Path planning for robots: an elucidating draft

Sharma, Kaushlendra; Doriya, Rajesh

doi:10.1007/s41315-020-00129-0

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…Path planner uses multithreading to make efficient use of available computing power. We can determine whether it is preferable to run a node in the cloud or on a robot based on three criteria: the availability of local processing resources; the nature of the node; and the capacity of the surrounding network [2,8].…”

Section: Related Workmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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With the development of computer technology and artificial intelligence (AI), service robots are widely used in our daily life. At the same time, the manufacturing cost of the robots is too expensive for almost all small companies. The greatest technical limitations are the design of the service robot and the resource sharing of the robot groups. Path planning for robots is one of the issues playing an important role in every application of service robots. Path optimization, fast computation, and minimum computation time are required in all applications. This paper aims to propose the Google Cloud Computing Platform and Amazon Web Service (AWS) platforms for robot path planning. The aim is to identify the effect and impact of using a cloud computing platform for service robots. The cloud approach shifts the computation load from robots to the cloud server. Three different path-planning algorithms were considered to find the path for robots using the Google Cloud Computing Platform, while with AWS, three different types of environments, namely dense, moderate, and sparse, were selected to run the path-planning algorithms for robots. The paper presents the comparison and analysis of the results carried out for robot path planning using cloud services with that of the traditional approach. The proposed approach of using a cloud platform performs better in this case. The time factor is crucially diagnosed and presented in the paper. The major advantage derived from this experiment is that as the size of the environment increases, the respective relative delay decreases. This proves that increasing the scale of work can be beneficial by using cloud platforms. The result obtained using the proposed methodology proves that using cloud platforms improves the efficiency of path planning. The result reveals that using the cloud computing platform for service robots can change the entire perspective of using service robots in the future. The main advantage is that with the increase in the scale of services, the system remains stable, while the traditional system starts deteriorating in terms of performance.

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Section: Related Workmentioning

confidence: 99%

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

et al. 2023

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“…The benefit of using the CS algorithm is that the parameters to be adjusted are less than the GA and PSO. 38 Thus, the CS algorithm is more generic to apply to a broader class of optimization problem. A detailed review of the CS algorithm is presented by Parhi et al 39 The graphical representation of the working CS algorithm for robot path planning is shown in detail in Figure 1 in four different stages.…”

Section: Cuckoo Searchmentioning

confidence: 99%

Optimized cuckoo search algorithm using tournament selection function for robot path planning

Sharma

Singh

Doriya

2021

International Journal of Advanced Robotic Systems

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Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

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“…Finding a collision-free path in C free may seem like an easy task for a human agent, nevertheless, it is a very complex issue for artificial intelligence. This has been the main motivation for researchers and developers community which have worked in this area during the last five decades [2][3][4][6][7][8][9][10][11]. These efforts have generated diverse planning algorithms with particular characteristics that define its degree of completeness, approach, and configuration of problems that each planner is capable of solve.…”

Section: Planning Algorithmsmentioning

confidence: 99%

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

Diaz-Arango

Vázquez-Leal

Hernandez-Martinez

et al. 2020

Sensors

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The ability to plan a multiple-target path that goes through places considered important is desirable for autonomous mobile robots that perform tasks in industrial environments. This characteristic is necessary for inspection robots that monitor the critical conditions of sectors in thermal, nuclear, and hydropower plants. This ability is also useful for applications such as service at home, victim rescue, museum guidance, land mine detection, and so forth. Multiple-target collision-free path planning is a topic that has not been very studied because of the complexity that it implies. Usually, this issue is left in second place because, commonly, it is solved by segmentation using the point-to-point strategy. Nevertheless, this approach exhibits a poor performance, in terms of path length, due to unnecessary turnings and redundant segments present in the found path. In this paper, a multiple-target method based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments is presented. This method exhibits a better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM). Among the new schemes that improve their performance are the Double Spherical Tracking (DST), the dummy obstacle scheme, and a systematic criterion to a selection of repulsion parameter. The case studies show its effectiveness to find a solution path for office-like environments in just a few milliseconds, even if they have narrow corridors and hundreds of obstacles. Additionally, a comparison between the proposed method and sampling-based planning algorithms (SBP) with the best performance is presented. Furthermore, the results of case studies show that the proposed method exhibits a better performance than SBP algorithms for execution time, memory, and in some cases path length metrics. Finally, to validate the feasibility of the paths calculated by the proposed planner; two simulations using the pure-pursuit controlled and differential drive robot model contained in the Robotics System Toolbox of MATLAB are presented.

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Path planning for robots: an elucidating draft

Cited by 8 publications

References 67 publications

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Development of Cloud Autonomous System for Enhancing the Performance of Robots’ Path

Optimized cuckoo search algorithm using tournament selection function for robot path planning

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

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