Adaptive path planning for unknown environment monitoring

1.1.Literature survey

Path planning algorithm for environmental exploration has two kinds. The first was myopic, which selects one step at a time as its next move. The study does not address the lack of anticipation mechanisms, which could be tricky in route planning and decision-making. The second technique was based on a partially observable Markov model that consigns a reward to each allowed action sequence. The computational complexity in non-myopic techniques was high. [33]. Using the evidential gird framework, Mouhagir et al. presented trajectory planning for autonomous vehicles in an uncertain scenario. The presented study models the integration of environmental uncertainty with an evidentiary grid, demonstrating great performance in avoiding barriers. The robotized vehicle was not implemented [8].The authors presented a novel acquisition technique for informative path planning, which was created for the purpose of detecting anomalies in exploration missions. The proposed algorithm was tested on synthetic test functions in the real world. The authors propose that a three-dimensional (3D) search space be added to the two-dimensional (2D) path parameterized technique. For complex time dependency of the environment, a covariance function was proposed, which encodes particular spatiotemporal surveillance.

The research was employed to detect anomalous quantities in a certain area of chemical substance, contaminants, or radioactive substances, as well as need to re-create a landscape with undefined terrain for large-scale architecture [38]. The research introduced an innovative deterrent deliberate IPP calculation that was useful for UAV target search problems. To generate the ideal, limited skyline 3D polynomial route in a major impediment climate, the organizer strikes a balance between inclusion, obstruction aversion, target re-perception, height subordinate sensor execution, flight time, and FoV. The presented layered improvement strategy was effective against fraudulent identifications since it was combined with a good investigative abuse mechanism.

In terms of search efficacy, the presented organizer outperforms the non-versatile IPP organizer, the inclusion organizer, and the arbitrary examining organizer [21]. The calculation took into account a wide sort of climate complications and barrier densities. Despite multiple bogus human recognitions by the sensor, it successfully tracked down all of the persons a meaningful SAR reform on the ground, demonstrating the vigor provided by the layered advancement technique. The organizer’s primary flaw was that it assumes a constant and predictable climate. Furthermore, for target inhabitance, a non-transient field was expected.

Researchers employed an informative continuous space path planning algorithm, evaluated the measured data online, and reasoned near the suitable measurement point. The presented method permits the robot to replan adaptively sampling in position, so that focus on a specific feature of interest. That technology was used in UAV-based gas distribution mapping, ground ice mapping with a rover, and monitoring with a catamaran-type ASV [7]. Bricher and teams introduced Receding Harizon planner for a known or unknown environment, the suggested planning approach finds noble paths for inspecting an a priori known surface. Additional effort might be devoted to developing plans for numerous robotic platforms, such as large-scale trials, fixed-wing Drones or automated machines [36].

SankalpArora and Sanjiban invented Partially Observable Markov Decision Processes (POMDP) were a simple way to simulate sequential decision making in uncertain situations. MDP-POMDP solvers accomplish multi-resolution gen collection task poorly at the budgeted and do not take advantage of UAV agility, rendering them inappropriate for such applications [5]. Olov Andersson presented a combining optimum and Actual planning of physically feasible pathways in tough situations with dynamic barriers by receding-horizon control with space-time multi-resolution lattices. Researchers have shown the context in action via handling complex indoor 3D quad copter situations of navigation that need planning ahead of time. Waiting for other people and quad copters, as well as taking diversions around them [30]. Qing and team explores path searching, it records all equidistant shortest paths and chooses the best path among numerous equidistant paths based on the turn time, resulting in the path with the shortest distance and time [47].

