Navigation Trajectory Research Articles

A comparative study of multi-tentacled underwater robot with different self-steering behaviors: Maneuvering and cruising modes

Based on the concept of same structure but different laws, we propose two driving modes, maneuvering and cruising, using multiple tentacles of cephalopods as biomimetic prototypes. These two modes are distinguished by transient or continuous kinematic laws and can achieve self-steering behaviors with different features. The computational evolution process between this underwater robot and the flow field is solved on the OpenFOAM platform. We nest the secondary developed solver with dynamic overlapping mesh technology and integrate multiple functional modules. The numerical results show that for the maneuvering mode, the robot achieves rapid turning by collectively generating high-intensity pressure and vorticity fields during the upstroke of tentacles. This mode is suitable for application scenarios that require real-time direction adjustment, such as obstacle avoidance and emergency response. For the cruising mode, the robot relies on continuous asymmetric swing of their tentacles to generate stable yaw moment, and the navigation trajectory presents a fan-shaped pattern with serrated edges. This mode is suitable for purposeful application scenarios such as anti-interference stability and advance prediction.

A Clustering Approach to Unveil User Similarities in 6-DoF Extended Reality Applications

The advent in our daily life of Extended Reality (XR) technologies, such as Virtual and Augmented Reality, has led to the rise of user-centric systems, offering higher level of interaction and presence in virtual environments. In this context, understanding the actual interactivity of users is still an open challenge and a key step to enabling user-centric system. In this work, our goal is to construct an efficient clustering tool for 6 Degree-of-Freedom (DoF) navigation trajectories by extending the applicability of existing behavioural tool. Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart, highlighting the main differences and novelties. Then, we investigate new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. More concretely, we define and compare 11 similarity metrics which are based on different distance features ( i.e. , user positions in the 3D space, user viewing directions) and distance measurements ( i.e. , Euclidean, Geodesic, angular distance). Our solutions are validated and tested on real navigation paths of users interacting with dynamic volumetric media in both 6-DoF Virtual Reality and Augmented Reality conditions. Results show that metrics based on both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Such easy-to-use but robust metrics allow us to answer a fundamental question for user-centric systems: “how do we detect if users look at the same content in 6-DoF?”, opening the gate to new solutions based on users interactivity, such as viewport prediction, live streaming services optimised based on users behaviour but also for user-based quality assessment methods.

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.

Research on High-Pressure Water Jet Interference for Collision Prevention of Waterway Viaduct Piers: Case Study of Guangzhou Lixinsha Bridge

In this paper, with the frequent occurrence of ship–bridge collision accidents as the context and the collision accident of the Lixinsha Bridge in China as the background, the scenario of a ship impacting a pier was simulated using ANSYS-FLUENT software, and the practical application possibility of the high-pressure water jet interference (HPWJI) anti-collision method was thoroughly investigated. Through the simulation analysis, the effectiveness of a high-pressure water jet with a total flow rate of 45 m3/s in altering the navigation direction of large-tonnage (2000 t) ships and avoiding obstacles was verified. Additionally, its impact on the stress of the ship steel plates and navigation status was also explored. It was found that, with reasonable layout and parameter adjustment, the high-pressure water jet technology could effectively intervene in the ship’s navigation trajectory while ensuring the structural safety of the ship, with minimal impact on the ship’s navigation stability and passenger comfort. Furthermore, the injection angle of the high-pressure water jet had a significant impact on the deflection and deceleration of the ship. Specifically, when the water jet impacted the ship along its forward direction, it could effectively increase the ship’s deceleration and deflection time, reducing the speed from 2.55 m/s to 1.7 m/s, a decrease of approximately 33%, significantly enhancing collision prevention effectiveness. This research provides important guidance for the practical application of high-pressure water jet collision prevention technology and is of great significance for improving the safety of waterway transportation.

Combining robotics and 3D printing facilitates closed reduction of humeral shaft fractures using a minimally invasive plate as a reduction template: A proof-of-concept study.

Minimally invasive percutaneous plate osteosynthesis for humeral shaft fractures (HSFs) has limitations due to malreduction and radiation exposure. To address these limitations, we integrated robotics and 3D printing by incorporating plates as reduction templates. The innovative technology facilitated closed reduction of HSFs in the operating theatre using 18 models with cortical marking holes. The dataset of the precontoured plate was imported into 3D planning software for virtual fixation and screw path planning. The models were divided into half to simulate transverse fractures. During the operation, the software generated drilling trajectories for robot navigation, and precise plate installation achieved automatic fracture reduction. The evaluation results of reduction accuracy revealed variations in length, apposition, alignment, and rotation that meet the criteria for anatomic reduction. High interoperator reliabilities were observed for all parameters. The proposed technology achieved anatomic reduction in simulated bones.

