Framework For Autonomous Navigation Research Articles

Autonomous Vision-Based Navigation and Stability Augmentation Control of a Biomimetic Robotic Hammerhead Shark

The application potential of robotic fish embedded with intelligent visual navigation algorithms in underwater autonomous operation is on full display recently. However, the existing visual navigation methods are limited by the underwater visual conditions and the motion characteristics of robotic fish. To this end, this paper proposes a novel autonomous navigation framework integrated with visual stabilization control. In practice, a stereo vision-based navigation network is proposed to generate the guidance law. On this basis, a biomimetic robotic hammerhead shark with a controllable cephalofoil is developed, and a nonlinear model predictive controller for cephalofoil stabilization relying on the dynamic model is elaborately designed. Extensive simulations and underwater experiments are conducted to validate the effectiveness and superiority of the proposed methods, which significantly enhance exploration efficiency and reduce image jitter by 26.02% compared to the traditional methods. The obtained results provide a new idea for underwater robots to autonomously explore the ocean. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problem of vision-based underwater autonomous navigation for a biomimetic robotic fish that possesses underwater visual stability and good maneuverability. The existing visual navigation networks usually generate unexpected navigation instructions when dealing with complex or ambiguous underwater scenes. Additionally, image jitter caused by the rhythmic motion of robotic fish can lead to navigation failure. This paper suggests an integrated navigation framework that includes a biomimetic platform design, a visual stabilization controller, and an intelligent underwater navigation network. Specifically, a novel sphyrnidae-inspired robotic shark is designed as a new platform with superior motion performance. To enhance underwater visual stability, a nonlinear model predictive control-based visual stabilization controller is proposed. Furthermore, a deep stereo attention navigation network based on a parallax attention mechanism is proposed to improve the generalization of vision-based autonomous navigation. A series of underwater search experiments on the robotic shark demonstrate the effectiveness and superiority of the proposed navigation framework. Hopefully, our proposed methods can provide valuable guidance and support for universal underwater robot navigation to accomplish practical marine tasks, such as underwater rescue, resource exploitation, biological observation, and so on.

Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

AbstractThe autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

Advanced Decision Making and Motion Planning Framework for Autonomous Navigation in Unsignalized Intersections

Advanced Decision Making and Motion Planning Framework for Autonomous Navigation in Unsignalized Intersections

Improving Walking Path Generation Through Biped Constraint in Indoor Navigation System for Visually Impaired Individuals.

This paper introduces a walking path generation method specifically developed for the Smart Cane, which is a RNA (Robotic Navigation Assistance Device) aimed at enhancing indoor navigation for visually impaired individuals. The proposed approach combines the utilization of a LIPM (Linear Inverse Pendulum Model) and LFPC (Linear Foot Placement Controller) motion primitives to generate walking paths specifically designed for visually impaired individuals. The primary objective is to generate paths that conform to human motion constraints, thereby guaranteeing an efficient and natural navigation experience. Integrating autonomous navigation framework, the Smart Cane facilitates safe and effective guidance for visually impaired participants in the indoor environments. Furthermore, comparative experiments have been conducted to validate the effectiveness of the proposed method, providing evidence of its capability to generate walking paths that conform to human motion constraints. The experiment results indicate that the proposed walking path generation method is a promising solution to enhance the navigation experience of visually impaired individuals.

Social navigation framework for autonomous vehicle with hierarchical cyber-physical system architecture

An autonomous vehicle operating alongside humans should ideally have a high social capability, such as being able to communicate with humans, negotiate space, predict reactions, etc. This can be achieved by a prediction feature trained with diverse data on human behavior. Hierarchical cyber-physical system (CPS) architecture design, which sends the prediction feature to an external server while observing a limited operational design domain (ODD) and acquiring data continuously, has great potential to refine the training process as well as improve the performance. However, this architecture design requires the planning and prediction modules to be explicitly decoupled, which takes away from the recent success on social navigation. In this paper, we propose a novel autonomous navigation framework enabling social behavior while decoupling the planning and prediction modules to take advantage of the hierarchical CPS architecture. In the proposed framework, pedestrian trajectories are predicted as reactions to pre-generated candidates for an ego vehicle trajectory, and the ego vehicle trajectory is then selected to maximize mutual benefit to both the ego vehicle and surrounding pedestrians. We evaluated the proposed framework with simulations using the social force model and found that it was able to achieve the social behavior.