Yu Feng, Shaoshan Liu and Yuhao Zhu proposed spatially encoding the sequence’s key for point-cloud, which was usually the middle one. The K-spatial frame’s encoding discoveries utilized to temporal encode the remaining point clouds, which refer to as expected point clouds (P-frames) (P-frames). Autonomous machines was able to connect more with each other and cloud using well organized point cloud compression, entering a new period of distributive and cloud robotics. The study by Rekleitis et al. [42] presents a spatio-temporal technique for compressing Light Detection and Ranging (LIDAR) point clouds. For a variety of reasons, IoannisRekleitis and Jean-Luc Bedwani have chosen a LIDAR as the primary sensing mode: for example, the mobile device has vastly limited ground consent. LIDAR sensor can provide range data that can be used to construct landscape models with a resolution of 1–2 cm. Most stereo vision systems would struggle to achieve such precision over the whole measuring range. Such precision was also critical for the mission’s scientific return, which includes detecting rock formations.

Furthermore, without the need for extra processing, LIDAR sensors deliver precise 3D geometric information in the shape of a 3D point cloud [6]. The planning of a receding horizon trajectory based on optimization approach [18] introduced by Kristoffer Bergman for nonlinear dynamics functioning in a congested and unstructured environment. To handle the 3D Path Planning problem in a dynamic setting, Utkarsh Goel and Shubham developed on the Glow-worm Swarm Optimization (GSO) algorithm. The application of Swarm based Intelligence allowed for more rapidly convergence, which increases the efficiency of path planning. Based on findings obtained when completing the model, the feasibility of Path planning for UAVs in a 3D Dynamic Environment was extrapolated utilizing the presented approach [27].

In Chen et al. [11], path planning and obstacle avoidance of USV based on enhanced ant colony optimization-artificial potential field (ACO-APF) hybrid algorithm with adaptive early warning was described. The technique described was based on a grid map for USV local and global path planning in dynamic situations. The ACO algorithm sought a globally optimal path from start to endpoint for USV in a grid environment, whereas the APF method avoided unexpected obstructions during USV navigation. It delivers greater accuracy while using a smaller map size.

Cao et al. [9] showed 3D Snake-Like Robot Motion Direction Control and Adaptive Path Following. The adaptive path-following was based on the line-of-sight guiding law and was used in conjunction with the direction control approach to steer the robot forward and along desired courses. The findings show the effectiveness of the given 3D model and control approaches. It outperformed the standard and widely used path-following model. It has a longer map duration but a shorter patch length.

Abed et al. [1] demonstrated an online optimisation application for path planning in unknown environments. Grey Wolf Optimisation (GWO) variants MGWO1 and MGWO2 were proposed for online route planning to help the mobile robot reach the target using the shortest path and avoid obstacles in unexpected surroundings. To avoid abrupt curves, a cost function was created by combining the path smoothing parameter with an integrated distance function. The findings showed that the provided GWO, MGWO1, and MGWO2 algorithms were capable of successfully ignoring barriers in a local minima condition. It has a faster execution time and a smaller map size.

In Tran et al. [52], adaptive trajectory tracking for quad rotor systems in uncertain wind settings using particle swarm optimization-based strictly negative imaginary controllers was described. The notion of adaptive strictly negative imaginary (SNI) controllers is introduced by particle swarm optimization. The provided adaptive control methods reduce a certain performance index, showing the control design’s goal. It gives greater precision while having a shorter patch length.

From the literature, the research gaps are identified by the authors and listed here, a variety of robotic platforms, such as large-scale test scenarios, UAVs, or robot arms have to develop to maximize the performance of autonomous vehicle [36]. First, the presented receding horizon planner has been designed to work in dynamic contexts. The presented approach scaled to bigger areas in the future, and the mapping model extended to capture temporal dynamics. It allows the use of previously gathered data as a baseline in long-term monitoring missions to increase real-time performance. Second, the receding horizon planner has been designed to work in dynamic contexts. Another extension is to use principles from fast MPC to increase real-time performance [6]. Third, to improve robustness, obstacle motion uncertainty must explicitly be included [30]. Fourth, the path planning process can be combined with Deep Learning or SLAM to assist UAVs to create a real-time map of the environment [18].

