Navigation Commands Research Articles

Optimizing autonomous navigation in unknown environments: A novel trap avoiding vector field histogram algorithm VFH+T

The Vector Field Histogram Plus (VFH+) algorithm is a cornerstone in robotic navigation, renowned for its efficiency and straightforward implementation across a multitude of environments. Despite its widespread utility, the algorithm's inherent limitations in handling complex obstacle entrapments necessitate refinement. This paper presents an advanced iteration, designated as VFH + T, which incorporates sophisticated memory-based trap recognition and avoidance mechanisms. This enhancement facilitates dynamic adjustment of navigation strategies through the integration of geometrical rules that retrospectively inform path planning decisions. Moreover, the VFH + T algorithm intricately melds the robotic platform's kinematic and dynamic constraints, optimizing real-time navigational commands based on both current sensory input and historical environmental interactions. Empirical simulations validate the enhanced algorithm's proficiency in circumventing navigational traps, improving operational safety and efficiency. Comparative analysis with VFH+ and VFH* algorithms show up to 17 % reduction in traveling distance due to the trap-avoidance technique during navigation. This advancement holds significant implications for enhancing autonomous navigation technologies in various practical applications, from self-driving vehicles to robotic aids in logistics and service industries.

Sensitivity analysis of profile navigation command of underwater gliders to the initial heading error for improving trajectory accuracy

With the widespread application of underwater gliders (UGs) in marine research, there is an increasing demand for improved trajectory control accuracy. However, the initial heading error is unavoidable in internally actuated UGs, making it challenging to rectify the trajectory error generated in the heading error correction process. To effectively mitigate the impact of this error on trajectory accuracy, this study analyzes the sensitivity of profile navigation command parameters to the initial heading error through dynamic modeling of Petrel-L UG. Firstly, a simulator is developed to model the behaviors of Petrel-L, mainly involving dynamic modeling, underwater navigation, low-level control, and environment model. Subsequently, the sensitivity of profile navigation parameters in various commands to the initial heading error is analyzed. Surrogate model and Monte Carlo simulation are integrated to improve the computational speed of the analysis while preserving calculation precision. The average maximum single-profile horizontal error is 661 m, and the average maximum single-profile course error is 6.47°. Among profile navigation parameters, the target pitch during diving is proved to be the primary factor affecting the maximum trajectory error, and the increase of this parameter can enhance trajectory accuracy. This research also provides valuable reference for path following and station-keeping control of other UGs.

OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones.

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.

Key Factors that Negatively Affect Performance of Imitation Learning for Autonomous Driving

Conditional imitation learning (CIL) has proven superior to other autonomous driving (AD) algorithms. However, its performance evaluation through physical implementations is still limited. This work contributes a systematic evaluation to identify key factors potentially improving its performance. It modified convolutional neural network parameter values, such as reducing the number of filter channels and neuron units, and implemented the model into a vision-based autonomous vehicle (AV). The AV has front-wheel steering with an Ackermann mechanism since it is commonly used by passenger cars. Using the Inertia Measurement Unit, we measured the vehicle’s location and yaw angle along the experimental route. The AV had to move autonomously through new road sectors in the morning, afternoon, and night. First, an overall performance evaluation was carried out. The results showed a 99% success rate from 648 evaluation experiments under different conditions in which the 1% failure rate happened at new intersections. Then, a turning performance evaluation was conducted to identify key factors leading to failure at new intersections. They include fast speed, dazzling light reflection, late navigation command change instant, and the untrained turning driving pattern. The AV never failed while driving on the trained routes. It had a 100% success rate when driving slower, even under various lighting conditions and at various driving patterns, including untrained intersections. Although this study is limited to identifying key factors at three constant speeds, the results become the foundation for future research to improve CIL performance for AD, including by incorporating multimodal fusion and multi-route networks.

IDROP: Robust Localization for Indoor Navigation of Drones With Optimized Beacon Placement

Drones in many applications need the ability to fly fully or partially autonomously to accomplish their mission. To allow these fully/partially autonomous flights, first, the drone needs to be able to locate itself constantly. Then the navigation command signal would be generated and passed on to the controller unit of the drone. In this paper, we propose a localization scheme for drones called iDROP (Robust Localization for Indoor Navigation of Drones with Optimized Beacon Placement) that is specifically devised for GPS-denied environments (e.g., indoor spaces). Instead of GPS signals, iDROP relies on speaker-generated ultrasonic acoustic signals to enable a drone to estimate its location. In general, localization error is caused by two factors: the ranging error and the error induced by relative geometry between the transmitters and the receiver. iDROP mitigates these two types of errors and provides a high-precision three-dimensional localization scheme for drones. iDROP employs a waveform that is robust against multipath fading. Moreover, placing beacons in optimal locations reduces the localization error induced by the relative geometry between the transmitters and the receiver.