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses.

Deep reinforcement learning-aided autonomous navigation with landmark generators.

Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL's training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness.

Autonomous navigation of underactuated bipedal robots in height-constrained environments

Navigating a large-scaled robot in unknown and cluttered height-constrained environments is challenging. Not only is a fast and reliable planning algorithm required to go around obstacles, the robot should also be able to change its intrinsic dimension by crouching in order to travel underneath height-constrained regions. There are few mobile robots that are capable of handling such a challenge, and bipedal robots provide a solution. However, as bipedal robots have nonlinear and hybrid dynamics, trajectory planning while ensuring dynamic feasibility and safety on these robots is challenging. This paper presents an end-to-end autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore height-constrained environments. A vertically actuated spring-loaded inverted pendulum (vSLIP) model is introduced to capture the robot’s coupled dynamics of planar walking and vertical walking height. This reduced-order model is utilized to optimize for long-term and short-term safe trajectory plans. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory. The entire framework is tested and experimentally validated using a bipedal robot Cassie. This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.

An Unmanned Surface Vehicle (USV): Development of an Autonomous Boat with a Sensor Integration System for Bathymetric Surveys.

A reliable yet economical unmanned surface vehicle (USV) has been developed for the bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder to collect submerged topography, temperature, and wind speed and monitor the vehicle's status during prescribed path-planning missions. The main objective of this research is to provide a methodological framework to build an autonomous boat with independent decision-making, efficient control, and long-range navigation capabilities. Integration of sensors with navigation control enabled the automatization of position, orientation, and velocity. A solar power integration was also tested to control the duration of the autonomous missions. The results of the solar power compared favorably with those of the standard LiPO battery system. Extended and autonomous missions were achieved with the developed platform, which can also evaluate the danger level, weather circumstances, and energy consumption through real-time data analysis. With all the incorporated sensors and controls, this USV can make self-governing decisions and improve its safety. A technical evaluation of the proposed vehicle was conducted as a measurable metric of the reliability and robustness of the prototype. Overall, a reliable, economic, and self-powered autonomous system has been designed and built to retrieve bathymetric surveys as a first step to developing intelligent reconnaissance systems that combine field robotics with machine learning to make decisions and adapt to unknown environments.

Adaptive Autonomous Navigation of Multiple Optoelectronic Microrobots in Dynamic Environments

The optoelectronic microrobot is an advanced light-controlled micromanipulation technology which has particular promise for collecting and transporting sensitive microscopic objects such as biological cells. However, wider application of the technology is currently limited by a reliance on manual control and a lack of methods for simultaneous manipulation of multiple microrobotic actuators. In this letter, we present a computational framework for autonomous navigation of multiple optoelectronic microrobots in dynamic environments. Combining closed-loop visual-servoing, SLAM, real-time visual detection of microrobots and obstacles, dynamic path-finding and adaptive motion behaviors, this approach allows microrobots to avoid static and moving obstacles, and perform a range of tasks in real-world dynamic environments. The capabilities of the system are demonstrated through micromanipulation experiments in simulation and in real conditions using a custom built optoelectronic tweezer system.

Autonomous Magnetic Navigation Framework for Active Wireless Capsule Endoscopy Inspired by Conventional Colonoscopy Procedures

In recent years, simultaneous magnetic actuation and localization (SMAL) for active wireless capsule endoscopy (WCE) has been intensively studied to improve the efficiency and accuracy of gastrointestinal (GI) examination using WCE. In this letter, we draw inspiration from the conventional colonoscopy procedures and propose an autonomous navigation framework for active WCE based on SMAL technologies, in order to realize efficient and accurate navigation of a robotic capsule endoscope in unknown tubular environments (e.g., intestines) with minimal user effort. First, the capsule is actuated to explore the unknown tubular environment using an automatic propulsion algorithm, and a viable path to represent the environment is generated. Then, the capsule is navigated towards any user-selected point on the trajectory using a trajectory following algorithm, to allow accurate and repeated inspections of suspicious lesions. We implement the navigation framework on a robotic system incorporated with advanced SMAL algorithms, and validate it in the navigation experiments in various tubular environments including phantoms and ex-vivo pig colons. Our results demonstrate that the proposed framework can effectively navigate the capsule in unknown, complex tubular environments with a satisfactory accuracy, repeatability and efficiency.