Development and field evaluation of a VR/AR‐based remotely controlled system for a two‐wheel paddy transplanter

AbstractOperating a two‐wheel paddy transplanter traditionally poses physical strain and cognitive workload challenges for farm workers, especially during headland turns. This study introduces a virtual reality (VR)/augmented reality (AR)based remote‐control system for a two‐wheel paddy transplanter to resolve these issues. The system replaces manual controls with VR interfaces, integrating gear motors and an electronic control unit. Front and rear‐view cameras provide real‐time field perception on light‐emitting diode screens, displaying path trajectories via an autopilot controller and real‐time kinematic global navigation satellite systems module. Human operators manipulate the machine using a hand‐held remote controller while observing live camera feeds and path navigation trajectories. The study found that forward speed necessitated optimization within manageable limits of 1.75–2.00 km h−1 for walk‐behind types and 2.00–2.25 km h−1 for remote‐controlled systems. While higher speeds enhanced field capacity by 11.67%–12.95%, they also resulted in 0.74%–1.17% lower field efficiency. Additionally, Operators' physiological workload analysis revealed significant differences between walk‐behind and remotely controlled operators. Significant differences in energy expenditure rate (EER) were observed between walk‐behind and remote‐controlled paddy transplanters, with EER values ranging from 8.20 ± 0.80 to 27.67 ± 0.45 kJ min⁻¹ and 7.56 ± 0.55 to 9.72 ± 0.37 kJ min⁻¹, respectively (p < 0.05). Overall, the VR‐based remote‐control system shows promise in enhancing operational efficiency and reducing physical strain in paddy transplanting operations.

A Novel Positional Calibration Method for an Underwater Acoustic Beacon Array Based on the Equivalent Virtual Long Baseline Positioning Model

The performance of long baseline (LBL) positioning systems is significantly impacted by the distribution and positional calibration accuracy of underwater acoustic beacon arrays. In previous calibration methods for beacon arrays based on autonomous underwater vehicle (AUV) platforms, the slant range information of each beacon was processed independently, and each beacon was calibrated one at a time. This approach not only decreases the calibration efficiency but also leaves the positional calibration accuracy of each beacon highly susceptible to the navigation trajectory of the AUV. To overcome these limitations, an equivalent virtual LBL (EVLBL) positioning model is introduced in this paper. This model operates by adjusting the positions of each beacon according to the dead reckoning increments computed during the AUV’s reception of positioning signals, effectively forming a virtual beacon array. Consequently, the AUV is capable of mitigating LBL positioning errors that arise from its motion by simultaneously receiving positioning signals from all beacons. Additionally, an overall calibration method for beacon arrays based on particle swarm optimization (PSO) is proposed. In this approach, the minimization of the deviation between the EVLBL trajectory and the dead reckoning trajectory is set as the optimization objective, and the coordinates of each beacon are iteratively optimized. The simulation results demonstrate that the proposed EVLBL-based PSO algorithm (EVPSO) significantly enhanced the calibration efficiency and positional accuracy of the beacon array. Compared with conventional methods, the estimation error of the beacon positions was reduced from 6.40 m to within 1.00 m. After compensating for the beacon array positions, the positioning error of the LBL system decreased from approximately 5.00 m (with conventional methods) to around 1.00 m (with EVPSO), demonstrating the effectiveness of the proposed approach.

Bidirectional neural network for trajectory planning: An application to medical emergency vehicle

This paper focuses on the autonomous trajectory optimization of in-hospital emergency vehicles, aiming to plan a fast, safe, and comfortable trajectory for real time navigation to reach the emergency room. This has direct and significant practical implications for saving patients’ time and reducing the burden on medical staff. To address it, we propose a novel real-time trajectory planning algorithm and introduce a training framework that contributes to planner’s transferability. In the dataset preparation phase, we employ an optimization-based method to solve the in-hospital trajectory planning problem, generating substantial trajectories. In the training phase, we design a neural network-based planner to establish logical connections between states and control commands. To enhance interaction with the vehicle itself, we design a novel network training framework that incorporates a neural network-based vehicle simulator to learn the vehicle’s self-information, facilitating training the planner. During the inference phase, our planner can plan a collision-free, time-efficient, and comfortable trajectory. Furthermore, our algorithm demonstrates ease of transferability to different vehicle models. Finally, extensive simulations experiments are conducted to validate the safety, speed, and comfortability of the algorithm’s trajectory planning, as well as its excellent transferability.