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI.

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.

Navigation and control development for a four-wheel-steered mobile orchard robot using model-based design

Autonomous agricultural robots play an essential role in agricultural mechanization. Self-reliable navigation of these robots is a challenging task. Generally, Global Navigation Satellite Systems (GNSS) based approach, combined with other sensors, is mainly utilized for robot navigation. However, these navigation methods encounter trouble during GNSS signal outages and require a stable navigation solution. This paper proposes a stable navigational algorithm for an orchard robot based on the model-based design that works in a GNSS denied environment. The designed model takes the input driving coordinates and vehicle state from wheel and steering encoders and translates them into vehicle navigational command. The navigation model is built in MATLAB/Simulink environment. The dynamic behavior of the model is neglected, considering that the robot is a slow-moving vehicle due to its application requirement. For robot navigation, the control command given by the controller is divided into velocity and steering control that follows a defined path at a given velocity and steering rate. The developed system is verified both in real and simulation environments. The control algorithm is verified using a mobile robot (non-holonomic) in an outdoor environment. It is confirmed that the robot is able to navigate satisfactorily in the given environment with normalized root mean square difference error within the range of 0.2–0.4 for steering offset at turning and 0.05 degrees during straight travel. Similarly, the model correlation lies close to 0.99 compared to the steering input angle. The proposed navigational model gives a realistic approach for vehicle navigation with the least sensor interaction.

ОНТОЛОГИЧЕСКИЙ ПОДХОД В СОЗДАНИИ РОБОТОТЕХНИЧЕСКИХ КОМПЛЕКСОВ С ПОВЫШЕННОЙ СТЕПЕНЬЮ АВТОНОМНОСТИ

The aspects necessary for the implementation of robotic complexes with an increased degree ofautonomy (RC with IDA) in practical work are considered. The distinctive features of such complexes,the needs of the corresponding intelligent information control systems (IIСS) are indicated. The requirementof situational awareness is highlighted and, as a consequence, the need for a diverse systemof knowledge representation, means of perception of the external environment and comparison of operationalinformation with models and a priori information about this environment. In addition, it is pointedout the need to automate the processes of creating RC with IDA, accessibility, and simplification of theiruse. In order to answer these questions, the paper proposes to use the concept and mechanisms of ontologiesin relation to autonomous robotics. Examples of existing solutions in this area are given. In robotics,ontologies are used to define and conceptualize knowledge accepted by the community, using aformal description that is machine-readable, shared, and contains the flexibility to justify this knowledgein order to derive additional information. Ontologies are of considerable interest for multi-agent systemsfor organizing interaction between agents and with other systems in heterogeneous environments,the possibility of reuse and support for the development of new RCs. The author describes the constructionof an ontology proposed by the author in such an applied field as information support for targetedmovements of autonomous ground vehicles based on technical vision systems. All consideration is conductedin the configuration space of the information and control systems of the RC with IDA. This spaceallows you to aggregate a large number of different technologies used in the construction of RC. Theembodiment of a particular system in this space corresponds to the "assembly point". The coordinationof the forms of knowledge representation in the IICS is ensured by the consistent consideration of planesin this space. As a connecting link – a means for automated translation of descriptions of descriptiveontologies into descriptions of functional, machine-readable ontologies, the use of the language of information-motor actions and interpretive navigation commands is proposed. In conclusion, the shorttermprospects for the development of the described approach are considered, and wishes are expressedto the domestic community of roboticists.

Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network

Sentiment classification (SC) is an important research field in natural language processing (NLP) <span lang="EN-US">that classifying, extracting and recognizing subjective information from unstructured text, including opinions, evaluations, emotions, and attitudes. Human-robot interaction (HRI) also involves natural language processing, knowledge representation, and reasoning by utilizing deep learning, cognitive science, and robotics. However, sentiment classification for HRI is rarely implemented, especially to navigate a robot using the Indonesian Language which semantically dynamics when written in text. This paper proposes a sentiment classification of Bahasa Indonesia that supports the delta robot to move in particular trajectory directions. Navigation commands of the delta robot were vectorized using a word embedding method containing two-dimensional matrices to propose the classifier pattern such as convolutional neural network (CNN). The result compared the particular architecture of CNN, GloVe-CNN, and Word2Vec-CNN. As a classifier method, CNN models trained, validated, and tested with higher accuracy are 98.97% and executed in less than a minute. The classifier produces four navigation labels: right means </span><em><span lang="EN-US">'kanan'</span></em><span lang="EN-US">, left means </span><em><span lang="EN-US">'kiri</span></em><span lang="EN-US">', top means </span><em><span lang="EN-US">'atas</span></em><span lang="EN-US">', bottom means </span><em><span lang="EN-US">'bawah</span></em><span lang="EN-US">', and multiplier factor. The classifier result is utilized to transform any navigation commands into direction along with end-effector coordinates.</span>

A chaotic path planning generator enhanced by a memory technique

This work considers the problem of chaotic path planning, using an improved memory technique to boost performance. In this application, the dynamics of two simple chaotic maps are first used to generate a pseudo-random bit generator. Using this as a source, a series of navigation commands are generated and used by an autonomous robot to explore an area, while maintaining a random and unpredictable motion. This navigation strategy can bring overall area coverage, but also yields numerous revisits to previous cells. Here, a memory technique is applied to limit the chaotic motion of the robot to adjacent cells with the least number of visits, leading to overall improvement in performance. Numerical simulations are performed to evaluate the path planning strategy. The simulation results showcase a major improvement in coverage performance compared to the memory-free technique and also compared to an inverse pheromone technique previously developed by the authors. Also, the number of multiple visits to previous cells is significantly reduced with the proposed technique.

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using state representation learning. The output of this lightweight module is then directly fed into a target-reaching and obstacle-avoiding policy trained with reinforcement learning. We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes. The key part is the state representation, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. In our experiments with the quadrupedal robot ANYmal in simulation and in reality, we show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.

End-to-End Autonomous Driving: An Angle Branched Network Approach

Imitation learning for the end-to-end autonomous driving has drawn renewed attention from academic communities. Current methods either only use images as the input, which will yield ambiguities when a vehicle approaches an intersection, or use additional command information to navigate the vehicle but inefficiently. Focusing on making the vehicle automatically drive along the given path, we propose a new and effective navigation command called as subgoal angle which does not require human participation and is calculated by the current position and subgoal of the ego-vehicle. Thus, the subgoal angle contains more information than the navigation command represented as a one-hot vector. Additionally, we propose a model architecture called as angle branched network that makes predictions based on the subgoal angle. In this network, the subgoal angle is not only used for extracting useful features but also for guiding the appropriate prediction layer to make predictions for both the steer angle and the throttle status (which controls the acceleration). Experiments are conducted in a three-dimensional urban simulator. Both quantitive and qualitive results show the effectiveness of the navigation command and the angle branched network. Moreover, the performance can be further boosted by integrating both semantic and depth information into the driving model. Especially by using the depth information, collisions with vehicles and pedestrians can be reduced.

Wireless Ugv For Target Tracking And Obstacle Detection Using Live Surveillance

our aim is to develop a project that will benefit society. But nowadays it is employed to assist human in surveillance, rescue and recovery missions. This paper presents the prototype model of an UGV which is operated wirelessly through manual navigation commands based on the live video captured from the IP camera mounted on the board. The distance measurement is done by the Ultrasonic sensor from the obstacle and displayed in the LCD. The target tracking as well as attacking is done based on the obstacle and environment situation monitored in the live video. This complete set up and working of the UGV is described further in this paper.

Capture Point-Based Controller Using Real-Time Zero Moment Point Manipulation for Stable Bipedal Walking in Human Environment.

For collaboration of humans and bipedal robots in human environments, this paper proposes a stability control method for dynamically modifiable bipedal walking using a capture point (CP) tracking controller. A reasonable reference CP trajectory for the CP tracking control is generated using the real-time zero moment point (ZMP) manipulation without information on future footstep commands. This trajectory can be modified at any time during the single support phase according to a given footstep command. Accordingly, this makes it possible for the robot to walk stably with dynamically modifiable walking patterns, including sudden changes in navigational commands during the single support phase. A reference CP trajectory during the double support phase is also generated for continuity. The CP of the robot is controlled to track the reference trajectory using a ZMP-based CP tracking controller. The ZMP while walking is measured by the force-sensing resistor sensors mounted on the sole of each foot. A handling method for infeasible footstep commands is utilized so that the manipulated ZMP satisfies the allowable ZMP region for stability. The validity of the proposed method is verified through simulations and experiments.

