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Letteratura scientifica selezionata sul tema "Autonomous search"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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Articoli di riviste sul tema "Autonomous search"

Gelenbe, Erol, and Yonghuan Cao. "Autonomous search for mines." European Journal of Operational Research 108, no. 2 (July 1998): 319–33. http://dx.doi.org/10.1016/s0377-2217(97)00373-1.

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Huo, Jianwen, Manlu Liu, Konstantin A. Neusypin, Haojie Liu, Mingming Guo, and Yufeng Xiao. "Autonomous Search of Radioactive Sources through Mobile Robots." Sensors 20, no. 12 (June 19, 2020): 3461. http://dx.doi.org/10.3390/s20123461.

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The research of robotic autonomous radioactivity detection or radioactive source search plays an important role in the monitoring and disposal of nuclear safety and biological safety. In this paper, a method for autonomously searching for radioactive sources through mobile robots was proposed. In the method, by using a partially observable Markov decision process (POMDP), the search of autonomous unknown radioactive sources was realized according to a series of radiation information measured by mobile robot. First, the factors affecting the accuracy of radiation measurement during the robot’s movement were analyzed. Based on these factors, the behavior set of POMDP was designed. Secondly, the parameters of the radioactive source were estimated in the Bayesian framework. In addition, through the reward strategy, autonomous navigation of the robot to the position of the radiation source was achieved. The search algorithm was simulated and tested, and the TurtleBot robot platform was used to conduct a real search experiment on the radio source Cs-137 with an activity of 37 MBq indoors. The experimental results showed the effectiveness of the method. Additionally, from the experiments, it could been seen that the robot was affected by the linear velocity, angular velocity, positioning accuracy and the number of measurements in the process of autonomous search for the radioactive source. The proposed mobile robot autonomous search method can be applied to the search for lost radioactive sources, as well as for the leakage of substances (nuclear or chemical) in nuclear power plants and chemical plants.

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Mansor, Hasmah, Muhamad Haziq Norhisam, Zulkifli Zainal Abidin, and Teddy Surya Gunawan. "Autonomous surface vessel for search and rescue operation." Bulletin of Electrical Engineering and Informatics 10, no. 3 (June 1, 2021): 1701–8. http://dx.doi.org/10.11591/eei.v10i3.2599.

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Search and rescue operation is performed to save human life, for example during natural disasters, unfortunate incidents on the land, in the deepwater, or lakes. There were incidents happened to the search and rescue crew during the operation although they were well trained. A new method using robotic technology is important to reduce the crew's risk during operations. This research proposed a development of an autonomous surface vessel for search and rescue operations for deepwater applications. The proposed autonomous surface vessel is equipped with a global positioning system (GPS) and underwater sensor to search for the victims, black box, debris, or other evidence on the surface and underwater. The vessel was designed with monitoring and control via radio frequency wireless communication. The autonomous surface vessel prototype was developed and tested successfully with the telemetry at the ground station. The ground station acts as the control centre of the overall system. Results showed the vessel successfully operated autonomously. The operator at the ground station was able to monitor the sensor data and control the vessel's manoeuvre according to the created path. The telemetry coverage to monitor the water surroundings and control the vessel's manoeuvre was around 100 meters.

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Harel, David, Assaf Marron, and Joseph Sifakis. "Autonomics: In search of a foundation for next-generation autonomous systems." Proceedings of the National Academy of Sciences 117, no. 30 (July 21, 2020): 17491–98. http://dx.doi.org/10.1073/pnas.2003162117.

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The potential benefits of autonomous systems are obvious. However, there are still major issues to be dealt with before developing such systems becomes a commonplace engineering practice, with accepted and trustworthy deliverables. We argue that a solid, evolving, publicly available, community-controlled foundation for developing next-generation autonomous systems is a must, and term the desired foundation “autonomics.” We focus on three main challenges: 1) how to specify autonomous system behavior in the face of unpredictability; 2) how to carry out faithful analysis of system behavior with respect to rich environments that include humans, physical artifacts, and other systems; and 3) how to build such systems by combining executable modeling techniques from software engineering with artificial intelligence and machine learning.

