Complex Unstructured Environments Research Articles

An Autonomous Navigation Method for Planetary Rover Based on Multi-modal Fusion and Multi-factor Graph Optimization

Planetary surface is a complex unstructured environment with strong light, strong shadows, weak textures and numerous obstacles. There is no GPS signal support and satellite networking support on the planetary surface. The limited computer and sensor capabilities of planetary rover have a significant gap from the ground autonomous driving configuration, which presents a great challenge to the autonomous navigation methods of planetary rover. Extreme lighting conditions will increase the camera mismatching rate, and poor texture will increase the lidar matching error, which will reduce the estimation accuracy. Moreover, camera and lidar produce great motion distortion in the rugged terrain and bumpy environment, which leads to a large increase in the cumulative error of odometer. Camera and lidar are not suitable as the main sensor of SLAM algorithm in unstructured environment because they are sensitive to the change of environment. Therefore, an autonomous navigation method for planetary rover based on multi-modal fusion and multi-factor graph optimization is presented in this paper. The IMU (Inertial Measurement Unit) odometer node is added to the lidar odometer and visual odometer thread, and the IMU is taken as the central node to build a multi-factor graph optimization model. A multi-factor graph optimization model and a strong adaptive constraint strategy with reasonable weights are constructed. The pose estimation of other sensors is used to constrain IMU bias, meanwhile historical rut tracking, obstacle contour matching and skyline feature matching are introduced. Finally, motion prediction is achieved in the IMU odometer node. Simulation results and field tests show that this method can effectively cope with the unstructured environment on planetary surface, which achieves adaptive and robust autonomous navigation of planetary rover.

A Biohybrid Self-Dispersing Miniature Machine Using Wild Oat Fruit Awns for Reforestation and Precision Agriculture.

Advances in bioinspired and biohybrid robotics are enabling the creation of multifunctional systems able to explore complex unstructured environments. Inspired by Avena fruits, a biohybrid miniaturized autonomous machine (HybriBot) composed of a biomimetic biodegradable capsule as cargo delivery system and natural humidity-driven sister awns as biological motors is reported. Microcomputed tomography, molding via two-photon polymerization and casting of natural awns into biodegradable materials is employed to fabricate multiple HybriBots capable of exploring various soil and navigating soil irregularities, such as holes and cracks. These machines replicate the dispersal movements and biomechanical performances of natural fruits, achieving comparable capsule drag forces up to ≈0.38 N and awns torque up to ≈100 mN mm-1. They are functionalized with fertilizer and are successfully utilized to germinate selected diaspores. HybriBots function as self-dispersed systems with applications in reforestation and precision agriculture.

A Review of Soft Crawling Robots with Different Driving Methods

Background: Traditional rigid robots are difficult to adapt to complex unstructured environments due to their limited degree of freedom and lack of flexibility. Therefore, soft crawling robots are concerned widely by their powerful deformation ability, infinite number of degrees of freedom, and effective interaction with humans. Objective: This paper aims to report the recent progress of soft crawling robots and provide a reference for readers in this field. Methods: By reading and summarizing the patents and papers related to soft crawling robots in recent years, they are divided into three categories according to different driving methods. The structure, motion mechanism, characteristics, and applications of each class of robots are compared and analyzed. Results: The advantages and disadvantages of each driving method are analyzed, and the key issues in soft crawling robots are pointed out. Based on this, the future development direction of this research field is predicted. Conclusion: The study shows that according to the driving method, soft crawling robots are classified as pressure driven, motor-wire driven, and soft active material driven. In addition, the characteristics of each drive are summarized. In the future, soft crawling robots will have more potential applications in biomedicine, outdoor survey, rescue search, and inspection and maintenance of equipment.

Path planning with obstacle avoidance for soft robots based on improved particle swarm optimization algorithm

Soft-bodied robots have the advantages of high flexibility and multiple degrees of freedom and have promising applications in exploring complex unstructured environments. Kinematic coupling exists for the soft robot in a problematic space environment for motion planning between the soft robot arm segments. In solving the soft robot inverse kinematics, there are only solutions or even no solutions, and soft robot obstacle avoidance control is tough to exist, as other problems. In this paper, we use the segmental constant curvature assumption to derive the positive and negative kinematic relationships and design the tip self-growth algorithm to reduce the difficulty of solving the parameters in the inverse kinematics of the soft robot to avoid kinematic coupling. Finally, by combining the improved particle swarm algorithm to optimize the paths, the convergence speed and reconciliation accuracy of the algorithm are further accelerated. The simulation results prove that the method can successfully move the soft robot in complex space with high computational efficiency and high accuracy, which verifies the effectiveness of the research.