Autonomous Navigation in Confined Waters - A COLREGs Rule 9 Compliant Framework

Fully or partial autonomous marine vessels are actively being developed by many industry actors. In many cases, the autonomous vessels will be operating close to shore, and within range of a Remote Control Center (RCC). Close to shore operation requires that the autonomous vessel is able to navigate in close proximity to other autonomous or manned vessels, and possibly in confined waters, while obeying the COLREGs on equal terms as any other vessel at sea. In confined waters however, certain COLREGs rules apply, which might alter the expected actions (give-way or stand-on), depending on the manoeuvrability of the vessels. This paper presents a Situation Awareness (SAS) framework for autonomous navigation that complies with COLREGs rule 9 (Narrow Channels). The proposed solution comprises a method for evaluating the manoeuvrability of a vessel in confined waters, for assessing the applicability of COLREGs rule 9. This feature is then integrated into an already existing SAS framework for facilitating COLREGs-compliant navigation in restricted waters. The applicability of the proposed method is demonstrated in simulation using a case study of a small autonomous passenger ferry.

Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment

Purpose This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment. Design/methodology/approach This paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map. Findings The result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field. Originality/value This paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments

Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator.

Autonomous navigation of MAVs in unknown cluttered environments

AbstractRecently, there have been many advances in the algorithms required for autonomous navigation in unknown environments, such as mapping, collision avoidance, trajectory planning, and motion control. These components have been integrated into drones with high‐end computers and graphics processors. However, further development is required to enable compute‐constrained platforms with such autonomous navigation capabilities. To address this issue, in this paper, we present an autonomous navigation framework for reaching a goal in unknown three‐dimensional cluttered environments. The framework consists of three main components. The first component is a computationally efficient method for mapping the environment from the disparity measurements obtained from a depth sensor. The second component is a stochastic approach to generate a path to a given goal, taking into account the field of view constraints on the space that is assumed to be safe for navigation. The third method is a fast algorithm for the online generation of motion plans, taking into account the robot's dynamic constraints, model and environmental uncertainty, and disturbances. We provide a qualitative and quantitative comparison with existing reaching a goal and exploration methods, showing the superior performance of our approach. Additionally, we present indoors and outdoors experiments using a robotic platform based on the Intel Ready to Fly drone kit, which represents the implementation, in the most computational constrained platform, of autonomous navigation in unknown cluttered environments demonstrated to date. Open source code is available at: https://github.com/IntelLabs/autonomousmavs. The video of the experimental results can be found in https://youtu.be/79IFfQfvXLE.

Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling

Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.

Real-Time Path Planning in Unknown Environments for Bipedal Robots

Autonomous navigation in dynamic and unknown environments requires real-time path planning. Solving the path planning problem for bipedal locomotion quickly and robustly is one of the main challenges in making humanoid robots competitive against mobile platforms. In this letter, we propose strategies to use mobile platform planners for improving the navigation of bipedal robots. These strategies combine advantageously continuous two-dimensional (2-D) paths with conventional step planners for humanoid robots. We introduce a mobile platform planner suitable for real-time navigation. It searches for multiple 2-D paths that makes the path planning more robust against limited calculation time and changing scenarios. It is combined with a step planner and integrated in the framework for autonomous navigation of our robot Lola . We evaluate different strategies in simulation and validate them in experiments in unknown dynamic environments.

A task-oriented and parameterized (semi) autonomous navigation framework for the development of simulation systems

Agent behaviors in simulation systems are related to fundamental capabilities of realistically developing (semi) autonomous navigation actions. This is particularly important when dealing with the implementation of Computer Generated Forces (CGFs) for simulation systems in tactical military training applications. Moreover, these systems take into consideration the particularities of the domain-specific simulation tasks and the numerous heterogeneous CGFs inserted on them in order to generate better knowledge and learning experience to simulation system users. Based on these reasons, this paper reviews recurrent navigation problems as to propose a task-oriented and parameterized (semi) autonomous navigation framework to deal with CGF navigation needs in military simulation. Combining global and local navigation techniques, and controlled transition between alternative degrees of navigation autonomy, the framework aims to overcome the challenges of implementing customizable CGF navigation behaviors and, at the same time, to allow interaction with both users and other simulation systems in distributed simulation settings. A case study is presented in which the proposed techniques are analyzed in a domain-specific simulation problem providing evidence of their suitability to address the studied military simulation problems.