Efficient Navigation Research Articles

Remember me - user-centered implementation of working memory architectures on an industrial robot.

The present research is innovative as we followed a user-centered approach to implement and train two working memory architectures on an industrial RB-KAIROS + robot: GRU, a state-of-the-art architecture, and WorkMATe, a biologically-inspired alternative. Although user-centered approaches are essential to create a comfortable and safe HRI, they are still rare in industrial settings. Closing this research gap, we conducted two online user studies with large heterogeneous samples. The major aim of these studies was to evaluate the RB-KAIROS + robot's appearance, movements, and perceived memory functions before (User Study 1) and after the implementation and training of robot working memory (User Study 2). In User Study 1, we furthermore explored participants' ideas about robot memory and what aspects of the robot's movements participants found positive and what aspects they would change. The effects of participants' demographic background and attitudes were controlled for. In User Study 1, participants' overall evaluations of the robot were moderate. Participant age and negative attitudes toward robots led to more negative robot evaluations. According to exploratory analyses, these effects were driven by perceived low experience with robots. Participants expressed clear ideas of robot memory and precise suggestions for a safe, efficient, and comfortable robot navigation which are valuable for further research and development. In User Study 2, the implementation of WorkMATe and GRU led to more positive evaluations of perceived robot memory, but not of robot appearance and movements. Participants' robot evaluations were driven by their positive views of robots. Our results demonstrate that considering potential users' views can greatly contribute to an efficient and positively perceived robot navigation, while users' experience with robots is crucial for a positive HRI.

Effect of repetition of vertical and horizontal routes on navigation performance in Australian bull ants

Solitarily foraging ant species differ in their reliance on their two primary navigational systems— path integration and visual learning. Despite many species of Australian bull ants spending most of their foraging time on their foraging tree, little is known about the use of these systems while climbing. “Rewinding” displacements are commonly used to understand navigational system usage, and work by introducing a mismatch between these navigational systems, by displacing foragers after they have run-down their path integration vector. We used rewinding to test the role of path integration on the arboreal and terrestrial navigation of M. midas. We rewound foragers along either the vertical portion, the ground surface portion, or across both portions of their homing trip. Since rewinding involves repeatedly capturing and releasing foragers, we included a nondisplacement, capture-and-release control, in which the path integration vector is unchanged. We found that rewound foragers do not seem to accumulate path integration vector, although a limited effect of vertical rewinding was found, suggesting a potential higher sensitivity while descending the foraging tree. However, the decrease in navigational efficiency due to capture was larger than the vertical rewinding effect, which along with the negative impact of the vertical surface, and an interaction between capture and rewinding, may suggest aversion rather than path integration caused the vertical rewinding response. Together these results add to the evidence that M. midas makes minimal use of path integration while foraging, and the growing evidence that they are capable of quickly learning from aversive stimulus.

Evolutionary Algorithm-Based Energy-Aware Path Planning with a Quadrotor for Warehouse Inventory Management

Quadrotors have been vital for automating warehouse processes. However, a significant gap in recent studies is that they use a single quadrotor with limited battery life, considering that their objective involves navigation in a large-scale environment such as a warehouse. Using an energy consumption model to enable more efficient navigation can be explored. Conventional data-driven energy models and path planning algorithms are insufficient for describing the various motions that a quadrotor can perform in warehouse operations, such as changes in yaw. This study aims to design a novel exhaustive data-driven energy consumption model and evolutionary algorithm-based path planning algorithm to consider various quadrotor movements involved in warehouse operations. The quadrotor is tasked with performing a set of movements to each be represented as a power equation in terms of their velocity. The obtained equations were subsequently used as the primary optimization objective for the path planning algorithm, which included yaw angle objectives and constraints. A set of experiments was performed with Crazyflie quadrotors to verify the model and the algorithm. The results showcased the accuracy of the energy consumption model, which was kept at a maximum difference of 0.6%. The designed path planning algorithm obtained greater energy efficiency in the generated paths compared to other state-of-the-art evolutionary algorithms with similar objectives and constraints. Doi: 10.28991/HIJ-2023-04-04-012 Full Text: PDF

Spatial navigation metrics differentiating preclinical and prodromal Alzheimer’s disease: a systematic review