Lightweight Algorithm for Apple Detection Based on an Improved YOLOv5 Model.

The detection algorithm of the apple-picking robot contains a complex network structure and huge parameter volume, which seriously limits the inference speed. To enable automatic apple picking in complex unstructured environments based on embedded platforms, we propose a lightweight YOLOv5-CS model for apple detection based on YOLOv5n. Firstly, we introduced the lightweight C3-light module to replace C3 to enhance the extraction of spatial features and boots the running speed. Then, we incorporated SimAM, a parameter-free attention module, into the neck layer to improve the model's accuracy. The results showed that the size and inference speed of YOLOv5-CS were 6.25 MB and 0.014 s, which were 45 and 1.2 times that of the YOLOv5n model, respectively. The number of floating-point operations (FLOPs) were reduced by 15.56%, and the average precision (AP) reached 99.1%. Finally, we conducted extensive experiments, and the results showed that the YOLOv5-CS outperformed mainstream networks in terms of AP, speed, and model size. Thus, our real-time YOLOv5-CS model detects apples in complex orchard environments efficiently and provides technical support for visual recognition systems for intelligent apple-picking devices.

Design and performance analysis of multi-wheeled lateral and longitudinal rocker arm series-parallel robot

The six-wheeled rocker robot and the eight-wheeled rocker robot have strong obstacle-surmounting and terrain adaptability, which are widely used in lunar and Mars exploration, military operations, and field rescue. However, for the terrain environment with low friction coefficient and more complex unstructured terrain environments such as ice and snow roads, the six-wheeled rocker robot and the eight-wheeled rocker robot are not enough, so the rocker arm robot with stronger ability to overcome obstacles and adapt to the terrain is needed. In order to solve the above technical problems, this paper innovatively designs a multi-wheeled lateral and longitudinal rocker arm series-parallel robot. Firstly, Creo3.0 is used to create a new three-dimensional model of a series-parallel robot with multi-wheeled lateral and longitudinal arms. Then, the 3D model of the robot is imported into Adams2018 to build a virtual prototype simulation model. Finally, through the construction and simulation analysis of various obstacle-surmounting terrain environments such as vertical obstacles, slopes and potholes, the adaptability, obstacle-surmounting, safety and maneuverability of the robot to different terrain environments are studied. The conclusions obtained in this paper can provide a reference for the innovative design of the obstacle-crossing mechanism of the multi-wheeled lateral and longitudinal rocker arm series-parallel robot, and the improvement of the obstacle-crossing performance, adaptability, maneuverability and safety performance on complex terrain.

Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning

<p>How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artificial potential field algorithm based on reinforcement learning. Firstly, use the traditional artificial potential field method to perform basic path planning for the robot. Secondly, in order to solve the local minimum problem in planning and improve the robot’s adaptive ability, the reinforcement learning algorithm is run by fixing preset parameters on the simulation platform. After intensive training, the robot continuously improves the decision-making ability of crossing typical concave obstacles. Finally, through simulation experiments, it is concluded that the rescue robot can combine the artificial potential field method and reinforcement learning to improve the ability to adapt to the environment, and can reach the destination with the optimal route.</p> <p> </p>

Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing.

A high resolution dataset is one of the prerequisites for tea chrysanthemum detection with deep learning algorithms. This is crucial for further developing a selective chrysanthemum harvesting robot. However, generating high resolution datasets of the tea chrysanthemum with complex unstructured environments is a challenge. In this context, we propose a novel tea chrysanthemum – generative adversarial network (TC-GAN) that attempts to deal with this challenge. First, we designed a non-linear mapping network for untangling the features of the underlying code. Then, a customized regularization method was used to provide fine-grained control over the image details. Finally, a gradient diversion design with multi-scale feature extraction capability was adopted to optimize the training process. The proposed TC-GAN was compared with 12 state-of-the-art generative adversarial networks, showing that an optimal average precision (AP) of 90.09% was achieved with the generated images (512 × 512) on the developed TC-YOLO object detection model under the NVIDIA Tesla P100 GPU environment. Moreover, the detection model was deployed into the embedded NVIDIA Jetson TX2 platform with 0.1 s inference time, and this edge computing device could be further developed into a perception system for selective chrysanthemum picking robots in the future.

Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model.

