Path Traversal Research Articles

End-to-End Learning for Visual Navigation of Forest Environments

Off-road navigation in forest environments is a challenging problem in field robotics. Rovers are required to infer their traversability over a priori unknown and dynamically changing forest terrain using noisy onboard navigation sensors. The problem is compounded for small-sized rovers, such as that of a swarm. Their size-proportional low-viewpoint affords them a restricted view for navigation, which may be partially occluded by forest vegetation. Hand-crafted features, typically employed for terrain traversability analysis, are often brittle and may fail to discriminate obstacles in varying lighting and weather conditions. We design a low-cost navigation system tailored for small-sized forest rovers using self-learned features. The MobileNet-V1 and MobileNet-V2 models, trained following an end-to-end learning approach, are deployed to steer a mobile platform, with a human-in-the-loop, towards traversable paths while avoiding obstacles. Receiving a 128 × 96 pixel RGB image from a monocular camera as input, the algorithm running on a Raspberry Pi 4, exhibited robustness to motion blur, low lighting, shadows and high-contrast lighting conditions. It was able to successfully navigate a total of over 3 km of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, and standing trees, in over five different weather conditions and four different times of day.

TrEKBQA:Traversing Knowledge Graph Embedding for Multi-hop Knowledge Base Question Answering

Recent research apply KG embedding to multi-hop Knowledge Base Question Answering(KBQA) to predict missing links, however, it is often affected by the skewed distribution of nodes in the knowledge graph, resulting in poor generalization of the model. Therefore, we propose a method TrEKBQA based on traversing the knowledge graph embedding space for multi-hop KBQA, which performs path traversal in the KG embedding space instead of KG itself for link prediction to complete the knowledge graph, thus improving the accuracy of multi-hop KBQA.TrEKBQA model complex relationships using correlations between individual links and longer paths connecting the same pair of entities to traverse the KG embedding space to mitigate the effects of biased distribution of nodes and improve the performance of link prediction. In the pre-processing process, TrEKBQA uses the PRN algorithm to extract subgraphs related to the problem entity to reduce the number of target entities. Through experiments on multiple benchmark datasets, we demonstrate the effectiveness of TrEKBQA on KBQA tasks.

Off-Road Navigation of Legged Robots Using Linear Transfer Operators⋆

This paper presents the implementation of of-road navigation on legged robots using convex optimization through linear transfer operators. Given a traversability measure that captures the of-road environment, we lift the navigation problem into the density space using the Perron-Frobenius (P-F) operator. This allows the problem formulation to be represented as a convex optimization. Due to the operator acting on an infinite-dimensional density space, we use data collected from the terrain to get a finite-dimension approximation of the convex optimization. Results of the optimal trajectory for of-road navigation are compared with a standard iterative planner, where we show how the linear operator approach generates a more traversable path for the legged robot compared to the suboptimal iterative planner.

Uncovering the Hippocampal Mechanisms Underpinning Spatial Learning and Flexible Navigation.

Navigating toward food, shelter, or mates is crucial for animals' survival. This process requires accurate estimates of both one's current self-location and intended goal location, as well as the ability to plan a traversable path between these sites. [Tolman (1948)][1] was the first to propose that

WayFAST: Navigation With Predictive Traversability in the Field

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

Fluoroscopic Image-Based 3-D Environment Reconstruction and Automated Path Planning for a Robotically Steerable Guidewire.

Cardiovascular diseases are the leading cause of death globally and surgical treatments for these often begin with the manual placement of a long compliant wire, called a guidewire, through different vasculature. To improve procedure outcomes and reduce radiation exposure, we propose steps towards a fully automated approach for steerable guidewire navigation within vessels. In this paper, we utilize fluoroscopic images to fully reconstruct 3-D printed phantom vasculature models by using a shape-from-silhouette algorithm. The reconstruction is subsequently de-noised using a deep learning-based encoder-decoder network and morphological filtering. This volume is used to model the environment for guidewire traversal. Following this, we present a novel method to plan an optimal path for guidewire traversal in three-dimensional vascular models through the use of slice planes and a modified hybrid A-star algorithm. Finally, the developed reconstruction and planning approaches are applied to an ex vivo porcine aorta, and navigation is demonstrated through the use of a tendon-actuated COaxially Aligned STeerable guidewire (COAST).