This work uses the Gmapping SLAM method using a mono laser sensor for mapping and localization. Furthermore, the Data-matching method is employed to support a more exact map based state position estimation. A portable system is a system that is portable in terms of hardware intended to run an algorithm. The research’s scope is to create a rough map of the area using portable devices. In the shortest amount of time and for the least amount of money, a handheld system is created. Pre-mapping is the term for this procedure. Premapping provides a solution to the chicken-and-egg conundrum other difficult issues in SLAM algorithms [31]. In this investigation, a single laser is used to maintain the solution. Sensor that uses the Gmapping technique only using a laser sensor measures as a data source for localization and mapping. In varied settings, just laser pixel intensities, IMU sensor and wheel encoder were employed for identification and mapping as data for pre-mapping. Pre-mapping is a low-cost solution to problems with autonomous exploration and navigation.

1.2.Contribution

The goal of this research is to propose a realistic solution to the problem of continuous monitoring in an unknown environment using Unmanned Ground Vehicles (UGV). This method is used to detect the Indian agriculture fields which does not have proper road. It is not having defined road signs and objects like vehicle, pedestrian. But these roads may have uncertainties like varieties of plants, trees, ridges, size of the path, etc. So, extracting or identifying such type of path helps to navigate the robots in the form field. It help to extent for many type of applications related to agriculture purpose. The subject was investigated in terms of collect the data about the environment, interpret the data for modelling and path planning, finally control the vehicle to follow path. The presented method contains the following five contributions when compared to related work and existing technology.

The remaining sections of this article are organized as follows: Section 2 describes the problem formulation for SLAM based persistent monitoring problem for agriculture environment. In Section 3, the PMP’s decision variable in SLAM based path planning is proposed. System design for persistent monitoring enabled robot using Hyb-SLAM-AStar algorithm is discussed in Section 4. In Section 5, experimental results are proved, Section 6 portrays the discussion and Section 7 presents the conclusion and future work.

2.1.Problem definition and assumptions

Likewise the above mentioned task of warehouse, the uncertainty issue is solved by modifying the motion parameter and lowering velocity prior to the start of the study in order to avoid the motion route from diverging from the planned path due to the too high velocity at the entrance and exit points. The robot’s initial position and target are pre-set, the robot recognizes the global map outline (walls of warehouse), i.e, the problems in map are entirely unidentified. The aim of the robot is to reach the destination while keeping a safe distance from the obstacles. The robot can reach the destination at any velocity and does not need to stop.

In addition to the global map’s layout, robot’s many sensors provide all the information it can (gyroscope, lidar, encoder). These sensors can be used to gather the data needed by the proposed method at each time step. Make given assumption regarding navigation issue.

The robot is differentially propelled and considers itself to be moving forward along a rotating axis. Equation (4) depicts the robot’s kinematics theory.

3.1.Simultaneous Localization and Mapping (SLAM):

SLAM allows the rapid creation of navigable outlines that converts as navigable spaces and maps. This is impractical to use manual methods for mapping indoor environments, because of complexity and size of indoor space. Traditional SLAM has been used primarily in the field of robot navigation, requiring high level of output precision in order to be useful for robot movement maps. This has prompted research on more effective and quick sensors, measurement techniques, and measurement data post-processing. However, every improvement in accuracy has been accompanied by an increase in processing or equipment costs.

The Robot Operating System (ROS) platform, which is existing in Ubuntu, may be used to do SLAM, It is among the approaches for mapping robots on the move. GMapping is used as the algorithm for this study. The responsibilities of cartography, positioning, target tracking tasks, overlapping hitches. The difficulty of constructing a map while simultaneously localizing a robot within that map is known as SLAM. Both tasks can’t be decoupled and solved separately. Localization requires a good map, whereas building a map necessitates an accurate pose approximation. To improve the pose estimation, the goal of effective localize is to guide the robot to specific locations on the grid. Exploration approaches, on the other hand, presume precise posture knowledge and concentration on efficiently directing the vehicle across the surroundings in order to develop a map.

Fig. 2.

Key component process of the robot.