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SYAFITRI, NIKEN, RATNA SUSANA, IHSAN FARRASSALAM AMMARPRAWIRA, MOCHAMAD SEPTONI FAUZI, and ARBI ABDUL JABBAAR. "The Autonomous Disaster Victim Search Robot using the Waypoint Method." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 2 (May 19, 2020): 347. http://dx.doi.org/10.26760/elkomika.v8i2.347.

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ABSTRAKPada kasus bencana alam, penyebaran robot pada area bencana sangat bermanfaat bagi tim SAR dalam proses pencarian korban. Khususnya, jika robot yang disebar merupakan robot otonom untuk meningkatkan proses pencarian, secara efektif dan efisien. Pada artikel ini, kami mengusulkan sebuah robot otonom untuk pencarian korban bencana yang ditanamkan metode waypoint dengan mengeksploitasi sensor, GPS dan divais komunikasi. Hasil yang didapat menunjukkan bahwa robot ini mampu mencari korban bencana secara otonom dengan mendeteksi pergerakan kecil dan temperatur korban. Kemudian, koordinat lokasi korban dikirim ke tim SAR untuk operasi penyelamatan. Robot ini juga mampu melakukan perjalanan pulang ke posko asal untuk pengisian daya ketika daya baterainya hampir habis.Kata kunci: robot otonom, waypoint, bencana, pencarian korban, perjalanan pulang ABSTRACTIn the case of disaster, the deployment of robots is immensely helpful for SAR team in the process of searching the victims. Especially, if the deployed robots are autonomous to enhance the searching process, effectively and efficiently. In this paper, we propose an autonomous robot for searching the disaster victims embedded by automatic waypoint method by exploiting sensors, GPS and communication devices. Result shows that this robot is able to search the victims autonomously by detecting their slight movement and temperature then sending the coordinates to the SAR team for the rescue operation. This robot is also able to do the return trip autonomously to the initial post for charging when its power is almost running out.Keywords: autonomous robot, waypoint, disaster, victim searching, return trip

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Piacentini, Chiara, Sara Bernardini, and J. Christopher Beck. "Autonomous Target Search with Multiple Coordinated UAVs." Journal of Artificial Intelligence Research 65 (August 8, 2019): 519–68. http://dx.doi.org/10.1613/jair.1.11635.

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Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others.

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Dollarhide, Robert L., Arvin Agah, and Gary J. Minden. "Evolving controllers for autonomous robot search teams." Artificial Life and Robotics 5, no. 3 (September 2001): 178–88. http://dx.doi.org/10.1007/bf02481466.

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Soto, Ricardo, Broderick Crawford, Wenceslao Palma, Karin Galleguillos, Carlos Castro, Eric Monfroy, Franklin Johnson, and Fernando Paredes. "Boosting autonomous search for CSPs via skylines." Information Sciences 308 (July 2015): 38–48. http://dx.doi.org/10.1016/j.ins.2015.01.035.

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de Cubber, Geert, Haris Balta, and Claude Lietart. "Teodor: A Semi-Autonomous Search and Rescue and Demining Robot." Applied Mechanics and Materials 658 (October 2014): 599–605. http://dx.doi.org/10.4028/www.scientific.net/amm.658.599.

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In this paper, we present a ground robotic system which is developed to deal with rough outdoor conditions. The platform is to be used as an environmental monitoring robot for 2 main application areas: - Humanitarian demining: The vehicle is equipped with a specialized multi-channel metal detector array. An unmanned aerial system supports it for locating suspected locations of mines, which can then be confirmed by the ground vehicle. - Search and rescue: The vehicle is equipped with human victim detection sensors and a 3D camera enabling it to assess the traversability of the terrain in front of the robot in order to be able to navigate autonomously. The paper discusses both the mechanical design of these platforms as the autonomous perception capabilities on board of these vehicles.

