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Journal ArticleDOI

A comparative review on mobile robot path planning : classical or meta-heuristic methods?

TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
About: This article is published in Annual Reviews in Control.The article was published on 2020-01-01 and is currently open access. It has received 92 citations till now. The article focuses on the topics: Motion planning & Dijkstra's algorithm.

Summary (4 min read)

1. Introduction

  • Navigation, an important factor in mobile robotics, is defined as the process of identifying the robot’s position accurately, planning the path, and following the path planned (Pennock, 2005).
  • As such, a reliable map is essential for navigation without which robots are not capable of accomplishing their goals.
  • This study explores the feasibility of meta-heuristic based approaches for motion planning and navigation of a mobile robot in static environments with numerous obstacles resulting in a hard to manoeuvre path.
  • Multiple start and destination locations are considered in this layout resembling paths with varying complexities raised from hard to manoeuvre corridors to obstacle dense environments.
  • Results and discussion are presented in Section 4.

2. Robot Motion Planning: Classical vs. Meta-Heuristic based methods

  • Heuristic-based and classical methods are two of the common categories of navigation and motion planning approaches.
  • Meta-heuristic approaches can overcome these issues due to their proficiency in handling unknown or partially known environments.
  • Duguleana and Mogan (Duguleana and Mogan, 2016) presented the combination of Neural Networks and Reinforcement Learning to enhance the capabilities of mobile robots to deal with static and dynamic obstacles.
  • Mac et al. also discussed about several classical algorithms and heuristic-based algorithms related to path planning, however, all the algorithms discussed focusing on the global path planning (Mac et al., 2016).
  • The details of methods considered in this study are presented in following sections.

Potential Field (PF)

  • Potential Field (PF) considers the differences between two opposite forces, known as attraction and repulsion, to help a robot maneuvering within the environment (Khatib, 1985).
  • The next maneuvering location is determined based on how close the final destination is and how strong the repulsive force of nearby obstacles are (Orozco-Rosas et al., 2019; Li et al., 2017; Mohammad et al., 2019).
  • Song and Kumar introduced a decentralized PF-based control unit for directing and maneuvering multiple agents in scenarios where they are tasked to find goals collaboratively and trap and lead them towards a final destination (Peng Song and Kumar, 2003).
  • One of the main concerns of PF is getting trapped in local minima.
  • This issue has been addressed by Cheng and Zelinsky using temporary high magnitude attraction forces at a random position to take the robot out of local optima (Cheng and Zelinsky, 1995).

Dijkstra’s Algorithm (DA)

  • Edsger Dijkstra introduced Dijkstra’s Algorithm (DA) in 1959, a classical algorithm that has proven its efficiency in finding the shortest possible path within a web of inter-connected nodes that represent spaces between obstacles (Dijkstra, 1959).
  • DA method is known to be computationally intensive and being less effective if the distance between the starting location and the destination is far from each other (Noto and Sato, 2002).
  • Noto and Soto (Noto and Sato, 2002) proposed an extended version of DA to solve this particular problem.

Rapidly-Exploring Random Tree (RRT)

  • The Rapidly-Exploring Random Tree (RRT), introduced by LaValle and Kuffner Jr., is an algorithm based on stochastic search strategies (Kuffner and LaValle, 2002).
  • Application of RRT in path planning is achieved by constructing a tree branching solution which revolves around random points selected within the searching space.
  • Moving forward, a random point, selected based on the closest node construct will be identified.
  • This step is repeated until the final destination is found and the outcome will form a feasible path which looks like a tree and covers almost all the empty space in the search space (Kuffner and LaValle, 2002; Connell and Manh La, 2018; Pimentel et al., 2018).
  • This variant uses the same concept and approach of global RRT with the main difference being lack of constructing a path first and execute it.