Solutions to the SLAM problem are sometimes known as integrated techniques. The key component process of the robot using SLAM algorithm is shown in Fig. 2. The Rao-Blackwell zed Particle Filter (RBPF) is utilized by G Mapping to collect data laser sensors and other sources. Make a 2D grid map with a robot stance [50]. In essence, the fundamental issues in map learning are listed as what place to steer a robot on a mission of self-exploration, how to handle noise in posture estimate and observations, and how to manage with ambiguity and how to interpret it data from sensors, how to effectively manage a team of mobile robots and variations in the echo system over time [59]. The mapping flow procedure is depicted in Fig. 3.

Fig. 3.

Environment mapping flow process.

There are a number of advantages to using GMapping as the primary HSAStar technique in this work. For starters, it’s an open source, algorithm which is ready to use, that’s part of the Robot Operating System (ROS). Second, it is deep rooted and has a proven track record, as evidenced by recent publications such as [31]. Finally, because the approach is a solo threaded technique in which each element is calculated separately, it lends itself nicely to parallelization. This is a good situation named at parallelization because there is no want to transfer data to particles. Whenever the robot acquires a fresh laser shot, the program executes steps outlined in the flowchart in Fig. 3. The vehicle alters the posture of the whole examined particle throughout execution based on odometry estimate using its motion model. The first scan is immediately recorded in the map when it is received. After that, only if the robot’s linear or angular distance traversed exceeds specific thresholds is registration performed. After that, data acquisition match is performed, which corrects pose estimation in each particle’s map. As a result, the particle tree’s weights have been adjusted, as has the effective sample size Pef is used to determine whether the present particle set performs posteriorly resembles goal particle set.

For posture estimation and map generation, this study uses the data-matching approach then the grid representation using Gmapping algorithm. As illustrated in (4), HSAStar algorithms are frequently built on a common filter using probabilistic Bayes, with specific data being used to assess the density of an unknown probability density. The Bayes Filter, also known as Recursive Bayesian Estimation, is the foundation of several probabilistic techniques in robotics. A state, such as a robot stance, is represented by a Probability Density Function, or pdf, in Probabilistic Robotics. Another term is belief, which refers to the robot’s internal knowledge, rather than ours, from its perspective. The Bayes Filter is a general method for iteratively estimating a state’s pdf using a Mathematical Process Model and Measurements.

3.2.A Star algorithm for point cloud data

A Star is equivalent to Greedy Better in that it uses a heuristic to direct itself. In the simplest instance, it’s as speedy as Greedy Best-First-Search. In standard A∗ terminology, g(x) denotes the accurate and appropriate path from the beginning point to every vertex x, and h(x) means the empirical approximate value from vertices n to the target. A∗ maintains a delicate equilibrium as it progresses by the original position to the objective. Then the parameters such as ϑ, ϖ, x and β represents optimization parameter for optimally reduce the uncertainty problem, such as robot’s linear velocity with angular velocity and robot’s linear acceleration with angular acceleration. These parameters are optimized using A star algorithm. The main loop seeks for the node x to the minimum function f(x)=g(x)+h(x). The heuristic technique h(x) gives A∗ an estimate of the least expensive path on each nodes n to the destination A. Pseudocode for A Star Algorithm is represented in Algorithm 1.

Algorithm 1

AStar (point cloud data)

3.3.Grid mapping

The Gmapping method is employed as a HSAStar approach in this investigation. It employs the particle filter to acquire lattice representation after laser range sensor data. Using laser scan measurement data z1:t=x1,…,xt, the joint posterior (4) between map mt and the robot’s state x1:t=x1,…,xt has been computed from the laser scan z1:t=z1,…,zt.

Algorithm 2

Hyb SLAM A Star (point cloud data)

ego_Moving Update: A ego_Moving update adjustment function of pc is set based on the discussion of the reward coefficient pc to increase continuous observation performance. Restraint backwards: The go_back of the UGV will unavoidably occur in path planning because of the heuristic technique’s randomness and the change in payout. When the go_back occurs, however, it is difficult to assess if this decision is sensible or not. As a result, the suggested approach allows the UGV to return to its new path. Visited_Location: UGV is said to be in a loop closure once the path budget exceeds payment or when the budget of various positions are equal. To boost monitoring efficiency, when a loop closure occurs, the viewpoint by the greatest payment of altogether neighboring viewpoints at present prespective is picked as the goal points for the following second.