Medicinal chrysanthemum detection is one of the desirable tasks of selective chrysanthemum harvesting robots. However, it is challenging to achieve accurate detection in real time under complex unstructured field environments. In this context, we propose a novel lightweight convolutional neural network for medicinal chrysanthemum detection (MC-LCNN). First, in the backbone and neck components, we employed the proposed residual structures MC-ResNetv1 and MC-ResNetv2 as the main network and embedded the custom feature extraction module and feature fusion module to guide the gradient flow. Moreover, across the network, we used a custom loss function to improve the precision of the proposed model. The results showed that under the NVIDIA Tesla V100 GPU environment, the inference speed could reach 109.28 FPS per image (416 × 416), and the detection precision (AP50) could reach 93.06%. Not only that, we embedded the MC-LCNN model into the edge computing device NVIDIA Jetson TX2 for real-time object detection, adopting a CPU–GPU multithreaded pipeline design to improve the inference speed by 2FPS. This model could be further developed into a perception system for selective harvesting chrysanthemum robots in the future.

Towards Automated Sample Collection and Return in Extreme Underwater Environments

In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in searchfor- life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators.We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.

Robustness of collective scenting in the presence of physical obstacles

Honey bees (Apis mellifera L.) aggregate around the queen by collectively organizing a communication network to propagate volatile pheromone signals. Our previous study shows that individual bees “scent” to emit pheromones and fan their wings to direct the signal flow, creating an efficient search and aggregation process. In this work, we introduce environmental stressors in the form of physical obstacles that partially block pheromone signals and prevent a wide open path to the queen. We employ machine learning methods to extract data from the experimental recordings, and show that in the presence of an obstacle that blocks most of the path to the queen, the bees need more time but can still effectively employ the collective scenting strategy to overcome the obstacle and aggregate around the queen. Further, we increase the complexity of the environment by presenting the bees with a maze to navigate to the queen. The bees require more time and exploration to form a more populated communication network. Overall, we show that given volatile pheromone signals and only local communication, the bees can collectively solve the swarming process in a complex unstructured environment with physical obstacles and hit a limit when the obstacle is a much more complex maze.

Modeling and motion control of an octopus-like flexible manipulator actuated by shape memory alloy wires

Soft robots are concerned by researchers due to several characteristics, such as high adaptability in complex unstructured environments, safe interaction with environments, and a high degree of dexterity. The deformability and dexterity of the octopus arm are remarkable and interesting for the development of bioinspiration soft robots. In this study, we proposed a biomimetic flexible manipulator actuated by shape memory alloy (SMA) wires and its PI controller, and evaluated its performances with focused experiments. This study aims to find out the advantages of the developed manipulator actuated by SMA wires. We designed the bionic structure to achieve flexible bending in 3D space based on the analysis of the octopus muscle structure. Then we built the mathematical models that described the relationship between the bending angle and the driving parameters. Surprisingly, the soft movement capacity of the bionic manipulator was investigated at different heating voltages and PWM duty cycles by the PI controller based on the self-sensing resistance feedback. The soft manipulator was able to bend flexibly with the maximum bending angle of 60°. Compared to the traditional soft arm, the soft manipulator has higher accuracy, with the error of the deflection angle less than 5° and the errors of bending angles less than 2°.

Limpet II: A Modular, Untethered Soft Robot.

The ability to navigate complex unstructured environments and carry out inspection tasks requires robots to be capable of climbing inclined surfaces and to be equipped with a sensor payload. These features are desirable for robots that are used to inspect and monitor offshore energy platforms. Existing climbing robots mostly use rigid actuators, and robots that use soft actuators are not fully untethered yet. Another major problem with current climbing robots is that they are not built in a modular fashion, which makes it harder to adapt the system to new tasks, to repair the system, and to replace and reconfigure modules. This work presents a 450 g and a 250 × 250 × 140 mm modular, untethered hybrid hard/soft robot—Limpet II. The Limpet II uses a hybrid electromagnetic module as its core module to allow adhesion and locomotion capabilities. The adhesion capability is based on negative pressure adhesion utilizing suction cups. The locomotion capability is based on slip-stick locomotion. The Limpet II also has a sensor payload with nine different sensing modalities, which can be used to inspect and monitor offshore structures and the conditions surrounding them. Since the Limpet II is designed as a modular system, the modules can be reconfigured to achieve multiple tasks. To demonstrate its potential for inspection of offshore platforms, we show that the Limpet II is capable of responding to different sensory inputs, repositioning itself within its environment, adhering to structures made of different materials, and climbing inclined surfaces.