Probabilistic Road Map (PRM)

  • The main difference between Probabilistic Road Map (PRM) and RRT is in PRM’s ability in multi-query planning.
  • PRM was first introduced in 1996 by Kavraki et al. with their main goal being to provide a path for a robot in a static work-space (Kavraki et al., 1996).
  • The algorithm starts with selecting random points within the search area.
  • All these nodes are connected from one to another to determine their feasibility path.
  • In the final stage, the shortest path between the initial location and the destination is identified from the remaining feasible paths (Kavraki et al., 1996; Sudhakara et al., 2018; Kumar et al., 2017).

Genetic Algorithm (GA)

  • Genetic Algorithm (GA), introduced by John Holland in 1975, is a search optimization algorithm based on the mechanics of the natural process (Holland et al., 1992).
  • The fundamental principle of this approach is based on the survival of the strongest members of a population while the weaker members are abandoned.
  • Each member of a population is define as a chromosome where each chromosome represents a possible solution for an optimization problem.
  • The feasibility of the chromosomes are evaluated by measuring their fitness function.
  • GA updates its population, strengthening its found solutions , by generating off-springs based on their best through several processes or operators such as crossover and mutation.

Particle Swarm Optimization (PSO)

  • Kennedy and Eberhart introduced Particle Swarm Optimization (PSO) in 1995. r1,j and r2,j are random values in the range of [0,1] while w is the inertia weight that controls the influence of the last velocity in the updated version.
  • The efficiency of PSO’s performance depends on how to adjust, control, and update its parameters.
  • These parameter adjustment methods are all considered in this study.
  • The equation for updating the particles is as follows: xi,j(t) = xi,j(t− 1) + Vi,j(t) (4) PBesti,j and GBesti,j represent the best solution found by the particle and the best overall solution found by the swarm and can be updated using equations 5 and 6.

Differential Evolution (DE)

  • Differential Evolution (DE) algorithm, introduced by Storn and Price in 1997, is another population-based approach with considerable similarities to GA due to its use of crossover, mutation, and selection operators (Storn and Price, 1997).
  • The mutation operator is the main search mechanism for DE as it takes advantage of the selection operation to direct the search towards high potential areas in the search space.
  • The algorithm starts with random initialization of a population of solutions and ranks these solutions based on their fitness value acquired using an evaluation process.
  • The target vectors are mixed with mutated vectors and trial vectors are produced by: uji,G+1 = { vji,G+1, if Rj ≤ CR xji,G, if Rj > CR (9) where CR is a crossover constant and Rj is a random real number between [0, 1] with j denoting the jth components of the resultant array.
  • The exploitation behaviour and the exploration behaviour depends on the scaling factor in equation 7.

Cuckoo Search Algorithm (CSA)

  • The Cuckoo Search Algorithm (CSA) is another meta-heuristic approach, introduced by Yang and Deb in 2009 (Yang and Deb, 2009).
  • CSA has three fundamental steps: i) each cuckoo is only allowed to lay one egg in each iteration and this egg is to be laid in a random nest based on Levy flights; ii) high quality eggs and nests are to be preserved for the next generation ; and iii) the number of available host nests are fixed.
  • The probability of egg discovery by the host bird of each nest that cuckoo laid egg in is pa ∈ [0, 1].
  • Equation 11 represents Levy Distribution formula used by equation 10 for large steps.
  • The advantages of CSA includes having fewer number of parameters to fine-tune and its feasibility to deal with multi-modal objective functions.

3. Experiments Setup

  • As stated earlier, this study aims to investigate feasibility of meta-heuristic based methods in robot motion planning problems.
  • Linearly Decreasing Inertia Weight (LDIW) and Time Varying Acceleration Coefficient (TVAC), also known as TVAC-PSO.
  • Symmetric Layout Fig.3 illustrates the symmetric layout, also known as 13 Experiment II.
  • This layout has a combination of one irregular-shaped obstacle with four sets of obstacles (mixed dimensions).