An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs

Set reachability query in directed graphs has a plethora of graph-based applications such as dependency analysis and graph centrality calculation. Given two sets <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> of source and target vertices, set reachability query needs to acquire all pairs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s{\in }S$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$t{\in }T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> can reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> . The state-of-the-art approach distributed set reachability (DSR) investigates the set reachability query in a distributed environment and adopts a static graph-based index to enhance the query efficiency. Nevertheless, DSR needs to store the graph-based index in all partitions, which causes a huge space overhead. Furthermore, it cannot efficiently solve the negative query <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> cannot reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , since DSR needs to traverse the whole reachable paths and becomes unable to efficiently reduce the computations. To alleviate these issues, we propose a novel multi-level 2-hop (ML2hop) index for the set reachability query in a distributed environment. Based on ML2hop, we further present a bi-directional query algorithm, called MLQA, to achieve efficient support for both positive and negative queries in Pregel-like systems. Generally, MLQA is equipped with the following three significant properties: (1) Low computation costs. It reduces redundant local computations in each partition by controlling the rounds of path traversals. (2) Low communication costs. It restricts the message exchange among different partitions within one single round with guaranteed accuracy of query results. (3) High parallelism. It adopts a bi-directional query technique for message propagation, achieving the better query efficiency than the forward-traversal query strategy utilized in DSR. Experimental results over several real-world graphs demonstrate that MLQA significantly outperforms the state-of-the-art algorithm by up to two orders of magnitude speedup.

A Path Planning Model for Stock Inventory Using a Drone

In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.

Optimally Solving the Multiple Watchman Route Problem with Heuristic Search (Extended Abstract)

In the Watchman Route Problem (WRP), the task is to find a path for a watchman agent such that all locations in the given map will be visually seen by the watchman at least once during the path traversal. Recently, the problem has been optimally solved on a grid map using heuristic search. In this paper, we extend this work to the case of multiple agents. We call this problem the Multiple Watchman Route Problem (MWRP). In MWRP, the task is to find a path for each watchman such that each location on the map will be seen by at least one watchman. We optimally solve MWRP with heuristic search for two different objective functions with a number of A*-based variants, including an enhanced branching mechanism. We then provide an experimental study on these methods and on other attributes of this problem.

MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains

Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to predict. Nevertheless, planning a safely traversable path through such an environment requires the ability to predict the outcomes of these interactions instead of avoiding them. One approach to doing this is to learn the interaction model offline based on collected data. Unfortunately, though, this requires large amounts of data and can often be brittle. Alternatively, models using physics-based simulators can generate large data and provide a reliable prediction. However, they are very slow to query online within the planning loop. This work proposes an algorithmic framework that utilizes the combination of a learned model and a physics-based simulation model for fast planning. Specifically, it uses the learned model as much as possible to accelerate planning while sparsely using the physics-based simulator to verify the feasibility of the planned path. We provide a theoretical analysis of the algorithm and its empirical evaluation showing a significant reduction in planning times.

Using WAF to protect the university’s internal services in the Zero Trust structure

The article presents the results of a pilot study on the use of a Web Application Firewall (Web Application Firewall or WAF) to protect the internal services of the information educational environment of the university (IEEU). It is shown that this task is extremely important in the context of globalization of education. The use of WAF is performed in the Zero Trust structure. The system was tested in two stages. Firstly, tools were used to automate the search for web vulnerabilities (web vulnerability scanners) IEEU. At the second stage, manual testing of applications for vulnerabilities of SQL injection, cross-site scripting and Path Traversal attacks was carried out. It is shown that the results obtained make it possible to improve the protection of services in the university’s local networks, which is important for achieving the ultimate goal effective protection of end users and IEEU services in the context of globalization of education. It has been established that the use of WAF in systems with zero trust is a fairly common option for protecting services within organizations, including educational ones. It is shown that the use of open WAF solutions in the Zero Trust structure allows you to more flexibly and personally adjust protection to the appropriate needs of university services.