AbstractBackgroundIncreasing evidence suggests that impairments of spatial navigation, orientation and memory may represent one of the earliest features Alzheimer’s disease (AD), preceding deficits in other cognitive domains. This is consistent with early pathological tau and amyloid deposition in temporal and parietal brain regions implicated in spatial processing. However, it remains unclear which metrics and spatial behaviours can i) differentiate high‐risk, preclinical or prodromal AD individuals, and ii) may be captured with digital devices suitable for future deployment at scale in clinical practice. We addressed this knowledge gap with a systematic review.MethodTwo databases (PubMed/Web of Science) were searched following PRISMA guidelines. Medical subheading keywords for different dementia types were combined with prodromal, preclinical and genetic or disorder risk factor terms. We included studies published until October 2022 that collected digital or physical objective measures of egocentric or allocentric processing (wayfinding, orientation, reference frame translation, route learning or path integration). We excluded diagnoses of other conditions or dementia, cross‐sectional MCI studies without biomarkers, young adults, case studies, interventional studies or studies without appropriate controls.Result25 articles were included from 316 identified abstracts. None investigated non‐AD dementias. 131 metrics were extracted from 38 different tasks, of which 31 where digital (81%). 82 metrics showed differentiation of predementia AD groups (63%). We harmonised these into 21 distinct summary metrics covering four domains of active or passively tracked spatial behaviours. Across all domains, metrics capturing decreased navigation efficiency (i.e. distance or time required to find goals) and accuracy (i.e. distance or angle from goal) were most frequently reported, but egocentric better differentiated prodromal than preclinical groups. Metrics for path integration and passive tracking of everyday GPS data were promising but relatively under‐explored.ConclusionFor future clinical use, spatial tests will require standardisation and validation against established markers of disease. These review data will inform the selection of digital tools to assess spatial behaviours in at‐risk, preclinical and prodromal AD populations as part of the EDoN Initiative.

Clinical Manifestations.

Increasing evidence suggests that impairments of spatial navigation, orientation and memory may represent one of the earliest features Alzheimer's disease (AD), preceding deficits in other cognitive domains. This is consistent with early pathological tau and amyloid deposition in temporal and parietal brain regions implicated in spatial processing. However, it remains unclear which metrics and spatial behaviours can i) differentiate high-risk, preclinical or prodromal AD individuals, and ii) may be captured with digital devices suitable for future deployment at scale in clinical practice. We addressed this knowledge gap with a systematic review. Two databases (PubMed/Web of Science) were searched following PRISMA guidelines. Medical subheading keywords for different dementia types were combined with prodromal, preclinical and genetic or disorder risk factor terms. We included studies published until October 2022 that collected digital or physical objective measures of egocentric or allocentric processing (wayfinding, orientation, reference frame translation, route learning or path integration). We excluded diagnoses of other conditions or dementia, cross-sectional MCI studies without biomarkers, young adults, case studies, interventional studies or studies without appropriate controls. 25 articles were included from 316 identified abstracts. None investigated non-AD dementias. 131 metrics were extracted from 38 different tasks, of which 31 where digital (81%). 82 metrics showed differentiation of predementia AD groups (63%). We harmonised these into 21 distinct summary metrics covering four domains of active or passively tracked spatial behaviours. Across all domains, metrics capturing decreased navigation efficiency (i.e. distance or time required to find goals) and accuracy (i.e. distance or angle from goal) were most frequently reported, but egocentric better differentiated prodromal than preclinical groups. Metrics for path integration and passive tracking of everyday GPS data were promising but relatively under-explored. For future clinical use, spatial tests will require standardisation and validation against established markers of disease. These review data will inform the selection of digital tools to assess spatial behaviours in at-risk, preclinical and prodromal AD populations as part of the EDoN Initiative.

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.