Understanding Interaction Design Challenges in Mobile Extreme Citizen Science

ABSTRACTExtreme citizen science is a bottom up practice used to empower people by supporting them, via processes and technological tools, to find solutions for local problems, but also to tackle major sustainability challenges of the 21st century. Methods and tools based on mobile computing have been utilized by communities in various parts of the world, from the Congo Basin through the Amazonian rain forest. However, extreme citizen science initiatives often face severe challenges as pre-designed technological solutions prove to be non-transferable to peculiar environments of rural developing regions. In this paper we collect and investigate evidence from the implementation of various extreme citizen science initiatives in the developing world. Our aim is to identify key obstacles towards their successful realization, mainly focusing on the problem of user interaction with mobile computing solutions. We conduct interviews with nine experienced researchers who all performed extensive fieldwork within these initiatives, and who reflect on the technology interaction, knowledge organization, inter-cultural, social and usability issues. Based on our analysis we report among others, symptomatic difficulties with abstractions, representational hierarchies, and navigation commands, as well as potential improvements that mobile technology developers can implement in order to create a more inclusive environment for extreme citizen science.

Enhancing interpretation of uncertain information in navigational commands for service robots using neuro-fuzzy approach

Enhancing interpretation of uncertain information in navigational commands for service robots using neuro-fuzzy approach

Improving the understanding of navigational commands by adapting a robot’s directional perception based on the environment

Improving the understanding of navigational commands by adapting a robot’s directional perception based on the environment

A Generalized Stochastic Petri Net model of route learning for emergency egress situations

Route learning is an essential activity for a person visiting a new environment. The element of forgetting a location (called decision point) along a route, where a change in direction is needed, is of immense importance especially during emergency evacuation scenarios. It is this element that has not been given the attention it deserves in developing a route learning algorithm. This work proposes a model of route learning in a new environment based on landmarks using generalized stochastic Petri nets because landmarks based route learning has been observed as a method natural to humans. The model takes information about landmarks along a route and associated navigation commands and then chooses whether to save this information as part of the learned route or not. The selection is made by exploiting stochastic transitions for which the firing rates are dependent on the type of landmark encountered at a decision point. The final output is a route having some decision points missing; resembling the situation that humans encounter after they visit a route in a new environment. The model results closely match empirical results obtained with human subjects.

Rotary inverted pendulum with magnetically external perturbations as a source of the pendulum’s base navigation commands

The main objective of this paper is to drive a rotary inverted pendulum by following a desired navigation instruction. This navigation is commanded by the user through a new electromagnetic device which is allowed to perturb the pendulum from its upright position. This apparatus consists of an electronic magnetic driving circuit to introduce commands and realized via two operated magnetic coils. So, the external programmed magnetic perturbation can be seen as external commandments. Therefore, the control problem statement is solved via a modified regulation control implementation, to maintain the pendulum on its upright position and giving free manipulation of the base of the rotary inverted pendulum. Hence, by using the corresponding H∞-linear matrix inequality technique, a static state controller is designed and tested experimentally so supporting our findings.

<inline-formula> <tex-math notation="LaTeX">$\varepsilon ^{\star }$</tex-math> </inline-formula>: An Online Coverage Path Planning Algorithm

This paper presents an algorithm called $\varepsilon ^{\star }$ , for online coverage path planning of unknown environment. The algorithm is built upon the concept of an Exploratory Turing Machine (ETM), which acts as a supervisor to the autonomous vehicle to guide it with adaptive navigation commands. The ETM generates a coverage path online using Multiscale Adaptive Potential Surfaces (MAPS), which are hierarchically structured and dynamically updated based on sensor information. The $\varepsilon ^{\star }$ -algorithm is computationally efficient, guarantees complete coverage, and does not suffer from the local extrema problem. Its performance is validated by 1) high-fidelity simulations on Player/Stage and 2) actual experiments in a laboratory setting on autonomous vehicles.