4.1.Body of a mobile robot platform

Mobile robot’s structure made up of 2 layer of acrylic plate. It measures 35 centimeters by 20 centimeters by 15 centimeters and weighs about 3 kg. It may make a range of sensors and devices to fulfill the experiment’s necessities. The authentic thing of a ground vehicle is depicted in Fig. 4. The experimentation is done on unknown environmental along the obstacles of static or dynamic to examine the proposed approach proficiency. Every experimental environment set up in office building’s corridor. Here, run global path planner to differentiate variation of orientation path created via global path planner as well as real trajectory derived from the approach. Nevertheless, the reference path does not take part in robot navigation. The original scenario has no constraints, its 2D map created through concurrent localization with mapping to reflect the quality of “unknown.” The actual environment equipped with constraints for navigation. The robot is unaware of the obstacles and can only detect them while it is navigating. These obstacles spatial distribution is set to considerably intercept the robot trajectory towards the goal. A power unit offers the essential energy of controller unit that oversee any actions enabled by the robot. As a robust less cost solution, the Arduino Uno micro controller equipped with appropriate motor shield. A driving unit is necessary to move the robot at X and Y directions. This is accomplished by dual gearbox equipped with two brushed DC motors. Two extra ball caster wheels offered stability when driving. Several sensors are utilized to track the robot progress, also speed encoder mounted on one wheels, an inertial sensor to sense the robot alignment is required. The digital magnetometer presented information regarding current heading of SLAM robot. A sensor unit is required to sense the environment. To attain a proper field of vision, the unit is mounted in rotating servo motor. The environmental conditions based numerous kinds of sensor can be applied. In this case, a low-cost LIDAR device is employed to scale the distance of any obstacles. Also, an ultrasonic range finder is employed, because laser-base sensors could not detect the glass elements. To identify changes in floor level (where there are stairs), an additional device pointing downwards by 45 degrees is suggested.

Fig. 5.

Architecture of a mobile robot’s hardware system.

The mobile robot walks with the help of a four-wheeled walking gadget. A brush motor 6V Gear Micro DC Encoder [53] propels each tyre, allows the vehicle to run at speeds of up to 1 m/s and swivel at a rate of 6.6 rad/s. The robotic tyre speed and displacement are measured using 360 line AB 2 phase continuous rotary encoder. A robot platform control scheme and server make up majority of the robot manipulator hardware system. Figure 5 depicts the structural block diagram. A principal processor, a VL53L1X Lidar Sensor, Inertial Measurement Unit (IMU) unit, a location controller, 22.2V LIPO Lithium Polymer Battery power supply module makes the open source robot platform control system. It’s not only proficient of autonomously directing and placing the mobile robot. Object detection and other aspects, as well as the right to receive server software to send commands to direct the robot’s movement and a range of sensor data sent over WIFI to the server. An ESP8266 Node MCU Development Board with 1GB of RAM and a range of connectors, such as a built-in 10/100 dynamic network adapter, an 802.11n WIFI wireless USB network card, and a server wireless network card, serves as main controller (3D printed using PLA material).

The bottom driver board Node MCU and Raspberry pi 4 is the key component of the position control module. The Node MCU Motor Shield will be the speed controller for 366PRM DC brush motor. The Motor Shield measures the speed at which the object moves and the direction of the robot manure is controlled by movement of side wheels. Determine the robot’s orientation, direction, and acceleration in 3D space, you’re moving. Motor Shield employs PID control to enable the robot to interact with the main controller as well as move precisely. The speed, angle, mileage of robot’s wheels determine its location. The main controller uses the VL53L1X Lidar Sensor laser sensor to capture 2D laser data from the environment, then wirelessly transfers it to the server utilizing ROS distributed processing to make a map of world around the robot.