Reduction in Trajectory Error by Generating Smoother Trajectory for the Time-Efficient Navigation of Mobile Robot

Reduction in Trajectory Error by Generating Smoother Trajectory for the Time-Efficient Navigation of Mobile Robot

State-constrained control strategy for safe navigation trajectory tracking of hovercraft based on improved barrier Lyapunov function

This study addresses the challenge of trajectory tracking control for underactuated hovercraft by utilizing an improved integral barrier Lyapunov function to ensure safe and efficient navigation in the presence of uncertain dynamics and environmental disturbances. The research introduces an improved barrier Lyapunov function tailored for both constrained and unconstrained systems by integrating prescribed performance control (PPC) with a log-type barrier Lyapunov function (LBLF). This function effectively limits tracking errors in both position and heading to within a predetermined range. To ensure the hovercraft's navigational stability at high speeds, the paper presents a novel, time-varying integral barrier Lyapunov function (IBLF) with both unilateral and bilateral asymmetric constraints, specifically designed to control surge speed and yaw angular velocity. This ensures the maintenance of surge speed above the critical resistance peak speed and keeps the yaw angular velocity within a secure operational band. The study also introduces a finite-time disturbance observer (FTDO) to estimate the system's uncertainties. Theoretical analysis confirms that the tracking errors are ultimately uniformly bounded, establishing Lyapunov stability. Simulation results substantiate the superiority and efficacy of the proposed control strategy.

Optimal Transport and Model Predictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm

This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.

A machine vision method for the evaluation of ship-to-ship collision risk

The development of ship technology and information technology has been driving the continuous improvement of ship intelligence, with safety being an inevitable requirement in the shipping industry. A machine vision-based ship collision warning method is proposed for high monitoring system cost and limited information acquisition in safety design of autonomous ship navigation. The method combines machine learning with image algorithms. Firstly, the backbone of YOLOv7 detector is replaced by EfficientFormerV2 network to achieve model lightweight while ensuring detection accuracy. Public datasets SeaShips, Flow and self-made ship pictures are combined, and the network is trained on this dataset. StrongSORT is used for target tracking. Secondly, a data fusion algorithm is introduced to determine the target point at the bow-bottom of the ship based on the time-varying attitude of the camera and the time-series features of the bounding boxes. Ship navigation trajectory estimation is performed using image algorithms. Finally, a collision evaluation model is established to calculate the collision risk index. Experimental results demonstrate that the improved YOLOv7 network maintains similar mAP.5 and Recall compared to the original model, while reducing the parameters by 31.2 % and GFLOPs by 58.4 %. The accuracy of target ship trajectory estimation is high, with MAE values below 1.5 % and RMSE values below 2 % in experiments. In ship collision warning experiments, the proposed method accurately identifies navigating vessels, estimates the trajectories, and provides timely warnings for imminent collision accidents. Compared to traditional ship collision warning methods, this paper offers a more intelligent and lightweight solution.

Digital and plasma biomarkers for an early diagnosis of Mild Cognitive Impairment and prodromal Alzheimer’s disease