Solving energy-efficient lock group co-scheduling problem with ship lift and approach channel using a collaborative adaptive multi-objective algorithm

There is growing interest in the lock group co-scheduling research because of serious capacity imbalance between two dams at Three Gorges-Gezhou Dams Hub (TGDH). However, most current studies ignore the impact of ship lift and approach channel on navigation efficiency, and the energy consumption from vessels on ecological environment. Encouraged by this, we investigate an energy-efficient lock group co-scheduling problem at the TGDH with the consideration of ship lift as well as approach channel. A new multi-objective model for the problem is proposed, aiming to simultaneously optimize the average area utilization of all locks, average tardiness of vessels and total energy consumption of vessels. A collaborative adaptive multi-objective algorithm (CAMOA) is well-designed to solve the studied problem. The CAMOA makes use of a well-tailored two-layer encoding scheme and a three-stage group-shift decoding approach to represent and decode each solution. Next, an adaptive adjustment search strategy based on step control factor is periodically triggered to reinforce local exploitation capability, where a novel fuzzy correlation entropy analysis is coupled to evaluate the neighborhood solutions. Extensive simulation experiments are implemented according to the real-world data from the TGDH. The statistical results demonstrate that the proposed CAMOA is efficient and reliable in solving the studied problem. This work is very significant for TGDH to improve the passing efficiency and reduce the energy consumption.

Navigating Rover Using Arduino

Abstract: The objective of the proposed system is to use Arduino to guide a rover. The connectivity will be the necessary electronic circuit. The PWM signal of a microcontroller powers the DC motors on the rover. The rover may be maneuvered along a straight path or any other path it chooses by manipulating each motor separately or in tandem. The rover is programmed with Arduino. The results of the system's implementation and testing in a real-world setting demonstrate its ability to precisely navigate through a variety of terrains and avoid obstacles. The suggested method may find use in search and rescue missions, environmental monitoring, and autonomous farming. Overall, navigating rover shows that creating a reliable and efficient rover navigation system is possible even with a commonly used programming language like Embedded C.

Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks

A considerable but ill-defined proportion of patients with mild traumatic brain injury (mTBI) experience persistent cognitive sequelae; the ability to identify such individuals early can help their neurorehabilitation. Here we tested the hypothesis that acute measures of efficient communication within brain networks are associated with patients’ risk for unfavorable cognitive outcome six months after mTBI. Diffusion and T1-weighted magnetic resonance imaging, alongside cognitive measures, were obtained to map connectomes both one week and six months post injury in 113 adult patients with mTBI (71 males). For task-related brain networks, communication measures (characteristic path length, global efficiency, navigation efficiency) were moderately correlated with changes in cognition. Taking into account the covariance of age and sex, more unfavorable communication within networks were associated with worse outcomes within cognitive domains frequently impacted by mTBI (episodic and working memory, verbal fluency, inductive reasoning, and processing speed). Individuals with more unfavorable outcomes had significantly longer and less efficient pathways within networks supporting verbal fluency (all t > 2.786, p < .006), highlighting the vulnerability of language to mTBI. Participants in whom a task-related network was relatively inefficient one week post injury were up to eight times more likely to have unfavorable cognitive outcome pertaining to that task. Our findings suggest that communication measures within task-related networks identify mTBI patients who are unlikely to develop persistent cognitive deficits after mTBI. Our approach and findings can help to stratify mTBI patients according to their expected need for follow-up and/or neurorehabilitation.

Snapshot GNSS receivers for low-effort, high-gain space situational awareness

This paper proposes a novel concept of using highly efficient Snapshot Global Navigation Satellite Systems (GNSS) receivers to provide precise position fixes of single or multiple satellites in Low-Earth Orbit (LEO) to improve upper atmospheric modeling and thus contribute to superior space situational awareness (SSA). While tracking of LEO satellites and the use of onboard GNSS receivers for drag measurements and upper atmosphere modeling are well-established techniques, the expected advent of snapshot GNSS receivers for spaceborne scientific applications will allow massive improvements on the GNSS sensor’s Size, Weight, Power and Cost (SWaP-C). With chip-size dimensions of 4x4 mm2, a mass of less than 5 gr, an average power level below 0.1 mW, snapshot receiver technology is expected to provide position fixes in space with an accuracy of ∼19 m (3D r.m.s.), which will surpass the accuracy of Two-Line Elements (TLE) provided by the US Joint Space Operations Center (JSpOC) by at least two orders of magnitude. Equally important to their SWaP-C benefits, Snapshot GNSS receivers will allow mission and spacecraft designers to trade onboard-processing requirements versus payload downlink requirements, leading to either minimum onboard processing or a minimum amount of downlinked data. In this research, we establish the concept and architectural overview of using snapshot GNSS receivers for SSA, including the role of using them in a Distributed Space System (DSS), and detail their characterization and performance in terms of the required GNSS hardware and the impact of these payload on the power budget, the link budget and the OnBoard Data Handling (OBDH) budget of a satellite. It will be shown that these receivers lend themselves especially to their use on femto-, pico- and nano-satellites, although integrated snapshot modules may be flown as auxiliary payloads on micro- or mini-satellites as well. While this work focuses on the implications of the use of snapshot GNSS receivers on spacecraft design for the use of upper atmosphere modeling and SSA, their use may open up other science applications which avoid the need for expensive high-grade GNSS receivers.

