scholarly journals Local path planning for mobile robots based on intermediate objectives

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yingchong Ma ◽  
Gang Zheng ◽  
Wilfrid Perruquetti ◽  
Zhaopeng Qiu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Kinematic Model ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Local Path ◽  
Velocity Constraints ◽  
Horizon Planning

SUMMARYThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real-time environment.

scholarly journals 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuexi Zhang ◽  
Jiajun Lai ◽  
Dongliang Xu ◽  
Huaijun Li ◽  
Minyue Fu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Basic System ◽  
Rescue Robot ◽  
Planning Algorithm ◽  
Local Path ◽  
Slam Algorithm ◽  
Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

scholarly journals Review on path planning algorithm for unmanned aerial vehicles

2021 ◽  
Vol 24 (2) ◽  
pp. 1017
Author(s):  
Nurul Saliha Amani Ibrahim ◽  
Faiz Asraf Saparudin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

scholarly journals Ship Motion Planning for MASS Based on a Multi-Objective Optimization HA* Algorithm in Complex Navigation Conditions

2021 ◽  
Vol 9 (10) ◽  
pp. 1126
Author(s):  
Meiyi Wu ◽  
Anmin Zhang ◽  
Miao Gao ◽  
Jiali Zhang
Keyword(s):  
Motion Planning ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Kinematic Model ◽  
Ship Motion ◽  
Planning Problem ◽  
Navigation Systems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Three Degree Of Freedom

Ship motion planning constitutes the most critical part in the autonomous navigation systems of marine autonomous surface ships (MASS). Weather and ocean conditions can significantly affect their navigation, but there are relatively few studies on the influence of wind and current on motion planning. This study investigates the motion planning problem for USV, wherein the goal is to obtain an optimal path under the interference of the navigation environment (wind and current), and control the USV in order to avoid obstacles and arrive at its destination without collision. In this process, the influences of search efficiency, navigation safety and energy consumption on motion planning are taken into consideration. Firstly, the navigation environment is constructed by integrating information, including the electronic navigational chart, wind and current field. Based on the environmental interference factors, the three-degree-of-freedom kinematic model of USVs is created, and the multi-objective optimization and complex constraints are reasonably expressed to establish the corresponding optimization model. A multi-objective optimization algorithm based on HA* is proposed after considering the constraints of motion and dynamic and optimization objectives. Simulation verifies the effectiveness of the algorithm, where an efficient, safe and economical path is obtained and is more in line with the needs of practical application.

Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Path Planning ◽  
Data Acquisition ◽  
Large Scale ◽  
Cluster Formation ◽  
Optimal Path ◽  
Research Work ◽  
Planning Problem ◽  
Sensing Data ◽  
Planning Algorithm ◽  
Load Balanced

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

scholarly journals An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2640 ◽  
Author(s):  
Junfeng Xin ◽  
Jiabao Zhong ◽  
Fengru Yang ◽  
Ying Cui ◽  
Jinlu Sheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Autonomous Navigation ◽  
Travelling Salesman Problem ◽  
Optimal Path ◽  
Convergence Speed ◽  
Planning Problem ◽  
Unmanned Surface Vehicle ◽  
Domain Inversion ◽  
And Control

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.

scholarly journals An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 2738-2743
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Object Identification ◽  
Planning Problem ◽  
Networked Robots ◽  
Design Choice ◽  
Planning Algorithm ◽  
3D Cnn ◽  
Path Planning Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks

Target Estimate PDF-Based Optimal Path Planning Algorithm With Application to UAV Systems

2010 ◽  
Author(s):  
Jared G. Wood ◽  
Benjamin Kehoe ◽  
J. Karl Hedrick
Keyword(s):  
Path Planning ◽  
Optimization Problem ◽  
Optimal Path ◽  
Finite Horizon ◽  
Path Optimization ◽  
Optimal Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Insight Into

Companies are starting to explore investing in UAV systems that come with standard autopilot trackers. There is a need for general cooperative local path planning algorithms that function with these types of systems. We have recently finished a project in which algorithms for autonomously searching for, detecting, and tracking ground targets was developed for a fixed-wing UAV with a visual spectrum gimballed camera. A set of scenarios are identified in which finite horizon path optimization results in a non-optimal ineffective path. For each of these scenarios, an appropriate path optimization problem is defined to replace finite horizon optimization. An algorithm is presented that determines which path optimization should be performed given a UAV state and target estimate probability distribution. The algorithm was implemented and thoroughly tested in flight experiments. The experimental work was successful and gave insight into what is required for a path planning algorithm to robustly work with standard waypoint tracking UAV systems. This paper presents the algorithm that was developed, theory supporting the algorithm, and experimental results.

scholarly journals Design of basketball robot based on behavior-based fuzzy control

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090996 ◽  
Author(s):  
Yonghong Zhi ◽  
Yan Jiang
Keyword(s):  
Path Planning ◽  
Fuzzy Control ◽  
Autonomous Navigation ◽  
Strong Dependence ◽  
Fuzzy Rule ◽  
Planning Problem ◽  
Unknown Environment ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Robot Configuration

Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.

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