AbstractBackgroundThe application of blood‐based biomarkers for the identification of Alzheimer’s disease (AD) and the development of novel digital technologies as cognitive screening tests are critical to moving toward a reliable, more accessible early diagnosis. Our aim was to evaluate the diagnostic performance of a machine learning‐based cognitive assessment known as Altoida’s digital neuro‐signature (DNS) in patients with non‐degenerative mild cognitive impairment (ndMCI) and MCI due to AD (prodromal AD) and its association with CSF and plasma biomarkers.MethodAltoida’s MCI‐DNS is a 10‐minute cognitive test battery evaluating activities of daily living via motoric and augmented reality tasks. The test consists of placing and finding virtual objects in a real environment and its final score is obtained by weighting multi‐modal digital data features, such as hands’ micro‐movements, speed, reaction times, or navigation trajectories. We included 81 participants, classified according to their clinical status and CSF AD biomarker profile as: cognitively unimpaired controls, CTR (n = 10; age = 68.5±5.9; MMSE = 29.4±1.1), ndMCI (n = 25; age = 67.6±7.2; MMSE = 26.9±1.9) and prodromal AD (n = 46; age = 70.8±4.9; MMSE = 24.3±3.3). We further investigated a subsample of participants classified according to their plasma pTau181 levels as measured by SiMoA [cutoff = 1.37 pg/mL (Sarto et al., 2022)]: pTau181 negative (n = 27; age = 68.5±5.9; MMSE = 26.9±2.7) or pTau181 positive (n = 30; age = 70.3±5.5; MMSE = 24.1±3.7).ResultSignificant differences were found in MCI‐DNS scores between CTR group and ndMCI (F = 23.5; p<0.001) and prodromal AD (F = 114.4; p<0.001) groups. Also, ndMCI showed higher MCI‐DNS scores than the prodromal AD group (F = 4.53; p<0.05, Fig. 1). ROC curves showed an excellent diagnostic accuracy of the MCI‐DNS in the discrimination between CTR vs. ndMCI (AUC = 0.879) and CTR vs. prodromal AD (AUC = 0.975) (Fig. 1). Further analyses showed differences in MCI‐DNS scores between CSF Aβ42 negative and CSF Aβ42 positive (F = 18.9; p<0.001; Fig. 2), as well as between plasma pTau181 negative and plasma pTau181 positive (F = 6.16; p<0.01; Fig. 2). Finally, MCI DNS scores significantly correlated with CSF Aβ42, CSF Aβ42/pTau ratio, CSF neurofilament‐light chain (NfL) and plasma pTau181 concentrations (Fig. 3).ConclusionAltoida’s MCI‐DNS test allows excellent discrimination between CTR and patients with MCI. MCI‐DNS scores significantly correlate with CSF AD core biomarkers, biomarkers of neurodegeneration and blood‐based biomarkers (i.e., plasma pTau181).

Obstacle Avoidance Learning for Robot Motion Planning in Human–Robot Integration Environments

In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this paper, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human-robot integration environment. The temporary target distribution is learned from imitations by using a Conditional Variational Autoencoder (CVAE) framework, whereby the dynamic scenario information including pedestrian information, the environmental information, and the robot information are considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions.

Emission Control Routes in Liner Shipping between Korea and Japan

Maritime shipping is considered a major air pollutant that directly affects global warming and climate change. This study aims to design an emission control route (ECR) that can contribute to long-term initiatives along with emission prevention policies. Applying a scenario analysis, this paper has analyzed the impact of ECR implementation on air quality in the shipping route between Korea and Japan. In this study, the ECR is defined as eco-friendly shipping routes that recommend maintaining a specific level of speed while sailing. Based on the navigation trajectories of 55 O/Ds, which were extracted by the automatic identification system (AIS) data of 135 container ships for each port call during one year, the ECR domains were designated. To analyze the introduction impacts, this study employed a scenario analysis based on the current Vessel Speed Reduction Program (VSRP) recommendation of 12 knots or less. The results demonstrated that the introduction of ECR in the liner services between Korea and Japan would increase the average travel time by 10.47–16.98%. However, according to the results of the scenario analysis, the introduction of the ECR can reduce emissions by 25.65% to 39.73%. By suggesting the initial concept of ECR and analyzing the emission reduction effects of its implementation, this study provides useful insights for a balance between economic and environmental performance.

Electromagnetically Navigated Tube Placement Device for Bedside Placement of Small Bowel Feeding Tube on Patients with Acute Severe Pancreatitis: Comparative Study.

A developing approach for the bedside installation of feeding tubes is the Electromagnetic Navigation-assisted Tube Placement Device (ENTPD). The ENTPD monitors the tip position of feeding tubes when they are inserted into the digestive tract. It aids in the avoidance of airway misalignment and allows placing into the small bowel. Several recent exploratory studies have shown that ENTPD for nasojejunal feeding tube installation can improve success rates, lower costs, and allow for a more rapid beginning of enteral nutrition. The aim of this study was to compare the effect of using an ENTPD for bedside placement of small bowel feeding tubes with blind placement on patients with acute severe pancreatitis and to see how well the electromagnetic navigation trajectory image (ENTI) and X-ray agreed on the location of the tube tip after placement. The study was done prospectively using randomized and single-blind methods. The 65 cases used electromagnetic navigation-assisted placement, and 58 cases were blind placement. For judging the tube tip location, we compared the success rate, median time, number of repeat placements, complications, and agreement of ENTI vs. X-ray. The blind placement group's success rate was 86.21% compared to the ENTPD's 95.38%, P = 0.075. The median time was significantly longer in the blind placement group (116.55 ± 68.62 min vs. 25.37 ±12.63 min, P=0.000); the average number of repeating placements was 3.02 ± 1.21 vs. 1.16 ± 0.31 (Blind placement vs. ENTPD, P = 0.002). It provided a high degree of agreement between ENTI and X-ray after contrast, κ=0.752 [95% confidence interval, 0.67-0.84]. No complications occurred in the two groups. ENTPD was used safely and effectively at the bedside to help patients with acute severe pancreatitis get feeding tubes. It not only improved the high successful rate of placement, decreased the time and reduced the exposure to X-ray, but it was also very convenient for bedside placement because of the portable equipment.