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Liu, Yuan, Min Wang, Zhou Su, Jun Luo, Shaorong Xie, Yan Peng, Huayan Pu, Jiajia Xie, and Rui Zhou. "Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm." Journal of Marine Science and Engineering 8, no. 11 (October 26, 2020): 843. http://dx.doi.org/10.3390/jmse8110843.

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As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

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Tesi sul tema "Autonomous search"

Peterson, John Ryan. "Autonomous Source Localization." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97954.

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This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments. Doctor of Philosophy This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.

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Cavallin, Kristoffer, and Peter Svensson. "Semi-Autonomous,Teleoperated Search and Rescue Robot." Thesis, Umeå University, Department of Computing Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-31928.

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The interest in robots in the urban search and rescue (USAR) field has increased the last two decades. The idea is to let robots move into places where human rescue workers cannot or, due to high personal risks, should not enter.In this thesis project, an application is constructed with the purpose of teleoperating a simple robot. This application contains a user interface that utilizes both autonomous and semi-autonomous functions, such as search, explore and point-and-go behaviours. The purpose of the application is to work with USAR principles in a refined and simplified environment, and thereby increase the understanding for these principles and how they interact with each other. Furthermore, the thesis project reviews the recent and the current status of robots in USAR applications and use of teleoperation and semi-autonomous robots in general. Some conclusions that are drawn towards the end of the thesis are that the use of robots, especially in USAR situations, will continue to increase. As robots and support technology both become more advanced and cheaper by the day, teleoperation and semi-autonomous robots will also be seen in more and more places.

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Earnest, Caleb A. "Dynamic action spaces for autonomous search operations." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/46549.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005. Includes bibliographical references (p. 148-150). This thesis presents a new approach for a Navy unmanned undersea vehicle (UUV) to search for and detect an evading contact. This approach uses a contact position distribution from a generic particle filter to estimate the state of a single moving contact and to plan the path that minimizes the uncertainty in the location of the contact. The search algorithms introduced in this thesis will implement a motion planner that searches for a contact with the following information available to the decision system: (1) null measurement (i.e., contact not detected at current time), (2) timedated measurement (i.e., clue found at current time that indicates contact was at this location in the past), and (3) bearings measurement (i.e., angular measurement towards contact position detected at current time). The results of this thesis will be arrived at by evaluating the best methods to utilize the three types of information. The underlying distribution of the contact state space will be modeled using a generic particle filter, due to the highly non-Gaussian distributions that result from the conditions mentioned above. Using the particle filter distribution and the measurements acquired from the three conditions, this thesis will work towards implementing a path planning algorithm that creates dynamic action spaces that evaluate the uncertainty of position distribution. Ultimately, the path planner will choose the path that contains the position distribution and leads to sustained searches. by Caleb A. Earnest. S.M.

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Beck, Zoltan. "Collaborative search and rescue by autonomous robots." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/411031/.