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.

Risk-Aware Deep Reinforcement Learning for Robot Crowd Navigation

Ensuring safe and efficient navigation in crowded environments is a critical goal for assistive robots. Recent studies have emphasized the potential of deep reinforcement learning techniques to enhance robots’ navigation capabilities in the presence of crowds. However, current deep reinforcement learning methods often face the challenge of robots freezing as crowd density increases. To address this issue, a novel risk-aware deep reinforcement learning approach is proposed in this paper. The proposed method integrates a risk function to assess the probability of collision between the robot and pedestrians, enabling the robot to proactively prioritize pedestrians with a higher risk of collision. Furthermore, the model dynamically adjusts the fusion strategy of learning-based and risk-aware-based features, thereby improving the robustness of robot navigation. Evaluations were conducted to determine the effectiveness of the proposed method in both low- and high-crowd density settings. The results exhibited remarkable navigation success rates of 98.0% and 93.2% in environments with 10 and 20 pedestrians, respectively. These findings emphasize the robust performance of the proposed method in successfully navigating through crowded spaces. Additionally, the approach achieves navigation times comparable to those of state-of-the-art methods, confirming its efficiency in accomplishing navigation tasks. The generalization capability of the method was also rigorously assessed by subjecting it to testing in crowd environments exceeding the training density. Notably, the proposed method attains an impressive navigation success rate of 90.0% in 25-person environments, surpassing the performance of existing approaches and establishing itself as a state-of-the-art solution. This result highlights the versatility and effectiveness of the proposed method in adapting to various crowd densities and further reinforces its applicability in real-world scenarios.

Multisensory input modulates memory-guided spatial navigation in humans

Efficient navigation is supported by a cognitive map of space. The hippocampus plays a key role for this map by linking multimodal sensory information with spatial memory representations. However, in human navigation studies, the full range of sensory information is often unavailable due to the stationarity of experimental setups. We investigated the contribution of multisensory information to memory-guided spatial navigation by presenting a virtual version of the Morris water maze on a screen and in an immersive mobile virtual reality setup. Patients with hippocampal lesions and matched controls navigated to memorized object locations in relation to surrounding landmarks. Our results show that availability of multisensory input improves memory-guided spatial navigation in both groups. It has distinct effects on navigational behaviour, with greater improvement in spatial memory performance in patients. We conclude that congruent multisensory information shifts computations to extrahippocampal areas that support spatial navigation and compensates for spatial navigation deficits.

A Hybrid Global/Reactive Algorithm for Collision-Free UAV Navigation in 3D Environments with Steady and Moving Obstacles

This paper introduces a practical navigation approach for nonholonomic Unmanned Aerial Vehicles (UAVs) in 3D environment settings with numerous stationary and dynamic obstacles. To achieve the intended outcome, Dynamic Programming (DP) is combined with a reactive control algorithm. The DP allows the UAVs to navigate among known static barriers and obstacles. Additionally, the reactive controller uses data from the onboard sensor to avoid unforeseen obstacles. The proposed strategy is illustrated through computer simulation results. In simulations, the UAV successfully navigates around dynamic obstacles while maintaining its route to the target. These results highlight the ability of our proposed approach to ensure safe and efficient UAV navigation in complex and obstacle-laden environments.