AUV planning and calibration method considering concealment in uncertain environments

IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered.

Intelligent ship collision avoidance model integrating human thinking experience

With the maturation of intelligent ship collision avoidance technology, the acceptability of this technology has gained attention and the ship collision avoidance model is confronting interpretability and trustworthiness difficulties. Therefore, this paper integrates human thinking experience into actual collision avoidance decision-making to propose an intelligent collision avoidance model that is more in line with the operator ‘s understanding, thus improving trustworthiness and autonomy. The general architecture of collision avoidance model is divided into two modules: a basic model and an analytical model, by adopting from human thinking patterns of fast and slow. The basic model considers the COLREGS and navigation experience to solve the encounter situation of two ships. The analytical model addresses the collision avoidance issues in complex scenarios with multi-ships, by introducing the repulsive force and the driving force parts of the social force model, which are used to implement course mapping adjustment and gentle course fluctuation respectively. Additionally, a field theory-based effect evaluation model is designed to blend human experience with algorithmic benefits for making the optimal decision in complex scenarios. The results show that when the basic model and the analytical model work together, this architecture performs effectively in a variety of scenarios. It is worth mentioning that the collision avoidance model can potentially be developed to advance collision-free and human-like process trajectory navigation in multi-ship situations.

How liner shipping heals schedule disruption: A data-driven framework to uncover the strategic behavior of port-skipping

Service disruption hurts the reliability of liner shipping schedules and the lack of high-quality data impedes research on schedule recovery in liner shipping. With the support of the Automatic Identification System (AIS), a satellite-based tracking system acquiring real-time records of vessels’ navigation trajectories worldwide, this study develops a novel data-driven framework to uncover the vessel schedule disruption recovery behavior (i.e., port-skipping). The framework consists of a series of independent yet closely related algorithms, which are in turn tasked with screening port calls, estimating closed-loop routes, measuring similarity between different routes, and identifying port-skipping behavior, respectively. Compared with partial proprietary data from the shipping industry and port authorities, the identification results can provide transparent datasets with wide applications and much easier access publicly. The developed data-driven framework is implemented with vessel trajectory information of around 2,000 container vessels worldwide from January 2016 to December 2020, and the results prove its validity and practical value for liner shipping scheduling management.

A cell-type-specific error-correction signal in the posterior parietal cortex

Neurons in the posterior parietal cortex contribute to the execution of goal-directed navigation1 and other decision-making tasks2–4. Although molecular studies have catalogued more than 50 cortical cell types5, it remains unclear what distinct functions they have in this area. Here we identified a molecularly defined subset of somatostatin (Sst) inhibitory neurons that, in the mouse posterior parietal cortex, carry a cell-type-specific error-correction signal for navigation. We obtained repeatable experimental access to these cells using an adeno-associated virus in which gene expression is driven by an enhancer that functions specifically in a subset of Sst cells6. We found that during goal-directed navigation in a virtual environment, this subset of Sst neurons activates in a synchronous pattern that is distinct from the activity of surrounding neurons, including other Sst neurons. Using in vivo two-photon photostimulation and ex vivo paired patch-clamp recordings, we show that nearby cells of this Sst subtype excite each other through gap junctions, revealing a self-excitation circuit motif that contributes to the synchronous activity of this cell type. These cells selectively activate as mice execute course corrections for deviations in their virtual heading during navigation towards a reward location, for both self-induced and experimentally induced deviations. We propose that this subtype of Sst neurons provides a self-reinforcing and cell-type-specific error-correction signal in the posterior parietal cortex that may help with the execution and learning of accurate goal-directed navigation trajectories.