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In recent years, professional first responders have started to use novel technologies at the scene of disasters in order to save more lives. Increasingly, they use robots to search disaster sites. One of the most widely and successfully used robot platforms in the disaster response domain are unmanned aerial vehicles (UAVs). UAVs allow remote inspection and mapping. They are able to provide high resolution imagery and often need minimal infrastructure to fly. This allows settings where multiple UAVs are airborne accelerating the information gathering from the disaster site. However, current deployments use labour intensive, individually teleoperated UAVs. Given this, there is a drive toward using multiple robots operating with a certain level of autonomy, in order to decrease the operators' workload. One approach for utilising multiple robots in this way is semi-autonomous operation supervised by a small number of professionals; only requiring human experts for crucial decisions. Current commercial UAV platforms also allow the deployment of a diverse group of robots, allowing them to combine their individual capabilities to be more ecient. For example, xed-wing UAVs are capable of flying faster and carry larger payload, but when they do so, they should be deployed with higher safety measures (safety pilots are required for non-lightweight aircraft). On the other hand, small rotary-wing UAVs are more agile and can approach and provide imagery about objects on the ground. To this end, this thesis develops a number of new approaches for the collaboration of a heterogeneous group of robots in disaster response. More specifically, the problem of collaborative planning with robots operating in an uncertain workflow based setting is investigated by solving the search and rescue (SAR) collaboration problem. Of course, the problem complexity increases when collaborating with dierent robots. It is not different in this setting, the actions of dierent types of robots need to be planned with dependencies between their actions under uncertainty. To date, research on collaboration between multiple robots has typically focused on known settings, where the possible robot actions are dened as a set of tasks. However, in most real world settings, there is a signicant amount of uncertainty present. For ii example, information about a disaster site develops gradually during disaster relief, thus initially there is often very little certainty about the locations of people requiring assistance (e.g. damaged buildings, trapped victims, or supply shortages). Existing solutions that tackle collaboration in the face of uncertain information are typically limited to simple exploration or target search problems. Moreover, the use of generic temporal planners rapidly becomes intractable for such problems unless applied in a domain-specific manner. Finally, domain specific approaches rarely involve complex action relations, such as task dependencies where the actions of some robots are built on the actions of others. When they do so, decomposition techniques are applied to decrease the problem complexity, or simple heuristics are applied to enhance similar collaboration. Such approaches often lead to low quality solutions, because vital action dependencies across different roles are not taken into account during the optimisation. Against this background, we oer novel online planning approaches for heterogeneous multi-robot collaboration under uncertainty. First, we provide a negotiation-based bidirectional collaborative planning approach that exploits the potential in determinisation via hindsight optimisation (HOP) combined with long-term planning. Second, we extend this approach to create an anytime Monte Carlo tree search planner that also utilises HOP combined with long-term planning. In online planning settings, such as SAR, anytime planners are benecial to ensure the ability of providing a feasible plan within the given computational budget. Third, we construct a scenario close to physical deployment that allows us to show how our long-term collaborative planning outperforms the current state of the art path-planning approaches by 25 %. We conclude that long-term collaborative planning under uncertainty provides an improvement when planning in SAR settings. When combined, the contributions presented in this thesis represent an advancement in the state of the art in the eld of online planning under uncertainty. The approaches and methods presented can be applied in collaborative settings when uncertainty plays an important role for defining dependencies between partial planning problems.

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Hammerseth, Vegard B. "Autonomous Unmanned Aerial Vehicle In Search And Rescue." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22880.

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This report presents a way of using autonomous drones to enhance search and rescue operations and takes the first steps in bringing the system to life. By using autonomous drones, less experience is required by the rescue personnel and drone specialists become excessive in this matter. Due to autonomy a drone can operate outside a valid radio link. Hence, when signal is lost, the craft can continue to search, buffer the information and send it when the link becomes active. By creating affordable drones the threshold decreases for deploying a unit in bad weather or other missions where the feedback is more important than drone return.Because the drones must sweep a large area, an aeroplane is the best suitable airframe. To generate less drag and increase stability; long, slender and thin-as-possible wings are recommended. To achieve aerodynamic stability, non-swept wings and a small positive dihedral angle is also advised. The wings should be attached on top of a slender and small-as-possible fuselage. However, due to the difficulties in landing and vulnerabilities related to this, a flying wing which obey the same design requirements, may be a better choice. A prototype for a flying wing made in expanded polypropylene was put together and tested. It proved to be resilient, able to withstand significant abuse, quickly recover to its former structure and be repaired in minutes. Highly convenient for various landing areas.An attitude and heading reference system (AHRS) is required to tell which orientation a vehicle may have. An affordable version has been realised by using micro electro mechanical sensors and a micro controller. Since the vehicle must orient itself within a search area, a NAVSTAR Global Positioning System (GPS) and way-point approach were drones are pre-programmed to follow a path has been developed together with a complete flight system.The drones search path must be programmed automatically from the given search area by the rescue personnel. This requires software and active communication link between a ground station and drones. An intuitive graphical user interface has been developed and verified to work by marking an area in the program and send coordinates over a commercial communication link to a prototype drone.A thermal imaging camera can be used to detect humans. It will make mammals stand out in an environment when viewed in the produced image (thermogram). Together with an arbitrarily threshold limit, the operators can be notified with coordinates when the threshold is reached. An experiment was carried out by placing a human at known coordinates. A drone with self position equipment was sent over the human repeatedly, automatically locating the person and reporting its location. It was concluded that the drone would be within 20 meters radius of the person.The generality and affordability of the airframe, AHRS and vision system can be useful for the university in the years ahead and the threshold for realising applications which uses any of these systems has therefore been reduced. A drone based system like this can enhance search and rescue and assist the majority by saving time, money and lives.

