Safe Path Research Articles

Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface ships

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

The Design Concept of Integrated Pedestrian Sidewalk with Solar Plants as a Form of Green Campus Development at the Bali Land Transportation Polytechnic

Abstract An important aspect of creating a viable campus environment is providing safe and comfortable walking paths. Proper walking paths give some advantages for students, including improved physical and mental health, transportation efficiency, and increased Student satisfaction and happiness. The pedestrian sidewalk area at Bali Land Transportation Polytechnic (Poltrada Bali) covers 3800 m2, experiencing high pedestrian activity but lacking protective roofing to enhance pedestrian comfort. Based on the pedestrian sidewalk area, a solar power plant as support for the green canopy construction combined with shading vines is possible. The generated electrical energy serves as the power source for the Public Electric Vehicle Charging Stations (PEVCS) in the campus parking lot as a support for the electric vehicle acceleration program in Bali province. In addition to PEVCS, energy extraction surplus is used as alternative energy for street lighting facilities in the campus area. In this study, the design concept of a shaded pedestrian sidewalk has been carried out, with multiple benefits as solar plants using Delphi as a variable identification and validation tool, and SketchUp software to visualize design shapes aesthetically. The results show that a pedestrian comfort level of 94% was obtained and the maximum power generation of 934,800 Wp, thus it was able to meet the 887,832 W campus operational power reserve needs, street and garden lights of 9,728 W, and the power requirements of two PEVCS units of 22,000 W with a surplus of 15,240 W electric power.

Uniform Finite Time Safe Path Tracking Control for Obstacle Avoidance of Autonomous Vehicle via Barrier Function Approach

Uniform Finite Time Safe Path Tracking Control for Obstacle Avoidance of Autonomous Vehicle via Barrier Function Approach

Learning-based model predictive control for safe path planning and control

Learning-based model predictive control for safe path planning and control

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

Safe and Efficient Exploration Path Planning for Unmanned Aerial Vehicle in Forest Environments

This study presents an enhanced exploration path planning for unmanned aerial vehicles. The primary goal is to increase the chances of survival of missing people in forest environments. Exploration path planning is an essential methodology for exploring unknown three-dimensional spaces. However, previous studies have mainly focused on underground environments, not forest environments. The existing path planning methods for underground environments are not directly applicable to forest environments. The reason is that multiple open spaces exist with various obstacles, such as trees, foliage, undergrowth, and rocks. This study mainly focused on improving the safety and efficiency to be suitable for forests rather than underground environments. Paths closer to obstacles are penalized to enhance safety, encouraging exploration at a safer distance from obstacles. A potential field function is applied based on explored space to minimize overlapping between existing and new paths to increase efficiency. The proposed exploration path planning method was validated through an extensive simulation analysis and comparison with state-of-the-art sampling-based path planning. Finally, a flight experiment was conducted to verify further the feasibility of the proposed method using onboard real hardware implementation in a cluttered and complex forest environment.

MT-SIPP: An Efficient Collision-Free Multi-Chain Robot Path Planning Algorithm

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

Reimagining Accessibility: Leveraging Deep Learning in Smartphone Applications to Assist Visually Impaired People Indoor Object Distance Estimation

Every other aspect of life is organized around the sight. A person with vision impairment suffers severely from independent mobility and quality of life. The proposed framework combines mobile deep learning with distance estimation algorithms to detect and classify indoor objects with estimated distance in real-time during indoor navigation. The user, wearing the device with a lanyard or holding it in a way that positions the camera forward, identifies in real-time surroundings indoor objects with estimated distance and voice commentary. Moreover, the mobile framework provides an estimated distance to the obstacles and suggests a safe navigational path through voice-guided feedback. By harnessing the power of deep learning in a mobile setting, this framework aims to improve the independence of visually impaired individuals by facilitating them a higher degree of independence in indoor navigation. This study's proposed mobile object detection and distance estimation framework achieved 99.75% accuracy. This research contributes by leveraging mobile deep learning with identifying objects in real-time, classification and distance estimation, a state-of-the-art approach to use the latest technologies to enhance indoor mobility challenges faced by visually impaired people.