Impacts of Arctic Sea Fog on the Change of Route Planning and Navigational Efficiency in the Northeast Passage during the First Two Decades of the 21st Century

Under the background of climate change, the Northeast Passage’s navigability is on the rise. Arctic sea fog significantly influences navigational efficiency in this region. Existing research primarily focuses on routes accumulating the lowest distance, neglecting routes with the lowest time and sea fog’s influence on route planning and navigational efficiency. This study compares the fastest and shortest routes and analyzes Arctic sea fog’s impact on the Northeast Passage from June to September (2001–2020). The results show that coastal areas are covered with less sea ice under notable monthly variations. Sea fog frequency is highest near coasts, declining with latitude. September offers optimal navigation conditions due to minimal ice and fog. When only sea ice is considered, the fastest route is approximately 4 days quicker than the shortest. The shortest route has migrated towards the higher latitude over two decades, while the fastest route remains closer to the Russian coast. Adding the impact of sea fog on the fastest route, the speed decreased by 30.2%, increasing sailing time to 45.1%. The new fastest route considering both sea ice and sea fog achieved a 13.9% increase in sailing speed and an 11.5% reduction in sailing time compared to the original fastest route.

Hierarchical Relational Graph Learning for Autonomous Multirobot Cooperative Navigation in Dynamic Environments

As a specific kind of Cyber-Physical Systems (CPSs), autonomous robot clusters play an important role in various intelligent manufacturing fields. However, due to the increasing design complexity of robot clusters, it is becoming more and more challenging to guarantee the safety and efficiency for multi-robot cooperative navigation in dynamic and complex environments. Although Deep Reinforcement Learning (DRL) shows great potential in learning multi-robot cooperative navigation policies, existing DRL-based approaches suffer from scalability issues and rarely consider the transferability of trained policies to new tasks. To address these problems, this paper presents a novel DRL-based multi-robot cooperative navigation approach named HRMR-Navi that equips each robot with both a two-layered hierarchical graph network model and an attention-based communication model. In our approach, the hierarchical graph network model can efficiently figure out hierarchical relations among all agents that either cooperate for efficiency or avoid obstacles for safety to derive more advanced strategies, and the communication model can accurately form a global view of the environment for a specific robot, thus the multi-robot cooperation efficiency can be further strengthened. Meanwhile, we propose an improved Proximal Policy Optimization (PPO) algorithm based on the Maximum Entropy reinforcement learning, named MEPPO, to enhance the robot exploration ability. Comprehensive experimental results demonstrate that, compared with state-of-the-art approaches, HRMR-Navi can achieve more efficient cooperative navigation with less time cost, lower collision rate, higher scalability, and better knowledge transferability.

A machine learning based Bayesian decision support system for efficient navigation of double-ended ferries

Ships can be operated more efficiently by utilizing intelligent decision support integrated with onboard data collection systems. In this study, a Bayesian optimization-based decision support system, which utilizes ship performance models built by machine learning methods, is proposed to help determine the operational set-points of two engines for double-ended ferries. By optimizing the ferries’ power allocation between the stern and bow engines, the Decision Support System (DSS) will simultaneously attempt to keep the ETA of the ferry fixed under a set of operational constraints using the Bayesian optimization. Its objective is to minimize fuel consumption along individual trips. Based on simulation environment, the DSS can reduce at maximum 40 % fuel consumption with no significant change of the ETA. Final full-scale experiments of a double-ended ferry demonstrated an average of 15 %, where at least half of this saving was achieved by the optimized power allocation between bow and stern engines.

Usability Testing Kualitas Desain UI Pemahaman Literasi Digital Anak-Anak

In 2023, digital literacy has increased to be discussed which navigates human interaction with the current digital environment. The purpose of this study is to determine the relationship between digital literacy and user interface (UI) design quality using a usability testing approach. The study involved collecting data through a series of trials with participants representing different levels of digital literacy. The main focus of this study was to assess the extent to which the UI design could improve the digital literacy understanding of the users, namely school children. The parameters evaluated involved aspects of digital literacy, such as the ability to understand and evaluate digital information, as well as usability elements such as interface clarity, navigation efficiency, and user engagement level. The approach used is the design thinking method which focuses on the end result only. Usability testing is an important evaluation method in measuring the effectiveness, efficiency, and user satisfaction of a user interface (UI). This research focuses on the use of usability testing to assess the quality of UI design in the context of understanding digital literacy. The results of this study can provide insight into how the level of digital literacy can affect the user experience in using digital applications and contribute to the development of UIs that are more user-friendly and accessible to various levels of society.

Analysis of synchronous localization systems for UAVs urban applications

Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.

HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).