Robot Navigation Research Articles

Robotic dog for navigation of a rehabilitation wheelchair robot in a highly constrained environment

Robotic dog for navigation of a rehabilitation wheelchair robot in a highly constrained environment

Online path tracking with an integrated H∞ robust adaptive controller for a double-Ackermann steering robot for orchard waypoint navigation

Online path tracking with an integrated H∞ robust adaptive controller for a double-Ackermann steering robot for orchard waypoint navigation

Probabilistic Multi-Robot Task Scheduling for the Antarctic Environments with Crevasses

This paper deals with the problem of multi-robot task scheduling in the Antarctic environments with crevasses. Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be planned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, the asymmetry of sensor data, and the possibility of crevasses drifting irregularly as time passes. To overcome these uncertain and asymmetric problems, this paper proposes a probabilistic multi-robot task scheduling method based on the Nearest Neighbors Test (NNT) algorithm and the probabilistic modeling of the positions of crevasses. The proposed method was tested with a Google map of the Antarctic environments and showed a better performance than the Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA) in the context of total cost and computational time.

A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments

Abstract In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Graph network-based human movement prediction for socially-aware robot navigation in shared workspaces

AbstractMethods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments.

Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

VLAI: Exploration and Exploitation based on Visual-Language Aligned Information for Robotic Object Goal Navigation

Object Goal Navigation(ObjectNav) is the task that an agent need navigate to an instance of a specific category in an unseen environment through visual observations within limited time steps. This work plays a significant role in enhancing the efficiency of locating specific items in indoor spaces and assisting individuals in completing various tasks, as well as providing support for people with disabilities. To achieve efficient ObjectNav in unfamiliar environments, global perception capabilities, understanding the regularities of space and semantics in the environment layout are significant. In this work, we propose an explicit-prediction method called VLAI that utilizes visual-language alignment information to guide the agent's exploration, unlike previous navigation methods based on frontier potential prediction or egocentric map completion, which only leverage visual observations to construct semantic maps, thus failing to help the agent develop a better global perception. Specifically, when predicting long-term goals, we retrieve previously saved visual observations to obtain visual information around the frontiers based on their position on the incrementally built incomplete semantic map. Then, we apply our designed Chat Describer to this visual information to obtain detailed frontier object descriptions. The Chat Describer, a novel automatic-questioning approach deployed in Visual-to-Language, is composed of Large Language Model(LLM) and the visual-to-language model(VLM), which has visual question-answering functionality. In addition, we also obtain the semantic similarity of target object and frontier object categories. Ultimately, by combining the semantic similarity and the boundary descriptions, the agent can predict the long-term goals more accurately. Our experiments on the Gibson and HM3D datasets reveal that our VLAI approach yields significantly better results compared to earlier methods. The code is released athttps://github.com/31539lab/VLAI.

Research on shared control of robots based on hybrid brain-computer interface

BackgroundWith the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain–computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control. New methodsThis paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion. ResultsEight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations. ConclusionsThe HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

Long-term outcome of robotic-guided closed reduction internal fixation for Delbet II femoral neck fractures in children

ObjectiveTo retrospectively analyze and assess the long-term effectiveness of robotic navigation and traditional closed reduction internal fixation in the treatment of Delbet type II femoral neck fracture in children.MethodsA total of fifty-five patients diagnosed with pediatric Delbet type II femoral neck fracture, who were admitted to Foshan Hospital of Traditional Chinese Medicine between January 2018 and June 2022, were included in this study. Among them, 22 cases of nailing under robotic navigation were set as the observation group, and 33 cases of nailing under fluoroscopy of the C-arm machine were set as the control group. All patients had their femoral neck fractures closed and repositioned first. After confirming the satisfactory fracture repositioning under the fluoroscopic view of the C-arm machine, internal fixation was performed by inserting hollow compression screws in the corresponding surgical way.A comparative analysis was conducted between the two groups to assess the disparity in the amount of X-ray exposure during surgery, the number of guide pins inserted, and the duration of the surgical procedure. The quality of comparative fracture reduction was assessed according to the Haidukewych criteria on the first postoperative hip X-ray, and the parallelism and distribution of the comparative screws were measured. The incidence of hip function and postoperative complications according to the Ratliff criteria were evaluated between each of the subgroups at the final follow-up.ResultsComparison of general information, operation duration, and quality of fracture reduction between the two groups failed to reveal statistically significant results (P > 0.05). The observation group had a lower number of X-ray exposures and guide pin placements compared to the control group, and this difference was statistically significant (P < 0.05).At the last follow-up, the observation group exhibited superior screw parallelism and distribution, as well as hip joint function, compared to the control group, and this difference was statistically significant (P < 0.05). The incidence of complications in the observation group was lower than that in the control group; however, the difference was not statistically significant (P > 0.05).ConclusionClosed reduction and internal fixation under orthopedic robot navigation can achieve better long-term efficacy in treating Delbet type II femoral neck fracture in children.