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Ryu, Kun Jin. "Autonomous Robotic Strategies for Urban Search and Rescue." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19194.

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This dissertation proposes autonomous robotic strategies for urban search and rescue (USAR) which are map-based semi-autonomous robot navigation and fully-autonomous robotic search, tracking, localization and mapping (STLAM) using a team of robots. Since the prerequisite for these solutions is accurate robot localization in the environment, this dissertation first presents a novel grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan and estimation of the robot pose, the proposed technique corrects the estimation error by matching the new scan to the globally defined grid map. To improve the accuracy of the correction, each grid cell of the map is represented by multiple normal distributions (NDs). The new scan to be matched to the map is also represented by NDs, which achieves the scan-to-map matching by the ND-to-ND matching. In the map-based semi-autonomous robot navigation strategy, a robot placed in an environment creates the map of the environment and sends it to the human operator at a distant location. The human operator then makes decisions based on the map and controls the robot via tele-operation. In case of communication loss, the robot semi-autonomously returns to the home position by inversely tracking its trajectory with additional optimal path planning. In the fully-autonomous robotic solution to USAR, multiple robots communicate one another while operating together as a team. The base station collects information from each robot and assigns tasks to the robots. Unlike the semi-autonomous strategy there is no control from the human operator. To further enhance the efficiency of their cooperation each member of the team specifically works on its own task. A series of numerical and experimental studies were conducted to demonstrate the applicability of the proposed solutions to USAR scenarios. The effectiveness of the scan-to-map matching with the multi-ND representation was confirmed by analyzing the error accumulation and by comparing with the single-ND representation. The applicability of the scan-to-map matching to the real SLAM problem was also verified in three different real environments. The results of the map-based semi-autonomous robot navigation showed the effectiveness of the approach as an immediately usable solution to USAR. The effectiveness of the proposed fully- autonomous solution was first confirmed by two real robots in a real environment. The cooperative performance of the strategy was further investigated using the developed platform- and hardware-in-the-loop simulator. The results showed significant potential as the future solution to USAR. Ph. D.

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Finegan, Edward Graham. "Intelligent Autonomous Data Categorization." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd/1343.

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The goal of this research was to determine if the results of a simple comparison algorithm (SCA) could be improved by adding a hyperspace analogue to language model of memory (HAL) layer to form NCA. The HAL layer provides contextual data that otherwise would be unavailable for consideration. It was found that NCA did improve the results when compared to SCA alone. However, NCA added complexity problems that limit its practicality. The complexity of this algorithm is On3 where n is equal to the number of unique symbols in the data. While there is a relativity reasonable soft upper bound for the number of unique symbols used in a language, the complexity still limits the uses of the NCA combined algorithm. The conclusion from this research is that NCA can improve results. This research also suggested that the quality of results might increase as more data is processed by NCA.

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Compton, Mark A. "Minefield search and object recognition for autonomous underwater vehicles." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/30604.

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Ambrose-Thurman, Andrew Michael Luke. "Autonomous, collaborative, unmanned aerial vehicles for search and rescue." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/10652/.

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Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue.

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Beyme, Steffen. "Autonomous, wireless sensor network-assisted target search and mapping." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50725.