A 22.2V 2800mAh large rechargeable lithium battery powers the mobile robot platform. Three 18650 lithiumion battery packs make up the battery, which is enough to power the complete system. Lenovo Y480 machines are employed on the server. Ubuntu 16.04 and the Free Software Robotics Operating System are installed in a virtual computer (ROS). The mobile robot platform’s principal controller first shifts the robot’s 2D laser data into 3D space, viz, orientation, velocity, distance, robot’s environment. Map of complete unfamiliar part is regularly generated by managing the robot’s behavior at the host.

4.2.Model of software for mobile robots

The trials are carried out in a two-wheel mobile robot equipped with an Intel Core i7-4500U 1.8 GHz CPU, 8 GB of memory, and a Robot Operating System running in a computer equipped with four Intel Core i7 2.2 GHz processors, eight gigabytes of RAM, and an Intel HD Graphics 6000 graphics card. The ROS system framework builds a platform that links all activities and installs the bulk of the key feature modules and packages [25,51], mostly from the ROS community, after install the complete desktop ROS version. By customizing the robot software system to own robot as well as generating more node and processing packaging, the gained general resources may be simply updated to finish the creation of the robot software system. This study takes extensive usage of ROS distributed processing architecture because map development and vehicle location need a significant count of data processing using a single card type computer is difficult. First, the host and the robot head controller are on the same network and are coupled to the same LAN. On the server, construct Node Managers and keyboard trouble shoots, IMU errors, autonomous vehicle linear and angular velocity adjustments, SLAM nodes in mobile robot host controller, Lidar, Odometer, and Base Controller nodes.

The node manager can then employ a peer-to-peer architecture on the current web to execute TCP/IP message and achieve successful nodes on separate hosts can communicate with each other after all of the nodes have been registered and monitored by the node management. Finally, perform the SLAM job on the server in an unknown area using the 3D visualization application RVIZ. The node topology is considered for robots in the ROS system framework. The processes that the RVIZ tool may debug are mostly handled by the server-side node: To implement SLAM and run at RVIZ, obtained objects such as lasers signals and postures from the robot control system laser sensor and odometer node, respectively. There are options in both horizontal and angular velocity adjustments. To improve the precision of adjustment, change the line tempo and its correction codes.

4.3.Programming for point regulator

As a node of ROS, the point regulator mode analyses the straight speed with angular velocity data provided through the core processing center, to determine parameters that the robot must be able to move in both wheels. The robot follows the trajectory given by the host’s movement instruction and uses laser distance meter to avoid barrier. Finally, robot communicates the mileage node attitude parameters including flow rate, angular momentum, and movement. Figure 6 shows the flowchart for the software.

Fig. 6.

Programming for point regulator.

6.1.Experimental scenario map or scenario for some sample trails

The map or scenario for some sample trails, A star algorithm is upgraded for feasible performance of navigation, then the trained agent is acquired via complex environment having certain trails, trial 1 is given in Fig. 12, trial 2 is given in Fig. 13, trial 3 is given in Fig. 14. The proposed approach handles using the picture including simple changing constraints. Original scenario map fabricated through Simultaneous Localization and Mapping is given in Fig. 13(a), the real environment to navigation is represented in Fig. 13(b). Here, a person randomly walks at scenario who considerably intercepts the robot path twice to target. Figure 14 depicts temporal series of robot navigation snapshots visualized in ROS, viz, related positioning of real robot in experiment. Figure 14(a) portrays initial place, objective and orientation path. Reference path goes straight to goal owing to lack of obstacle in the original map. Figure 14(b)–(g) displays the robot movements averting the moving pedestrian. The robot steers away from this pedestrian when the pedestrian hinders the robot motion (Figs 14(b) to 14(f)). If the person is away from the robot, the robot back to the direction of goal (Figs 14(d), (g)). The real path as robot attains the target is depicted in Fig. 14(e). Here, the real trajectory averts the moving pedestrian, also vary from reference path that exemplifies the proposed approach proficiently navigate the robot in simple dynamic situation.

