A Study on Building Optimal Path Planning Algorithms for Mobile Robot

Nguyen, Quoc My; Tran, Lam Ngoc Minh; Phung, Tri Cong

doi:10.1109/gtsd.2018.8595558

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…However, existing bug algorithms in the literature remain rather theoretical and are not suitable for application to navigation in real, GPS-denied environments because they typically rely on either a known global position or perfect odometry. For example, in (31,32), real-world robots used their wheel odometry for navigation within an indoor environment; nevertheless, the testing environments were too small to experience the full extent of the possible odometry drift. A flying robot typically relies on visual odometry and, due to the vibrations and texture dependence, is even more prone to odometry inaccuracies than a driving robot.…”

Section: A Minimal Navigation Solutionmentioning

confidence: 99%

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

et al. 2019

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Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find “victims” in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.

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Section: A Minimal Navigation Solutionmentioning

confidence: 99%

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

et al. 2019

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“…In the explainable intelligence model proposed by Keneni et al, the unmanned aerial vehicles can make decisions according to six rules when they are on the mission [2]. In [3], Nguyen et al improved K-Bug algorithm by introducing the fuzzy logic for boundary following. In order to achieve 3D AUV path planning, Sun et al designed a fuzzy system with accelerate/break module to enable the AUV avoid dynamic obstacles automatically [4].…”

Section: Related Workmentioning

confidence: 99%

“…These types of nodes are shown with different logos in Figure 4, the purple line represents the free space, and the blue area is the obstacle space. Based on this classification, the robot will get different reward at different state nodes, as shown in (3).…”

Section: ) the Static Reward Functionmentioning

confidence: 99%

The Experience-Memory Q-Learning Algorithm for Robot Path Planning in Unknown Environment

Zhao

Yang

et al. 2020

IEEE Access

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In order to solve the problem of slow convergence speed and long planned path when the robot plans a path in unknown environment by using Q-learning algorithm, we propose the Experience-Memory Q-Learning (EMQL) algorithm based on the continuous update of the shortest distance from the current state node to the start point. The autonomous learning ability of the robot is enhanced by the different role assignments of two tables in the proposed algorithm. EM table with (m * 1) dimension is designed to record the distance information, reflecting the learning process of the robot. Q table is adopted as an auxiliary guidance for the experience transfer strategy and experience reuse strategy, and these strategies enable the robot accomplish the task even if the destination is changed or the path is blocked. Further, the learning efficiency of the robot in the EMQL algorithm is improved by the dual reward mechanism consisting of static reward and dynamic reward. The static reward is designed to prevent the robot from exploring a state node excessively. The dynamic reward is responsible for helping the robot avoid searching blindly in unknown environment. We test the effectiveness of the proposed algorithm on both grid maps and road network maps. The comparison results in planning time, iteration times and path length show that the performance of the EMQL algorithm is superior to Q-learning algorithm in convergence speed and optimization ability. Additionally, the practicability of the proposed algorithm is validated in a real-world experiment using the Turtlebot3 burger robot. INDEX TERMSPath planning, Q-learning, experience memory, experience transfer, experience reuse. HUI LU (Senior Member, IEEE) received the Ph.D. degree in navigation, guidance and control from Harbin Engineering University, Harbin, China, in 2004. She is currently a Professor with Beihang University and a member of the Shaanxi Key Laboratory of Integrated and Intelligent Navigation. Her research interests include information and communication systems, intelligent optimization, and fault diagnosis and prediction.

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“…This is due to the fact that the heuristic approaches are close to the human way of behavior learning [17]. The classic algorithms in path planning consists of two types of the behavior; movement toward the target and movement around the obstacles [18]. The metaheuristic has been characterized as one of the most reliable methods for solving the complicated problems of optimization [19].…”

Section: Introductionmentioning

confidence: 99%

Hybrid metaheuristic approach for robot path planning in dynamic environment

Amar

Jasim

2021

Bulletin EEI

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Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

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A Study on Building Optimal Path Planning Algorithms for Mobile Robot

Cited by 7 publications

References 16 publications

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment

The Experience-Memory Q-Learning Algorithm for Robot Path Planning in Unknown Environment

Hybrid metaheuristic approach for robot path planning in dynamic environment

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