploration and Exploitation (DCEE), for a target at an unknown location in an unknown environment. Source localisation is to find sources of atmospheric hazardous material release in an unknown environment. This paper proposes a control theoretic approach to this autonomous search problem. To cope with an unknown target location, at each step, the target location is estimated by Bayesian inference. Then a control action is taken to minimise the error between future robot position and the predicted future estimation of the target location. The latter is generated by hypothesised measurements at the corresponding future robot positions (due to the control action) with the current estimation of the target location as a prior. It shows that DCEE can take into account both the error between the next robot position and the estimated target location, and the uncertainty of the estimate. This approach is further extended to deal with both an unknown source location and unknown local environment (e.g. wind speed and direction). Different from current information theoretic approaches, this new control theoretic approach achieves the optimal trade-off between exploitation and exploration in an unknown environment with an unknown target by driving the robot moving towards estimated target location while reducing its estimation uncertainty. Simulation and experimental studies demonstrate promising performance of the proposed approach. The relationships between the proposed approach, informative path planning, dual control, and classic model predictive control are discussed and compared. This work opens a door for developing control systems operating in unknown environments, or performing tasks with unknown parameters.

Effectiveness of a Camera as a UAV Mounted Search Sensor for Target Detection: An Experimental Investigation

In this paper, we consider a problem of autonomous search using single or multiple Unmanned Ariel Vehicles (UAVs) mounted with downward-facing cameras. A model of the effectiveness of the search sensor, camera, in this case, is essential for developing strategies for optimal deployment and path planning of UAVs for efficient search. The probability of detection of a target of interest as a function of its distance from the point directly below the camera is used to model the search effectiveness. We carried out experiments and obtained a search effectiveness model for a camera in the laboratory environment using ArUco markers and triangular shapes as targets.

Individual and collective foraging in autonomous search agents with human intervention

Humans and other complex organisms exhibit intelligent behaviors as individual agents and as groups of coordinated agents. They can switch between independent and collective modes of behavior, and flexible switching can be advantageous for adapting to ongoing changes in conditions. In the present study, we investigated the flexibility between independent and collective modes of behavior in a simulated social foraging task designed to benefit from both modes: distancing among ten foraging agents promoted faster detection of resources, whereas flocking promoted faster consumption. There was a tradeoff between faster detection versus faster consumption, but both factors contributed to foraging success. Results showed that group foraging performance among simulated agents was enhanced by loose coupling that balanced distancing and flocking among agents and enabled them to fluidly switch among a variety of groupings. We also examined the effects of more sophisticated cognitive capacities by studying how human players improve performance when they control one of the search agents. Results showed that human intervention further enhanced group performance with loosely coupled agents, and human foragers performed better when coordinating with loosely coupled agents. Humans players adapted their balance of independent versus collective search modes in response to the dynamics of simulated agents, thereby demonstrating the importance of adaptive flexibility in social foraging.

Collective Gas Sensing in a Cyber-Physical System

This paper discusses a novel collective sensing approach using autonomous sensors specially designed to monitor gas leaks and search for gas sources. The proposed collective behavior aims to improve the gas-source search by sharing information between mobile sensors and reducing the risks associated with gas leakage. The group acts as a composite sensor that can move independently to search for an optimal sensing zone. The autonomous searching behavior is bio-inspired by colonies of bacteria that continuously seek energy sources throughout their existence. Each sensor makes its own autonomous search decision, considering the group sense, to move in the direction of a better energy source. The collective approach is based on autonomous agents sharing information to achieve a collective sense of gas perception and utilizes more intelligent searching. The method is evaluated in a cyber-physical system specially developed to safely experiment with gases and mobile sensors while reproducing the realistic dynamic behavior of the gas. Experiments are performed to clarify the collective gas-sensing contributions, and the gas search is compared through multiple mobile sensors with and without collective sensing. The proposed approach is evaluated in an unhealthy environment to elucidate its effectiveness. In addition to presenting the related differences between collective and individual sensory approaches, this work contributes with analyzes of the scalability of mobile gas sensing systems. This work also contributed as a simulated semi-physical experimental system to test algorithms' performance before applying it to practice.

Digital navigation of energy\u2013structure\u2013function maps for hydrogen-bonded porous molecular crystals

Energy–structure–function (ESF) maps can aid the targeted discovery of porous molecular crystals by predicting the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of rigid molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. We show that the positioning of the hydrogen-bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps, revealing promising structure–function spaces for future experiments. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map, enabling an efficient navigation of the ESF data to identify ‘landmark’ structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, an important goal for closed-loop, autonomous searches for molecular crystals with useful functions.

Development of an Autonomous Search System for Process Optimization of Laser Full-Cutting of Glass

Development of an Autonomous Search System for Process Optimization of Laser Full-Cutting of Glass

Geomagnetic Gradient-Assisted Evolutionary Algorithm for Long-Range Underwater Navigation

Extensive research results have shown that animals like pigeons and turtles can use geomagnetic information for long-distance migration and homing. This article studies the bionic navigation method inspired by magnetotaxis behavior without prior knowledge. The problem of bionic geomagnetic navigation is generalized as an autonomous search of navigation path under the excitation of geomagnetic environment. The geomagnetic gradient-assisted evolutionary algorithm for long-range underwater navigation is proposed. In order to optimize the navigation path, the heading angle predicted by the geomagnetic gradient is used to constrain the sample space in the evolutionary algorithm. Then, according to the principle of multiparameter simultaneous convergence, the evaluation function is improved to enhance the reliability and accuracy of the navigation path. Simulations of the algorithm before and after improvement are carried out based on the data retrieved from the enhanced magnetic model (EMM). The performance of the improved method is evaluated and verified in the case of the area with normal geomagnetic field (GF), geomagnetic anomaly area, and multiple destinations. The simulation results show that the search efficiency and the straightness of the navigation path are greatly improved. The reason is that the constraint of sample space reduces the randomness in the process of navigation path search, and the improved evaluation function can evaluate the quality of samples more accurately. The improved algorithm also has good performance in the geomagnetic anomaly area, which indicates the potential application in the future.