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The requirements of wireless sensor networks for localization applications are largely dictated by the need to estimate node positions and to establish routes to dedicated gateways for user communication and control. These requirements add significantly to the cost and complexity of such networks. In some applications, such as autonomous exploration or search and rescue, which may benefit greatly from the capabilities of wireless sensor networks, it is necessary to guide an autonomous sensor and actuator platform to a target, for example to acquire a large data payload from a sensor node, or to retrieve the target outright. We consider the scenario of a mobile platform capable of directly interrogating individual, nearby sensor nodes. Assuming that a broadcast message originates from a source node and propagates through the network by flooding, we study applications of autonomous target search and mapping, using observations of the message hop count alone. Complex computational and communication tasks are offloaded from the sensor nodes, leading to significant simplifications of the node hardware and software. This introduces the need to model the hop count observations made by the mobile platform to infer node locations. Using results from first-passage percolation theory and a maximum entropy argument, we formulate a stochastic jump process which approximates the message hop count at distance r from the source. We show that the marginal distribution of this process has a simple analytic form whose parameters can be learned by maximum likelihood estimation. Target search involving an autonomous mobile platform is modeled as a stochastic planning problem, solved approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by an open-loop search plan in which path constraints and assumptions about future information gains are relaxed. It is shown that the performance is improved over typical information-driven approaches. Finally, the hop count observation model is applied to an autonomous mapping problem. The platform is guided under a myopic utility function which quantifies the expected information gain of the inferred map. Utility function parameters are adapted heuristically such that map inference improves, without the cost penalty of true non-myopic planning. Applied Science, Faculty of Electrical and Computer Engineering, Department of Graduate

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Più fonti

Libri sul tema "Autonomous search"

Hamadi, Youssef, Eric Monfroy, and Frédéric Saubion. Autonomous search. Heidelberg: Springer, 2011.

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Eric, Monfroy, Saubion Frédéric, and SpringerLink (Online service), eds. Autonomous Search. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Hamadi, Youssef, Eric Monfroy, and Frédéric Saubion, eds. Autonomous Search. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-21434-9.

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Real-time search for learning autonomous agents. Boston: Kluwer Academic Publishers, 1997.

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Wang, Yue, and Islam I. Hussein. Search and Classification Using Multiple Autonomous Vehicles. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2957-8.

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Burian, Erik Alfred. Search methods for an autonomous underwater vehicle using scalar measurements. Springfield, Va: Available from National Technical Information Service, 1996.

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Wang, Yue. Search and Classification Using Multiple Autonomous Vehicles: Decision-Making and Sensor Management. 2nd ed. London: Springer London, 2012.

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The search for the Panchen Lama. New York: W.W. Norton, 2000.

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A mountain in Tibet: The search for Mount Kailas and the sources of the great rivers of India. London: Abacus, 2003.

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Foreign devils on the Silk Road: The search for the lost cities and treasures of Chinese Central Asia. Oxford: Oxford University Press, 2001.

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Capitoli di libri sul tema "Autonomous search"

Petrik, Marek, and Shlomo Zilberstein. "Learning Feature-Based Heuristic Functions." In Autonomous Search, 269–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_11.

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Hamadi, Youssef, Eric Monfroy, and Frédéric Saubion. "An Introduction to Autonomous Search." In Autonomous Search, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_1.

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Hamadi, Youssef, Said Jabbour, and Jabbour Sais. "Control-Based Clause Sharing in Parallel SAT Solving." In Autonomous Search, 245–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_10.

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Eiben, A. E., and S. K. Smit. "Evolutionary Algorithm Parameters and Methods to Tune Them." In Autonomous Search, 15–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_2.

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Hoos, Holger H. "Automated Algorithm Configuration and Parameter Tuning." In Autonomous Search, 37–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_3.

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Bridge, Derek, Eoin O’Mahony, and Barry O’Sullivan. "Case-Based Reasoning for Autonomous Constraint Solving." In Autonomous Search, 73–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_4.

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Epstein, Susan L., and Smiljana Petrovic. "Learning a Mixture of Search Heuristics." In Autonomous Search, 97–127. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_5.