Platform

  • The Turtlebot with a dimension of 30 cm width is used in combination with Robotic Operating System (ROS) to carry out the experiments.
  • Turtlebot is equipped with six infra-red sensors (acting as the main sensors) and 14 two bumpers.
  • The robot is protected from having a high speed collision that may cause a large amount of error in the odometry sensor data by instructing the robot to reduce its speed immediately after infra-reds detects any object presents within its sensing range.
  • Any collision that happens is considered as a controlled collision since the robot is programmed to stop immediately and perform a reverse mode to prevent the distortion of the odometry sensory data and accumulate unnecessary error in its odometry data.
  • The recovery steps implemented in this robot once it encounters possible obstacle collision are i) select a temporary go-to location away from the obstacle location, ii) rotate towards the identified temporary go-to location, iii) move towards the direction of the temporary go-to location for 2 seconds, iv) return to motion planner algorithm to construct a new path towards destination location.

Parameter Settings

  • Table 2 provides information regarding the parameter settings used in the study.
  • Experiments I and II (consists of 3 Sub-Experiments) are repeated with 10 runs and the results acquired are averaged.
  • A mobile robot (a Turtlebot) is assigned to maneuver through the obstacles scattered within the environments from a fixed starting point towards a fixed destination location.
  • Each node in the DA represents a centre point between two nearby obstacles and the distance between these nodes are considered as weights.
  • As discussed earlier, from existing meta-heuristic methods, GA, DE, CSA and variations of PSO are considered.

4. Results and Discussion

  • To evaluate the performance of the selected algorithms in experiments I and II, seven performance factors are considered with Dijkstra’s Algorithm (DA) is set as the benchmark of the best possible results that other algorithms 17 can achieved because it has full knowledge about the environment.
  • From the figure, DA is considered as the benchmark performance.
  • CPSO is the best performing algorithm compared to DE and RRT.

5. Conclusion

  • This study is focused on providing a comparison between conventional motion planning approaches such as Potential Field (PF), Dijkstra’s Algorithm (DA), Rapidly-explore Random Tree (RRT), Probabilistic Road Map (PRM) and a collection of meta-heuristic based methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Cuckoo Search (CSA).
  • The performance of meta-heuristic based methods are first evaluated under a collection of benchmark functions.
  • The results points to CPSO as the best performing algorithm by outperforming all other meta-heuristic based and conventional motion planning approaches investigated in this study.
  • CPSO is found to be among the top 3 performing algorithms across all experiments and performance measure factors considered in this study.
  • It is noteworthy that no statistical significance is found between CPSO and DA under travelled distance category in any of the motion planning experiments.

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Citations
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TL;DR: In this article , an interval multi-objective path planning (PP) scheme for patrol robot in nuclear power plant is presented, and the purpose of this PP scheme is to find collision-free paths with the shortest length and smallest risk degree.

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Journal ArticleDOI
17 Feb 2022-PLOS ONE
TL;DR: An improved A*-based algorithm is proposed, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning, and improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89%.
Abstract: Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the traditional A* algorithm has some limitations, such as slow planning speed, close to obstacles. In this paper, we propose an improved A*-based algorithm, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning. The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions. Bidirectional search is a strategy searching path from the start node and the goal node simultaneously. Smoothing improves path robustness by reducing the number of right-angle turns. In addition, simulation tests for the EBS-A* algorithm are performed, and the effectiveness of the proposed algorithm is verified by transferring it to a robot operating system (ROS). The experimental results show that compared with the traditional A* algorithm, the proposed algorithm improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89% and the number of right-angle turns by 100%.

17 citations

Journal ArticleDOI
TL;DR: The results show that the proposed method has out-performed to shorten the path length as well as ensured collision-free navigation and virtual intermediate targets (VITs) makes the navigation free from any dead-end situation, even in a cluttered environment.
Abstract: A navigation control methodology is proposed in this paper, which utilizes a hybrid concept of grey wolves optimisation (GWO) algorithm and an artificial potential field (APF) method for on-time path planning of mobile robot. The proposed methodology runs in two folds. The first fold defines the focus region (FR), which shows the obstacle-free locations of the all possible robot movements. However, in the second fold step GWO algorithm searches the shortest path by minimizing the artificial potential field value of the location generated within FR. The presented methodology was simulated and verified in various real and simulation environments. The simulation and experimental results reveal that the presented methodology can not only be capable of finding an optimal or near-optimal robot-path in a complex obstacle-present environment but also provides an effective path planning strategy on-time basis. The results show that the proposed method has out-performed to shorten the path length as well as ensured collision-free navigation. At the same time, virtual intermediate targets (VITs) makes the navigation free from any dead-end situation, even in a cluttered environment.