Back-and-Forth (BaF): a new greedy algorithm for geometric path planning of unmanned aerial vehicles

The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a path for a UAV or UCAV system requires solving a challenging problem optimally by considering the different objectives about the enemy threats protecting the battlefield, fuel consumption or battery usage and kinematic constraints on the turning maneuvers. Because of the increasing demands to the UAV systems and game-changing roles played by them, developing new and versatile path planning algorithms become more critical and urgent. In this study, a greedy algorithm named as the Back-and-Forth (BaF) was designed and introduced for solving the path planning problem. The BaF algorithm gets its name from the main strategy where a heuristic approach is responsible to generate two predecessor paths, one of which is calculated from the start point to the target point, while the other is calculated in the reverse direction, and combines the generated paths for utilizing their advantageous line segments when obtaining more safe, short and maneuverable path candidates. The performance of the BaF was investigated over three battlefield scenarios and twelve test cases belonging to them. Moreover, the BaF was integrated into the workflow of a well-known meta-heuristic, artificial bee colony (ABC) algorithm, and detailed experiments were also carried out for evaluating the possible contribution of the BaF on the path planning capabilities of another technique. The results of the experiments showed that the BaF algorithm is able to plan at least promising or generally better paths with the exact consistency than other tested meta-heuristic techniques and runs nine or more times faster as validated through the comparison between the BaF and ABC algorithms. The results of the experiments further proved that the integration of the BaF boosts the performance of the ABC and helps it to outperform all of fifteen competitors for nine of twelve test cases.

Research on robot path planning with safe distance optimization

PurposeWith the development of technology, the application scenarios of mobile robots are becoming more and more extensive, accompanied by a variety of application scenarios suitable and safe path planning algorithms are indispensable for mobile robots.Design/methodology/approachThe purpose of this paper to improve the safety performance of your bot during the execution of tasks. The methods are synthesized in three main areas: setting appropriate safety distances based on the actual radius of the robot, turn penalty reduces the number of turns by applying an additional penalty to the number of turns in a heuristic function and path smoothing is used to improve path reliability by reducing the number of right-angle turns.FindingsA suitable safety distance greatly improves the safety of mobile robots and facilitates their development. Optimization of turns in the path of mobile robots improves the travel efficiency of robots. Enhancing the safety of mobile robots has become a research hotspot for path-planning algorithms.Originality/valueThis paper proposes a path planning scheme for mobile robots with safe distances, which provides readers with a comprehensive and systematic progress of path planning research. It helps readers to get inspiration from enhancing the safety of mobile robots.

Safe Path: Energy Harvesting from Pedestrian Movement in Karbala- Spatial Suitability and Crowd Dynamics Towards Sustainability

Safe Path: Energy Harvesting from Pedestrian Movement in Karbala- Spatial Suitability and Crowd Dynamics Towards Sustainability

Longitudinal dynamic envelope solution and transition path optimization based on reachability analysis for fixed-wing VTOL UAVs

Flight velocity and tilt angle are commonly employed to define the safe region for fixed-wing vertical take-off and landing (FW-VTOL) UAVs as they undergo tilting, referred to as the transition corridor. Nevertheless, the transition corridor only considers the aircraft's limitations in a quasi-stationary condition and ignores the additional constraints introduced by the aircraft's kinematic behavior. To obtain a more accurate safe state space, the concept of the dynamic envelope was proposed by previous researchers. The dynamic envelope of UAV represents the safe state space taking into account aerodynamic constraints and kinematic constraints. Solving the dynamic envelope has consistently remained a focal point of research in the field. This study introduces an innovative approach that employs reachability analysis to solve the dynamic envelope of the FW-VTOL UAV during the transition stage. Building upon this, it proposes a safety index for the transition path, optimized to determine the safest transition path. Subsequently, specific case studies verified the effectiveness of the solution process and optimization method. The designed optimal path enhances safety by 9.71% compared to the basic path, signifying its practical significance in mitigating risk during the transition stage of FW-VTOL UAVs. Furthermore, the attributes of the transition path are analyzed based on two aspects: (A) a comparison between the optimal transition path and the two-dimensional transition path with a constant angle of attack; (B) an examination of how key UAV design parameters influence the transition path's safety. Overall, the methodology and general rules proposed in this study provide a theoretical basis and technical support for improving the security of FW-VTOL UAVs in the transition stage.

