Mobile robot navigation for complete coverage of an environment

Šelek, Ana; Seder, Marija; Petrović, Ivan

doi:10.1016/j.ifacol.2018.11.582

Cited by 4 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that, execution time is very crucial for robots in motion to take quick and intelligent decision before they collide with obstacles especially in situations where the speed of a vehicle is high and the environment is complex and dynamic. Moreover, most replanning algorithms consider replanning when obstacles are threat to the path without considering the dynamics of the specific obstacles to determine the threat level to the vehicle and the needed replanning response [28]. It is obvious that not all obstacles appearing in the replanning zone of a vehicle are threat to the vehicle to require replanning.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

View full text Add to dashboard Cite

This paper presents a novel path planning method for mobile robots in complex and dynamic environments using Voronoi diagram (VD) and computation geometry technique (CGT) termed, VD-CGT. An algorithm to categorize moving obstacles based on their positions, velocities, distances, and directions to ascertain their collision threat level and possible replanning decision is introduced. The initial path computation is done using morphological dilation, VD, A-star, and cubic spline algorithms. Instead of considering the entire map of the environment, CGT is used to compute a small rectangular region estimated to enclose a detected collision-threat obstacle and the current position of the robot. The roadmap is computed in the geometrical shape using VD and nodes are added to the initial roadmap nodes to compute a new path for replanning. To avoid increasing time and space requirements, these nodes are discarded before subsequent replanning is done. The results indicate better path replanning performance in complex and dynamic environments in terms of success path computation rate, path cost, time, and the number of replanning computations compared with other five popular related path planning approaches. The proposed method is efficient, and it computes safe and shortest replan path to goal with low computation time requirement. Unnecessary replanning computations are avoided which aid in reducing time and distance to get to the goal. With the performance results, the proposed method is a promising method for achieving safe, less path cost, and time in path replanning computations in complex and dynamic environments.INDEX TERMS Mobile robot, motion planning, path planning, unmanned autonomous vehicles, Voronoi diagram.

show abstract

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To evaluate the overall performance of the proposed model, the performance was compared with the methods such as Mobile robot navigation (MRN) [4], BA*: An Online Complete Coverage Algorithm for Cleaning Robots (BA*) [9] and Complete Coverage Path Planning Algorithms based on A* Algorithms (A*-CPP) [21]. The repetition rate of methods is used to compare the performance of methods.…”

Section: Discussionmentioning

confidence: 99%

“…There are generally two types of motion planning approaches for a cleaning robot. These are (according to the behavior of obstacles) off-line (or static) and on-line (or dynamic) motion planning [1,4]. In the case of stationary obstacles, robot routing uses a predefined decision.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

et al. 2019

View full text Add to dashboard Cite

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

show abstract

“…Another solution for TSP can be determined by using the Hopfield neural network (HNN) for an optimization that consists of a single layer containing one or more fully connected recurrent neurons [ 25 , 26 , 27 ]. The Spanning Tree Coverage (STC) algorithm [ 28 ] and the Replanning Spanning Tree Coverage (RSTC) algorithm [ 10 ] produce the optimal coverage path in linear time. The CCPP algorithms on a high-resolution occupancy grid map have increased coverage rate.…”

Section: Related Workmentioning

confidence: 99%

“…The existing coverage algorithms in the literature are either non-smooth so they have increased coverage redundancy due to the non-ideal path following, or they have slow path planning and replanning. The SCCPP algorithm combines two of our previous works: the fast coverage planning algorithm [ 10 ] with the fast clothoid calculation [ 11 ]. The first algorithm is our replanning spanning tree coverage (RSTC) algorithm that generates a path in a low-resolution occupancy grid map to reduce the computational complexity and minimize the overlap rate.…”

Section: Introductionmentioning

confidence: 99%

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

Šelek

Seder

Brezak

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.

show abstract

Mobile robot navigation for complete coverage of an environment

Cited by 4 publications

References 4 publications

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

Contact Info

Product

Resources

About