16 citations

Journal ArticleDOI
11 Feb 2022-PeerJ
TL;DR: In this article , a new set of stabilizing switched velocity-based continuous controllers was derived using the Lyapunov-based Control Scheme (LbCS) from the category of classical approaches where switching of these nonlinear controllers is invoked by a new rule.
Abstract: Robotic arms play an indispensable role in multiple sectors such as manufacturing, transportation and healthcare to improve human livelihoods and make possible their endeavors and innovations, which further enhance the quality of our lives. This paper considers such a robotic arm comprised of n revolute links and a prismatic end-effector, where the articulated arm is anchored in a restricted workspace. A new set of stabilizing switched velocity-based continuous controllers was derived using the Lyapunov-based Control Scheme (LbCS) from the category of classical approaches where switching of these nonlinear controllers is invoked by a new rule. The switched controllers enable the end-effector of the robotic arm to navigate autonomously via a series of landmarks, known as hierarchal landmarks, and finally converge to its equilibrium state. The interaction of the inherent attributes of LbCS that are the safeness, shortness and smoothness of paths for motion planning bring about cost and time efficiency of the controllers. The stability of the switched system was proven using Branicky’s stability criteria for switched systems based on multiple Lyapunov functions and was numerically validated using the RK4 method (Runge–Kutta method). Finally, computer simulation results are presented to show the effectiveness of the continuous time-invariant velocity-based controllers.

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Journal ArticleDOI
TL;DR: A novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings.
Abstract: Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

13 citations

References
More filters
Journal ArticleDOI
Rainer Storn1, Kenneth Price
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

24,053 citations

Journal ArticleDOI
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Abstract: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec~ to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to

22,704 citations

Proceedings ArticleDOI
01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.

5,521 citations

Journal ArticleDOI
01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Abstract: A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).

4,977 citations

Proceedings ArticleDOI
24 Apr 2000
TL;DR: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces by incrementally building two rapidly-exploring random trees rooted at the start and the goal configurations.
Abstract: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two rapidly-exploring random trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through, the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.

3,102 citations

Frequently Asked Questions (9)
Q1. What are the contributions in "A comparative review on mobile robot path planning : classical or meta-heuristic methods?" ?

This study explores the implementation of many meta-heuristic algorithms, e. g. Genetic Algorithm ( GA ), Differential Evolution ( DE ), Particle Swarm Optimization ( PSO ) and Cuckoo Search Algorithm ( CSA ) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra ’ s Algorithm ( DA ), Probabilistic Road Map ( PRM ), Rapidly Random Tree ( RRT ) and Potential Field ( PF ). Several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. 

The classical algorithms for motion planning considered in this study are Potential Field (PF), Dijkstra’s Algorithm (DA), Rapidly-explore Random Tree (RRT) and Probabilistic Road Map (PRM). 

The worst performing method in this performance measure factor is RRT where all sub-experiments have at least 15% performance difference compared to DA. 

CPSO closely followed with an average performance of 453.9 iterations and GA as the third best performing algorithm for this category with an average of 462.0 iterations. 

2. Execution Time (s): DA set the benchmark with an average execution time where it clocked only 87.55 seconds to complete the navigation task. 

5. Convergence Iteration: CPSO outperformed other meta-heuristic methods in average convergence iterations performance factor with 370.0 iterations. 

2. Execution Time (s): CPSO managed to outperform other algorithms including DA with 252.66 seconds on average across 10 executions. 

Within variants of PSO considered in this study, TVAC is found to be the worst performing variant since it has never achieved less than 5% performance difference from DA. 

PF and DE are the second and third best performing methods in this experiment due to their consistent high performances in most categories across all three sub-experiments in this layout.