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Battiti, Roberto, and Paolo Campigotto. "An Investigation of Reinforcement Learning for Reactive Search Optimization." In Autonomous Search, 131–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_6.

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Maturana, Jorge, Álvaro Fialho, Frédéric Saubion, Marc Schoenauer, Frédéric Lardeux, and Michèle Sebag. "Adaptive Operator Selection and Management in Evolutionary Algorithms." In Autonomous Search, 161–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_7.

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Stützle, Thomas, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari, and Marco Dorigo. "Parameter Adaptation in Ant Colony Optimization." In Autonomous Search, 191–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21434-9_8.

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Atti di convegni sul tema "Autonomous search"

Gelenbe, Erol, and Yonghuan Cao. "Autonomous search for mines." In AeroSense '97, edited by Abinash C. Dubey and Robert L. Barnard. SPIE, 1997. http://dx.doi.org/10.1117/12.280898.

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Stevens, Timothy, and Timothy H. Chung. "Autonomous search and counter-targeting using Levy search models." In 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2013. http://dx.doi.org/10.1109/icra.2013.6631134.

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Nesterenko, P. A., and V. P. Gusev. "Autonomous anti-theft search satellite system." In 2014 12th International Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2014. http://dx.doi.org/10.1109/apeie.2014.7040835.

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Xue, Songdong, Jianchao Zeng, and Guoyou Zhang. "A review of autonomous robotic search." In 2011 International Conference on Electrical and Control Engineering (ICECE). IEEE, 2011. http://dx.doi.org/10.1109/iceceng.2011.6057781.

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Rasouli, Amir, and John K. Tsotsos. "Visual Saliency Improves Autonomous Visual Search." In 2014 Canadian Conference on Computer and Robot Vision (CRV). IEEE, 2014. http://dx.doi.org/10.1109/crv.2014.23.

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Cao, Yonghuan, and Erol Gelenbe. "Autonomous search for mines: II. Hierarchical search using sensory data." In Aerospace/Defense Sensing and Controls, edited by Abinash C. Dubey, James F. Harvey, and J. Thomas Broach. SPIE, 1998. http://dx.doi.org/10.1117/12.324143.

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Plenge, Benjamin, and M. Anderson. "Wide area autonomous search munition search pattern "optimization" using genetic algorithms." In 39th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-1124.

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Britt, Winard, William Lyles, and David M. Bevly. "A State Machine Controller for the Autonomous Guidance of a Trained Canine." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4020.

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Abstract (sommario):

This work demonstrates the autonomous command of a trained search canine to multiple waypoints using a novel state machine control algorithm. A hardware system is utilized in order to interface with the Global Position Satellite (GPS) system and with a tone and vibration generator for the purpose of accurately navigating and commanding the canine. An operational control algorithm for autonomous guidance of the canine is described in detail. Empirical results of an autonomously commanded canine are demonstrated with an 73% mission success rate for simple paths and a 62% mission success rate for complex paths. This work demonstrates a novel way to expand the capabilities of canines in a wide variety of missions, including search and detection.

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Perera, Chamathke, Muhammad Galib, and Ryan Tang Dan. "Autonomous Search and Rescue System (Project ASARS)." In The 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation, and Infrastructure for Sustainable Cities and Communities”. Latin American and Caribbean Consortium of Engineering Institutions, 2019. http://dx.doi.org/10.18687/laccei2019.1.1.494.

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Xu, Liheng, Chi Zhang, Yuehu Liu, Le Wang, and Li Li. "Worst Perception Scenario Search for Autonomous Driving." In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020. http://dx.doi.org/10.1109/iv47402.2020.9304731.

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Rapporti di organizzazioni sul tema "Autonomous search"

Edwards, Dean B. Cooperative Autonomous Underwater Vehicles Used to Search Large Ocean Areas for Mines. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada507940.

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Gage, Douglas W. Proceedings of the Autonomous Vehicles in Mine Countermeasures Symposium, Monterey CA, 4-7 April 1995 Many-Robot MCM Search Systems. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada422749.

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