Endoscopic Transpterygoid Approach to Meckel’s Cave: Technical Considerations and Retrospective Analysis of a Clinical Series

ObjectiveTumors located within the Meckel's cave (MC) pose a significant surgical challenge. Although several corridors to access this complex region have been described, the endoscopic transpterygoid approach (ETPA) and the endoscopic transorbital superior eyelid approach (ETOA) have emerged in recent years, as viable alternatives to traditional microsurgical transcranial approaches (MTA). To date, there is a limited literature on surgical series considering endoscopic-assisted approaches to the MC. MethodsWe conducted a retrospective analysis of patients with primary MC tumors treated at our Institution between 2015 and 2022, specifically those managed via the ETPA assisted by intraoperative Endoscopic Diving Technique (EDT). Lesion resection extent was evaluated using pre- and post-intervention radiological images and surgical videos. Moreover, a literature review on ETPA was performed. ResultsThis series comprises 7 patients affected by 4 trigeminal schwannomas, 1 benign notochordal cell tumor, 1 dermoid cyst and 1 mesenchymal tumor. In 71 % of cases, trigeminal neuralgia was the presenting symptom. Post-operative clinical improvement was observed in all but one case. Notably, 85.7 % of patients achieved total or near-total resection (NTR), with the remaining case undergoing subtotal resection (STR). No significant intraoperative complications occurred, and no recurrences were observed during the mean follow-up period of 41 months. ConclusionsIn selected cases, the ETPA offers a direct and safe path to lesions located into the MC. This approach circumvents complications and constraints associated with ETOA or MTA. Moreover, the use of the EDT reduces manipulation of critical neurovascular structures, enhancing the efficacy of the ETPA.

MXenes and MXene-Based Metal Hydrides for Solid-State Hydrogen Storage: A Review.

Hydrogen-driven energy is fascinating among the everlasting energy sources, particularly for stationary and onboard transportation applications. Efficient hydrogen storage presents a key challenge to accomplishing the sustainability goals of hydrogen economy. In this regard, solid-state hydrogen storage in nanomaterials, either physically or chemically adsorbed, has been considered a safe path to establishing sustainability goals. Though metal hydrides have been extensively explored, they fail to comply with the set targets for practical utilization. Recently, MXenes, both in bare form and hybrid state with metal hydrides, have proven their flair in ascertaining the hydrides' theoretical and experimental hydrogen storage capabilities far beyond the fancy materials and current state-of-the-art technologies. This review encompasses the significant accomplishments achieved by MXenes (primarily in 2019-2024) for enhancing the hydrogen storage performance of various metal hydride materials such as MgH2, AlH3, Mg(BH4)2, LiBH4, alanates, and composite hydrides. It also discusses the bottlenecks of metal hydrides for hydrogen storage, the potential use of MXenes hybrids, and their challenges, such as reversibility, H2 losses, slow kinetics, and thermodynamic barriers. Finally, it concludes with a detailed roadmap and recommendations for mechanistic-driven future studies propelling toward a breakthrough in solid material-driven hydrogen storage using cost-effective, efficient, and long-lasting solutions.

Analysis and Application of Chaotic Genetic Algorithm Based on Network Security in The Research of Resilience of Cluster Networks

With the wide application of UAV, in the actual flight process, UAV needs to calculate the safe path according to its own position, environment, obstacles and other information. Due to the complex and changeable scene and environment of UAV mission execution, it is very important to select an appropriate UAV path planning algorithm. This paper aims at the path planning problem of multiple UAVs in a complex three-dimensional environment to ensure that multiple UAVs reach the mission location from different angles. Taking the chaotic genetic algorithm in network security protection as the main body, the operation difficulty of the algorithm is reduced, and the solution speed and accuracy of the algorithm are improved. The path length obtained by the proposed algorithm is 8.4% less than that of the ABC algorithm, 11.3% less than that of the PSO algorithm, and 4.2% less than that of the BABC algorithm. The system running time of the improved algorithm is also reduced by 27% to 45% compared with other algorithms. In terms of unmanned cooperation, this paper proposes a system capability based on network modeling to improve the cooperative combat capability of multiple UAVs. By establishing a network model, information sharing, collaborative decision-making and collaborative decision-making between drones are realized, thereby improving the effectiveness of the entire system. At the same time, this paper also considers the problem of network survivability. By introducing redundant design and fault recovery mechanism, the robustness and reliability of the system are enhanced.