Origami-Inspired Vacuum-Actuated Foldable Actuator Enabled Biomimetic Worm-like Soft Crawling Robot.

The development of a soft crawling robot (SCR) capable of quick folding and recovery has important application value in the field of biomimetic engineering. This article proposes an origami-inspired vacuum-actuated foldable soft crawling robot (OVFSCR), which is composed of entirely soft foldable mirrored origami actuators with a Kresling crease pattern, and possesses capabilities of realizing multimodal locomotion incorporating crawling, climbing, and turning movements. The OVFSCR is characterized by producing periodically foldable and restorable body deformation, and its asymmetric structural design of low front and high rear hexahedral feet creates a friction difference between the two feet and contact surface to enable unidirectional movement. Combining an actuation control sequence with an asymmetrical structural design, the body deformation and feet in contact with ground can be coordinated to realize quick continuous forward crawling locomotion. Furthermore, an efficient dynamic model is developed to characterize the OVFSCR's motion capability. The robot demonstrates multifunctional characteristics, including crawling on a flat surface at an average speed of 11.9 mm/s, climbing a slope of 3°, carrying a certain payload, navigating inside straight and curved round tubes, removing obstacles, and traversing different media. It is revealed that the OVFSCR can imitate contractile deformation and crawling mode exhibited by soft biological worms. Our study contributes to paving avenues for practical applications in adaptive navigation, exploration, and inspection of soft robots in some uncharted territory.

Comparison of robotic system and dynamic navigation for zygomatic implant placement: An in vitro study

ObjectivesTo compare the accuracy of robotic and dynamic navigation systems in assisting zygomatic implant (ZI) using an in vitro model experiment. MethodsPreoperative cone-beam computed tomography (CBCT) images of patients who underwent ZI treatment between 2011 and 2023 were collected from local databases. Corresponding three-dimensional resin models were printed and assigned to two groups: the robotic and dynamic navigation system groups. Following preoperative plans, ZIs were placed in the models with the assistance of either a robotic or dynamic navigation system. Deviations in the in vitro navigation surgery were measured and compared between the groups. ResultsA total of 110 ZIs were placed in 56 models, with 55 ZIs in each group. No significant differences were observed in entry and angle deviations between the groups (p>0.05). However, the exit deviation in the robotic system group (2.39±1.24 mm) was larger than that in the dynamic navigation group (1.83±1.25 mm) (p<0.05). On the exit side, the Z-axis deviation in the robotic group (left: -0.28±1.43 mm, right: -0.21±1.30 mm) was smaller than that in the dynamic navigation group (left: 0.76±1.11 mm, right: 0.85±1.52 mm) (p<0.05), while no significant differences were found in X- and Y-axis deviations (p>0.05). ConclusionsCompared with the dynamic navigation system, the robotic system can effectively prevent ZI overextension. However, its accuracy on the exit side is slightly lower than that of the dynamic navigation system. Clinical SignificanceThis preliminary in vitro study showed that the accuracy of the robotic system was slightly inferior to that of the dynamic navigation system in terms of exit deviation when used in ZI placement. Further clinical studies are required to confirm these findings.

Optimize data association of point cloud to improve the quality of mapping and positioning

Purpose Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop. Design/methodology/approach In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching. Findings Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization. Originality/value The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.

Swarm Robotics Navigation Task: A Comparative Study of Reinforcement Learning and Particle Swarm Optimization Methodologies

Automatic design methods focus on generating the collective behavior of swarm robotic systems. These methods enable multiple robots to coordinate and execute complex tasks in their environments autonomously. This research paper investigated two prominent methodologies: particle swarm optimization (PSO) and reinforcement learning (RL). A new comparative study was conducted to analyze the performance of a group of mobile robots through extensive experimentation. The objective was to produce navigational collective behavior through unknown environments. These environments differ in complexity ranging from obstacle-free environments to cluttered ones. The core metrics of the comparison include the time efficiency of individual robots and the overall swarm, flexibility in pathfinding, and the ability to generalize solutions for new environments. The obtained results from the Webots simulator with Python controller suggested that RL excels in environments closely aligned with its training conditions. RL achieved a faster completion time and demonstrated superior coordination among individual robots. However, its performance dips when facing untrained scenarios necessitating computationally expensive retraining or structural complexities to enhance adaptability. Conversely, PSO showed commendable consistency in performance. Despite its slower pace, it exhibited robustness in various challenging settings without reconfiguration.

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6–9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection.

Low-Cost Real-Time Localisation for Agricultural Robots in Unstructured Farm Environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach with Topological Memory Configuration

Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach with Topological Memory Configuration

The Impact of Robotic Assistance-Based Navigation Systems on the Adequacy of EBUS-Guided Cytology Specimens - Experience From a Large University Hospital

The Impact of Robotic Assistance-Based Navigation Systems on the Adequacy of EBUS-Guided Cytology Specimens - Experience From a Large University Hospital

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.