Fig. 13.

(a): Experiment scenario original map of sample trails 1, (b) real experiment scenario.

Fig. 14.

Procedure of robot experimentation 2. (a)–(h) displays robot location at chronological sort.

6.2.Performance metrics

Here, the performance measures like accuracy, map size, specificity, patch length, map duration and execution time are looked at in order to assess performance.

6.2.5.Execution time

Execution time is a term used in environmental monitoring to refer to the total amount of time required to complete a monitoring program. The execution time is an important consideration in environmental monitoring, as it affects the overall cost and efficiency of the program.

The execution time can be mathematically defined using the following equation (16),

(16)T=n∗(Tm+Ta)

where T is the execution time (in seconds), n is the number of monitoring locations, Tm is the time required to collect data at each monitoring location, and Ta is the time required for data analysis.

The map can saved periodically; for the mapping procedure in this study, the SLAM approach is combined with the GMapping algorithm. Three trials with varying robot velocity, map update frequency, and particle filter parameters are established to see how they affect mapping quality. Figure 15 represent the mapping accuracy for different trails. The mapping and navigation process can be observed using the ROS “rviz” interface. Inside the ROS, the multisensory data provided by the RPLIDAR360o laser scanning and two optical progressive encoders were processed. It’s more convenient and quick this way. As it develops, it becomes a method for long-distance navigation and mapping.

Fig. 15.

Accuracy analysis.

Figure 15 shows the accuracy analysis. The proposed hyb SLAM-A Star-APP method provides 22.35%, 29.67%, and 28.67% higher accuracy for trial 1; 32.56%, 29.06% and 29.05% higher accuracy for trial 2; 25.32%, 35.48% and 54.28% higher accuracy for trial 3 than the existing methods such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP respectively.

Figure 16 shows the map size analysis. The proposed hyb SLAM-A Star-APP method provides 23.65%, 29.67%, and 32.56% lower map size for trial 1; 36.52%, 22.17% and 34.89% lower map size for trial 2; 31.08%, 29.63% and 32.56% lower map size for trial 3 than the existing methods such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP respectively.

Fig. 16.

Map size analysis.

Fig. 17.

Map duration analysis.

Fig. 18.

Patch length analysis.

Figure 17 shows the map duration analysis. The proposed hyb SLAM-A Star-APP method provides 36.29%, 27.68%, and 35.68% lower map duration for trial 1; 21.36%, 29.64% and 29.67% lower map duration for trial 2; 41.32%, 30.27% and 42.35% lower map duration for trial 3 than the existing methods such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP respectively.

Figure 18 shows the patch length analysis. The proposed hyb SLAM-A Star-APP method provides 23.65%, 29.68%, and 24.58% lower patch length for map 2; 27.38%, 33.21% and 29.68% lower patch length for map 4; 23.65%, 29.36% and 31.05% lower patch length for map 6 than the existing methods such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP respectively.

Fig. 19.

Execution time analysis.

Figure 19 shows the execution time analysis. The proposed hyb SLAM-A Star-APP method provides 32.10%, 29.68%, and 22.47% lower execution time for map 2; 32.15%, 33.21% and 32.10% lower execution time for map 4; 23.10%, 45.36% and 22.10% lower execution time for map 6 than the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP respectively.

7.1.Future work

The proposed algorithm attains more accurate result in terms of map generation, pose estimation in unknown condition. The experiment concludes that the proposed hybrid SLAM-A star algorithm is effectual as well as practical for optimizing motion and velocity in uncertainty. The tuning model hints to certain merits in real-life as well as industrial application, because it save time to novice control engineers to reach ideal motion with velocity in the parameters uncertainties, also enhance the performance superiority. However, it is limited due to high power consumption. In future work, low power consumption robots can be designed and deep learning method with optimization algorithm can be used.