Fluently specifying taint-flow queries with fluentTQL

Previous work has shown that taint analyses are only useful if correctly customized to the context in which they are used. Existing domain-specific languages (DSLs) allow such customization through the definition of deny-listing data-flow rules that describe potentially vulnerable or malicious taint-flows. These languages, however, are designed primarily for security experts who are expected to be knowledgeable in taint analysis. Software developers, however, consider these languages to be complex. This paper thus presents fluent TQL, a query specification language particularly for taint-flows. fluentTQL is internal Java DSL and uses a fluent-interface design. fluentTQL queries can express various taint-style vulnerability types, e.g. injections, cross-site scripting or path traversal. This paper describes fluentTQL’s abstract and concrete syntax and defines its runtime semantics. The semantics are independent of any underlying analysis and allows evaluation of fluent TQL queries by a variety of taint analyses. Instantiations of fluentTQL, on top of two taint analysis solvers, Boomerang and FlowDroid, show and validate fluent TQL expressiveness. Based on existing examples from the literature, we have used fluentTQL to implement queries for 11 popular security vulnerability types in Java. Using our SQL injection specification, the Boomerang-based taint analysis found all 17 known taint-flows in the OWASP WebGoat application, whereas with FlowDroid 13 taint-flows were found. Similarly, in a vulnerable version of the Java Spring PetClinic application, the Boomerang-based taint analysis found all seven expected taint-flows. In seven real-world Android apps with 25 expected malicious taint-flows, 18 taint-flows were detected. In a user study with 26 software developers, fluentTQL reached a high usability score. In comparison to CodeQL, the state-of-the-art DSL by Semmle/GitHub, participants found fluentTQL more usable and with it they were able to specify taint analysis queries in shorter time.

HaploMaker: An improved algorithm for rapid haplotype assembly of genomic sequences.

BackgroundIn diploid organisms, whole-genome haplotype assembly relies on the accurate identification and assignment of heterozygous single-nucleotide polymorphism alleles to the correct homologous chromosomes. This appropriate phasing of these alleles ensures that combinations of single-nucleotide polymorphisms on any chromosome, called haplotypes, can then be used in downstream genetic analysis approaches including determining their potential association with important phenotypic traits. A number of statistical algorithms and complementary computational software tools have been developed for whole-genome haplotype construction from genomic sequence data. However, many algorithms lack the ability to phase long haplotype blocks and simultaneously achieve a competitive accuracy.ResultsIn this research we present HaploMaker, a novel reference-based haplotype assembly algorithm capable of accurately and efficiently phasing long haplotypes using paired-end short reads and longer Pacific Biosciences reads from diploid genomic sequences. To achieve this we frame the problem as a directed acyclic graph with edges weighted on read evidence and use efficient path traversal and minimization techniques to optimally phase haplotypes. We compared the HaploMaker algorithm with 3 other common reference-based haplotype assembly tools using public haplotype data of human individuals from the Platinum Genome project. With short-read sequences, the HaploMaker algorithm maintained a competitively low switch error rate across all haplotype lengths and was superior in phasing longer genomic regions. For longer Pacific Biosciences reads, the phasing accuracy of HaploMaker remained competitive for all block lengths and generated substantially longer block lengths than the competing algorithms.ConclusionsHaploMaker provides an improved haplotype assembly algorithm for diploid genomic sequences by accurately phasing longer haplotypes. The computationally efficient and portable nature of the Java implementation of the algorithm will ensure that it has maximal impact in reference-sequence–based haplotype assembly applications.

Security enhanced optimal trajectory selection for mobile sink using reinforcement learning

Wireless visual sensor networks (WVSNs) have emerged as a strategic inter disciplinary category of WSN with its visual sensor based intelligence that has garnered considerable attention. The growing demand for energy efficient and maximized life time networks in highly critical applications like surveillance, military and medicine has opened up more prospects as well as challenges in the deployment of WVSNs. Multi-hop communication in WVSN results in overloading of intermediate sensor nodes due to its dual function in the network which results in hotspot effect. This can be mitigated with the help of mobile sinks and rendezvous points based route design. But mobile sinks has to visit every cluster head to gather data which results in longer traversal path and higher latency and power consumption related issues if not addressed properly will impact the performance of the network. Our objective is to analyze and determine the optimal trajectory for mobile sink node traversal with the help of a high quality transmission architecture integrated with reinforcement learning and isolation forest based anomaly detection to propose an energy efficient meta-heuristic approach to enhance the performance of network by reducing the latency and securing the network against possible attacks.

