scholarly journals Tangent Based-Path Path Planning with Distinctive Topologies in a Dynamic Environment

2021 ◽  
Author(s):  
Zhuo Yao
Keyword(s):  
Shortest Path ◽  
Optimal Trajectory ◽  
Dynamic Environment ◽  
Elastic Band ◽  
Sensor Reading ◽  
Dynamic Constraints ◽  
Planning Methods ◽  
Global Optimal ◽  
Window Approach ◽  
Local Map

This manuscript proposes a novel tangent-graph-based method that provides all distinctive topology paths, as a set of trajectories that can not be transformed into each other by gradual bending and stretching without colliding with obstacles. As the global optimal trajectory (limited by dynamic constraints) may not correspond to the shortest path (almost without dynamic constraints), it is important to provide all distinctive topology paths rather than just the globally shortest one. One of the application is provide initial paths for trajectory planning methods, such as time elastic band (TEB) and dynamic window approach (DWA). Considering that a mobile platform is always working in a dynamic changing environment and that update the whole graph is time consuming, a dynamic rectify method is proposed to update tangents according to local map provided by sensor reading. Finally, results under real grid maps are presented.

Motion Planning and Control with Randomized Payloads on Real Robot Using Deep Reinforcement Learning

2019 ◽  
Vol 13 (04) ◽  
pp. 541-563
Author(s):  
Ali Demir ◽  
Volkan Sezer
Keyword(s):  
Motion Planning ◽  
Dynamic Environment ◽  
Test Time ◽  
Continuous Control ◽  
Success Rates ◽  
Dynamic Constraints ◽  
Planning And Control ◽  
Comparison Results ◽  
Real Robot ◽  
Torque Values

In this study, a unified motion planner with low level controller for continuous control of a differential drive mobile robot under variable payload values is presented. The deep reinforcement agent takes 11-dimensional state vector as input and calculates each wheel’s torque value as a 2-dimensional output vector. These torque values are fed into the dynamic model of the robot, and lastly steering commands are gathered. In previous studies, intersection navigation solutions that uses deep-RL methods, have not been considered with variable payloads. This study is focused specifically on service robotic applications where payload is subject to change. In this study, deep-RL-based motion planning is performed by considering both kinematic and dynamic constraints. According to the simulations in a dynamic environment, the agent successfully navigates to target with 98.2% success rate in test time with unseen payload masses during training. Another agent is also trained without payload randomization for comparison. Results show that our agent outperforms the other agent, that is not aware of its own payload, with more than 40% gap. Our agent is also compared with the Time-to-Collision (TTC) algorithm. It is observed that our agent uses far less time than TTC to accomplish the mission while success rates of two methods are same. Lastly, the proposed method is applied on a real robot in order to show the real-time applicability of the approach.

Leader–follower-based dynamic trajectory planning for multirobot formation

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1351-1359 ◽  
Author(s):  
Shuang Liu ◽  
Dong Sun
Keyword(s):  
Analytical Solution ◽  
Objective Function ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Dynamic Environment ◽  
Current Paper ◽  
New Approach ◽  
Dynamic Trajectory

SUMMARYThe present paper presents a new approach to a leader–follower-based dynamic trajectory planning for multirobot formation. A near-optimal trajectory is generated for each robot in a decentralized manner. The main contributions of the current paper are the proposal of a new objective function that considers both collision avoidance and formation requirement for the trajectory generation, and an analytical solution of trajectory parameters in the trajectory optimization. Simulations and experiments on multirobots are performed to demonstrate the effectiveness of the proposed approach to the multirobot formation in a dynamic environment.

Navigation of a mobile robot by locally optimal trajectories

Robotica ◽  
1999 ◽  
Vol 17 (5) ◽  
pp. 553-562 ◽  
Author(s):  
Kokou Djath ◽  
Ali Siadet ◽  
Michel Dufaut ◽  
Didier Wolf
Keyword(s):  
Mobile Robot ◽  
Shortest Path ◽  
Laser Scanner ◽  
Navigation Algorithm ◽  
Map Construction ◽  
Unstructured Environment ◽  
Environment Perception ◽  
Simple Equations ◽  
Holonomic Mobile Robot ◽  
Local Map

This paper proposes a navigation system for a non-holonomic mobile robot. The navigation is based on a “look and move” approach. The aim is to define intermediate points called sub-goals through which the robot must pass. This algorithm is particularly suitable for navigation in an unknown environment and obstacle avoidance. Between two successive sub-goals, a shortest path planning solution is adopted. We have adopted the “Dubins' car” because of the environment perception sensor, a 180° laser scanner. In order to minimize the calculation time, the theoretical results of shortest path are approximated by simple equations. The navigation algorithm proposed can be used either in a structured or unstructured environment. In this context the local map construction is based on the segmentation of a structured environment; so for an unstructured environment, a suitable algorithm must be used instead.