Human-robot skills transfer interface for UAV-based precision pesticide in dynamic environments

Purpose Nowadays, the global agricultural system is highly dependent on the widespread use of synthetic pesticides to control diseases, weeds and insects. The unmanned aerial vehicle (UAV) is deployed as a major part of integrated pest management in a precision agriculture system for accurately and cost-effectively distributing pesticides to resist crop diseases and insect pests. Design/methodology/approach With multimodal sensor fusion applying adaptive cubature Kalman filter, the position and velocity are enhanced for the correction and accuracy. A dynamic movement primitive is combined with the Gaussian mixture model to obtain numerous trajectories through the teaching of a demonstration. Further, to enhance the trajectory tracking accuracy under an uncertain environment of the spraying, a novel model reference adaptive sliding mode control approach is proposed for motion control. Findings Experimental studies have been carried out to test the ability of the proposed interface for the pesticides in the crop fields. The effectiveness of the proposed interface has been demonstrated by the experimental results. Originality/value To solve the path planning problem of a complex unstructured environment, a human-robot skills transfer interface is introduced for the UAV that is instructed to follow a trajectory demonstrated by a human teacher.

Internet of Things Applied on Assistive Robotics

With the Americans with Disabilities Act (ADA), the past two decades have seen a proliferation of Assistive Technology (AT) and its enabling impact on the lives of people with disabilities in the areas of accessing information, communication, and daily living activities. Due to recent emergence of the Internet of Things (IoT), the research of assistive robotics has been contributing to assisting humans to manipulate and communicate with the robot in complex unstructured environments. The ongoing revolution of Internet of Things (IoT), together with the growing diffusion of robots applied in everyday life and industry, makes Internet of Robotic Things (IoRT) as the future direction for assistive robotics research. New advanced technologies and services are explored in assisting humans. This study provides an overview of the IoT and applications into robotics based on the building blocks of the IoT, along with recent trends and issues relevant to accessing technology for people with disabilities. This research also discusses the technologies in IoT that would benefit the applications of assistive robotics. The most important research challenges to be faced are also highlighted.

Modelling of a soft multi-chambered climbing robot and experiments

Soft robots have wide potential applications prospects in unstructured environments owing to their being able to imitate forms of motion of creatures in climbing and creeping through small spaces. By utilising the high flexibility of soft materials, a pneumatic soft climbing robot was designed. At first, a model for soft climbing robots with a stiffness gradient was designed according to the drive mode of pneumatic networks in soft robots. Afterwards, the visco-mechanical properties of robots at the contact surface were analysed and also the deformation characteristics of cavities were discussed by using the method of minimum potential energy. Subsequently, through simulation and use of the finite element method, the optimal number of cavities in an actuator required by a climbing robot was calculated and also the climbing behaviours of the robot were analysed. Finally, by employing 3D printing and layer-by-layer casting, a prototype soft climbing robot was prepared to perform climbing experiments. The research is expected to provide a new method for monitoring complex unstructured environments.

A Hybrid Hand–Eye Calibration Method for Multilink Cable-Driven Hyper-Redundant Manipulators

Multilink cable-driven hyper-redundant manipulators (MCDHMs) have a slender body and flexible movement characteristics, which are very suitable for complex unstructured environments. Since MCDHMs have multiple coupling between active cables, linkage cables, joints, and the end-effector, the mathematical modeling of the segmented linkage MCDHM becomes more complicated, and the uncertainty of its model makes the motion accuracy of the end-effector become low. Traditional calibration methods that rely solely on eye-in-hand or eye-to-hand calibration method to calibrate the MCDHM model will produce relatively large errors. This article proposes a hybrid hand–eye calibration method, which eliminates the errors introduced by inaccurate kinematics models through the fusion modeling of eye-in-hand and eye-to-hand. First, for unstructured application scenarios, the hybrid calibration framework under the condition of large model errors is introduced, and then the multiple kinematics model of the MCDHM is derived. Then, two different types of hybrid hand–eye calibration equations are derived and solved by the recursive iteration method. Furthermore, according to the hand–eye calibration result, the MCDHM kinematic parameters are calibrated by the particle swarm optimization (PSO) method. Finally, in order to demonstrate the feasibility and superiority of the hybrid calibration method, experiments are carried out on the real MCDHM to further verify the proposed method. The experimental results show that the proposed hybrid calibration method has higher calibration accuracy.

Self-Sensing Pneumatic Compressing Actuator.