On‐line estimation of power model parameters for skid‐steer robots with applications in mission energy use prediction

AbstractThis paper presents algorithms to predict the energy used by a skid‐steer robot to complete a given path, including the refinement of that prediction during operation. The vast majority of commercially available ground mobile robots utilize skid‐steer technology due to the robustness and maneuverability of the design. However, the complex track/terrain interactions developed during skid‐steer maneuvers make traction‐related power losses difficult to model. Accurate estimates of skid‐steer power and energy usage enable more intelligent mission planning decisions to be made. In this study, a skid‐steer power model that is linear in three unknown parameters was used and tested extensively on two skid‐steer vehicles, one tracked and the other wheeled. Repeated testing shows good repeatability of estimated parameters, and results indicate that the tracked vehicle exhibits a much larger sensitivity to the surface types tested than the wheeled vehicle. The skid‐steer power model is then combined with a kinematic model of skid‐steer movement and a trajectory control algorithm to predict the energy required to complete a given traversal path. This is useful for estimating energy demands for retro‐traversal, an application demonstrated in this study. The energy predictions are further refined through the mapping of power model parameters onto terrain types using overhead imagery of the area of operation. The maps are used to predict what terrain types will be encountered along the planned path to improve the estimate of the energy required.

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. Artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

An Improved A-Star Algorithm for Complete Coverage Path Planning of Unmanned Ships

Aiming at the low efficiency and high energy consumption of unmanned ships traversing the entire area, a complete coverage path planning algorithm based on the improved A-star algorithm is proposed. The positioning and vision systems of unmanned ships are used to digitize the actual water information, and the grid method is used to convert the information into an environmental map that can be planned. Compared to the trapezoidal partition of unity method and the short-side reciprocating traversal algorithm in the traversal process, experiments show that path planning is more efficient with the boustrophedon partition of unity method and the long-side reciprocating traversal algorithm. Aiming at the “dead zone”, an improved A-star algorithm is proposed on the basis of the traditional A-star algorithm, that it can shorten about 1/4 path using the proposed algorithm. Simulation shows that the improved A-star algorithm can shorten the traversal path to 40 steps but the traditional A-star algorithm needs 54 steps. Navigation test shows that the proposed algorithm can shorten the traversal path and improve traversal efficiency while ensuring the coverage of unmanned ships.

Your WAP Is at Risk: A Vulnerability Analysis on Wireless Access Point Web-Based Management Interfaces

This work provides an answer to the following key question: Are the Web-based management interfaces of the contemporary off-the-shelf wireless access points (WAP) free of flaws and vulnerabilities? The short answer is not very much. That is, after performing a vulnerability assessment on the Web interfaces of six different WAPs by an equal number of diverse renowned vendors, we reveal a significant number of assorted medium-to-high severity vulnerabilities that are straightforwardly or indirectly exploitable. Overall, 13 categories of vulnerabilities translated to 28 zero-day attacks are exposed. Our findings range from legacy path traversal, cross-site scripting, and clickjacking attacks to HTTP request smuggling and splitting, replay, denial of service, and information leakage among others. In the worst-case scenario, the attacker can acquire the administrator’s (admin) credentials and the WAP’s Wi-Fi passphrases or permanently lock the admin out of accessing the WAP’s Web interface. On top of everything else, we identify the already applied hardening measures by these devices and elaborate on extra countermeasures that are required to tackle the identified weaknesses. To our knowledge, this work contributes the first wholemeal appraisal of the security level of this kind of Web-based interfaces that go hand in glove with the myriads of WAPs out there, and it is therefore anticipated to serve as a basis for further research in this timely and challenging field.

Mining Simple Path Traversal Patterns in Knowledge Graph

The data mining has remained a subject of unfailing charm for research. The knowledge graph is rising and showing infinite life force and strong developing potential in recent years, where it is observed that acyclic knowledge graph has capacity for enhancing usability. Though the development of knowledge graphs has provided an ample scope for appearing the abilities of data mining, related researches are still insufficient. In this paper, we introduce path traversal patterns mining to knowledge graph. We design a novel simple path traversal pattern mining framework for improving the representativeness of result. A divide-and-conquer approach of combining each path is proposed to discover the most frequent traversal patterns in knowledge graph. To support the algorithm, we design a linked list structure indexed by the length of sequences with handy operations. The correctness of algorithm is proven. Experiments show that our algorithm reaches a high coverage with low output amounts compared to existing frequent sequence mining algorithms.

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.