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior.

The protection of plants in mountainous and hilly areas differs from that in plain areas due to the complex terrain, which divides the work plot into many narrow plots. When designing the path planning method for plant protection UAVs, it is important to consider the generality in different working environments. To address issues such as poor path optimization, long operation time, and excessive iterations required by traditional swarm intelligence algorithms, this paper proposes a bionic three-dimensional path planning algorithm for plant protection UAVs. This algorithm aims to plan safe and optimal flight paths between work plots obstructed by multiple obstacle areas. Inspired by krill group behavior and based on group intelligence algorithm theory, the bionic three-dimensional path planning algorithm consists of three states: "foraging behavior", "avoiding enemy behavior", and "cruising behavior". The current position information of the UAV in the working environment is used to switch between these states, and the optimal path is found after several iterations, which realizes the adaptive global and local convergence of the track planning, and improves the convergence speed and accuracy of the algorithm. The optimal flight path is obtained by smoothing using a third-order B-spline curve. Three sets of comparative simulation experiments are designed to verify the performance of this proposed algorithm. The results show that the bionic swarm intelligence algorithm based on krill swarm behavior reduces the path length by 1.1~17.5%, the operation time by 27.56~75.15%, the path energy consumption by 13.91~27.35%, and the number of iterations by 46~75% compared with the existing algorithms. The proposed algorithm can shorten the distance of the planned path more effectively, improve the real-time performance, and reduce the energy consumption.

Umfassender Literaturüberblick über die Anwendung der otologisch-chirurgischen Planungssoftware OTOPLAN® bei der Cochleaimplantation. Englische Version

Abstract Background The size of the human cochlear, measured by the diameter of the basal turn, varies between 7 and 11 mm. For hearing rehabilitation with cochlear implants (CI), the size of the cochlear influences the individual frequency map and the choice of electrode length. OTOPLAN® (CAScination AG [Bern, Switzerland] in cooperation with MED-EL [Innsbruck, Austria]) is a software tool with CE marking for clinical applications in CI treatment which allows for precise pre-planning based on cochlear size. This literature review aims to analyze all published data on the application of OTOPLAN®. Materials and methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied to identify relevant studies published in the PubMed search engine between January 2015 and February 2023 using the search terms “otoplan” [title/abstract] OR “anatomy-based fitting” [title/abstract] OR “otological software tool” [title/abstract] OR “computed tomography-based software AND cochlear” [title/abstract]. Results The systematic review of the literature identified 32 studies on clinical use of OTOPLAN® in CI treatment. Most studies were reported from Germany (7 out of 32), followed by Italy (5), Saudi Arabia (4), the USA (4), and Belgium (3); 2 studies each were from Austria and China, and 1 study from France, India, Norway, South Korea, and Switzerland. In the majority of studies (22), OTOPLAN® was used to assess cochlear size, followed by visualizing the electrode position using postoperative images (5), three-dimensional segmentation of temporal bone structures (4), planning the electrode insertion trajectory (3), creating a patient-specific frequency map (3), planning of a safe drilling path through the facial recess (3), and measuring of temporal bone structures (1). Conclusion To date, OTOPLAN® is the only DICOM viewer with CE marking in the CI field that can process pre-, intra-, and postoperative images in the abovementioned applications.

Research on Lifting Path Planning Algorithms for Intelligent Cranes

Path planning algorithm is one of the key technologies in the study of intelligent cranes, and a good path planning algorithm can quickly plan a safe and collision-free path. The current research status of path planning technology in the field of crane lifting path planning is reviewed. The implementation principles of various types of algorithms are analysed, and the current research status of mainstream path planning algorithms is summarized. According to the characteristics of crane path planning algorithms, crane path planning algorithms are classified into traditional path planning algorithms, intelligent path planning algorithms and deep reinforcement learning algorithms. For each type of algorithm is analysed and summarized, and the development of crane path planning algorithms is looked forward to, hoping to provide reference for related research.

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

Bi-directional adaptive enhanced A* algorithm for mobile robot navigation

PurposeThe present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.Design/methodology/approachThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.FindingsThe paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.Research limitations/implicationsThe rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.Originality/valueThe originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.