Using soft pneumatic actuator is a feasible solution in the complex unstructured environment, owing to their inherent compliance, light weight, and safety. However, due to the limitations of soft actuators' materials and structures, they fall short of motion accuracy and load capacity, or need large-size, bulky compressors. Meanwhile, in order to gain better control, it is essential for them to sense the environments as well. This leads to high-price sensors or a complicated manufacture technique. Here, a self-sensing vacuum soft actuation structure is proposed, aiming at acquiring good balance among precision, output force, and actuation pressure. The actuator mainly comprises a flexible membrane and a compression spring. When actuated, the flexible membrane outside the actuator compresses the internal spring skeleton, realizing large contractile motion in axial direction. Its built-in force sensor can indirectly measure the absolute displacement of the actuator with certain accuracy (about 5% F.S.). Besides, it does not require high actuation pressure to generate enough output force. The actuator is quite easy to manufacture with low cost, and there are a variety of materials to choose from. We established quasi-static models for actuators built of two different kinds of membrane materials, and tested their accuracy and output force. In addition, to break through the limits of vacuum actuation, a method of positive-negative pressure combined actuation has been proposed, which lowers the requirements for air source equipments, increases actuation pressure, and reduces potential safety threats at the same time. This kind of soft actuators can also effectively resist and detect impacts. The design of a two-finger dexterous robot hand and robot joint based on this soft actuator illustrates its broad application prospects in the fields of mobile robots, wearable devices, and human–robot interaction.

Collision Free Smooth Path for Mobile Robots in Cluttered Environment Using an Economical Clamped Cubic B-Spline

Mobile robots have various applications in agriculture, autonomous cars, industrial automation, planetary exploration, security, and surveillance. The generation of the optimal smooth path is a significant aspect of mobile robotics. An optimal path for a mobile robot is measured by various factors such as path length, path smoothness, collision-free curve, execution time, and the total number of turns. However, most of the planners generate a non-smooth less optimal and linear piecewise path. Post processing smoothing is applied at the cost of increase in path length. Moreover, current research on post-processing path smoothing techniques does not address the issues of post smoothness collision and performance efficiency. This paper presents a path smoothing approach based on clamped cubic B-Spline to resolve the aforementioned issues. The proposed approach has introduced an economical point insertion scheme with automated knot vector generation while eliminating post smoothness collisions with obstacles. It generates C2 continuous path without any stitching point and passes more closely to the originally planned path. Experiments and comparison with previous approaches have shown that the proposed approach generates better results with reduced path length, and execution time. The test cases used for experiments include a simple structure environment, complex un-structured environment, an environment full of random cluttered narrow obstacles, and a case study of an indoor narrow passage.

A fast rotary soft actuator driven by piezoelectric vibration

<p indent="0mm">The soft actuator is a soft driving device exhibiting large-deformation ability, which is significantly different from the traditional rigid actuator usually composed of rigid components such as motors, bearings, gears, hinges and so on. Soft actuators are usually made of soft materials; theoretically, they have the advantages of infinite motion freedom, continuous deformation, and compliant contact. Therefore, the soft actuator shows remarkable advantages in grasping and handling of fragile objects or motion in complex unstructured environments. However, most of the previous soft actuators are usually made of the forms of bending, stretching, and twisting deformation, for which it is hard to achieve continuous rotation. Some limbless creatures, such as snakes and worms, have bristles arranged periodically at a certain angle in nature. When sliding between the bristles and ground caused by vibration of the bristles or deformation of the soft body, the asymmetrical friction force is generated which can drive their forward motion. Based on this idea, an initiative rotating soft actuator, comprised of a piezoelectric bimorph and a soft rotating structure, which is driven by piezoelectric vibration is proposed in this paper. This actuator is composed of a piezoelectric bimorph and a soft rotary structure. The soft rotary structure is an array of inclined bristles arranged uniformly along the circumferential direction, fabricated by 3D printing and injection molding technology. By analyzing the friction force of a single bristle during vibration, the formula of the rotation moment is given. In addition, through finite element analysis of the deformation of the bristles, the contact force and friction force of the actuator in one vibration period are obtained. It shows that the static friction force generated during the downward motion of the bristles is larger than the sliding friction generated during the upward motion of the bristles, so that a net driving rotation moment is gotten. Besides, the effects of the actuation amplitude and frequency of the applied actuation force on the rotation speed of the actuator are studied by finite element analysis, based on which an optimized bristle geometry of <italic>θ</italic>=60°, <italic>l</italic><sc>=7 mm,</sc> and <italic>d</italic><sc>=1.6 mm</sc> is selected for subsequent experiments. The effects of voltage and frequency on the rotation performance of the rotary soft actuator are analyzed through experiments, which show that the rotation speed increases first and then decreases with the increasing of actuation frequency when the applied frequency exceeds <sc>700 Hz.</sc> Furthermore, when the applied voltage <italic>V</italic>_P-P is <sc>400 V</sc> and frequency is <sc>1400 Hz,</sc> the rotation speed can reach <sc>118.3 r/min,</sc> which is larger than the rotation speed of the soft actuator reported so far.