scholarly journals A Study on the Effect of Parameters for ROS Motion Planer and Navigation System for Indoor Robot

2021 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Chen Zheng Looi ◽  
Danny Wee Kiat Ng
Keyword(s):  
Operating System ◽  
Service Robot ◽  
Dijkstra Algorithm ◽  
Elastic Band ◽  
Office Environment ◽  
Robot Operating System ◽  
The Past ◽  
Navigation Algorithms ◽  
The Common ◽  
Window Approach

In the past decades, the service robot industry had risen rapidly. The office assistant robot is one type of service robot used to assist officers in an office environment. For the robot to navigate autonomously in the office, navigation algorithms and motion planners were implemented on these robots. Robot Operating System (ROS) is one of the common platforms to develop these robots. The parameters applied to the motion planners will affect the performance of the Robot. In this study, the global planners, A* and Dijkstra algorithm and local planners, Dynamic Window Approach (DWA) and Time Elastic Band (TEB) algorithms were implemented and tested on a robot in simulation and a real environment. Results from the experiments were used to evaluate and compare the performance of the robot with different planners and parameters. Based on the results obtained, the global planners, A* and Dijkstra algorithm both can achieve the required performance for this application whereas TEB outperforms DWA as the local planner due to its feasibility in avoiding dynamic obstacles in the experiments conducted.

AN INTEGERATED NAVIGATION SYSTEM FOR AUTONOMOUS MOBILE ROBOT IN DYNAMIC ENVIROMENTS

2020 ◽  
pp. 32-46
Author(s):  
Nguyen Lan Anh
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Navigation System ◽  
Tracking System ◽  
Dynamic Environment ◽  
Planning System ◽  
Autonomous Mobile Robot ◽  
Integrated Navigation ◽  
Elastic Band ◽  
Integrated Navigation System

To enable an autonomous mobile robot to navigate safely in a dy- namic environment, the mobile robot must address four typical functional blocks of the navigation system including perception, localization, motion planning, and motor control. In this study, we present an integrated navigation system for the autonomous mobile robot in the dynamic environment by incorporating the techniques proposed in our previous studies, including object detection and tracking system, localization system and motion planning system, into a completed navigation system. In addition, we propose an extended timed elastic band (ETEB) technique for online trajectory planning, which allows the mobile robot to proactively avoid obstacles in the surrounding environment. We validate the effectiveness of the proposed model through a series of experiments in both simulated and real-world environments. The experimental results show that our proposed motion model is capable of driving the mobile robots to proactively avoid dynamic obstacles, providing safe navigation for the robots.

scholarly journals Routing Optimization of Intelligent Vehicle in Automated Warehouse

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yan-cong Zhou ◽  
Yong-feng Dong ◽  
Hong-mei Xia ◽  
Jun-hua Gu
Keyword(s):  
Ant Colony Algorithm ◽  
Dynamic Environment ◽  
Intelligent Vehicle ◽  
Optimal Route ◽  
Routing Optimization ◽  
Global Dynamic ◽  
Environment Map ◽  
Adjustment Strategy ◽  
Global Optimal ◽  
Vehicle Routing Optimization

Routing optimization is a key technology in the intelligent warehouse logistics. In order to get an optimal route for warehouse intelligent vehicle, routing optimization in complex global dynamic environment is studied. A new evolutionary ant colony algorithm based on RFID and knowledge-refinement is proposed. The new algorithm gets environmental information timely through the RFID technology and updates the environment map at the same time. It adopts elite ant kept, fallback, and pheromones limitation adjustment strategy. The current optimal route in population space is optimized based on experiential knowledge. The experimental results show that the new algorithm has higher convergence speed and can jump out the U-type or V-type obstacle traps easily. It can also find the global optimal route or approximate optimal one with higher probability in the complex dynamic environment. The new algorithm is proved feasible and effective by simulation results.

ACO-Based Global Optimal Transportation Path

2012 ◽  
Vol 488-489 ◽  
pp. 1680-1683
Author(s):  
Wei Hua Zhu ◽  
Ying Shen
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Shortest Path ◽  
Real Life ◽  
Optimal Transportation ◽  
Time Analysis ◽  
The Real ◽  
Real Time Analysis ◽  
Global Optimal ◽  
Simulation Results

This paper discusses how to address some issues when contemplating the global optimal transportation path (GOTP) such as dynamics, the ability of real-time analysis as well as complexity of prediction. Using shortest path methodology, this paper abstracts the real-life problem to a graphic context. Based on the solution of ant colony optimization (ACO) algorithm, the simulation indicates that this manner is efficient and effective in dealing with these problems. The indicators utilized ACO are achieved through simulation results analysis, providing the range of exact elements.

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