18,220 research outputs found
A sEMG-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots
We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collision avoidance guidance. The motion of the mobile robot is a resultant of compliant motion control and obstacle avoidance. In the mode of compliant motion, the sEMG signals obtained from the operator's forearms are transformed into human commands to control the moving direction and linear velocity of the mobile robot, respectively. When the mobile robot is blocked by obstacles, the motion mode is converted into obstacle avoidance. Aimed at the obstacle avoidance problem without a specific target, we develop a no-target Bug (NT-Bug) algorithm to guide the mobile robot to avoid obstacles and return to the command line. Besides, the command moving direction given by the operator is taken into consideration in the obstacle avoidance process to plan a smoother and safer path for the mobile robot. A model predictive controller is exploited to minimize the tracking errors. Experiments have been implemented to demonstrate the effectiveness of the proposed shared control strategy and the NT-Bug algorithm
Sistem Navigasi pada Mobile Robot dengan Global Positioning System (GPS)
This paper present the use of Global Positioning Systems (GPS) as navigational system for a ground based mobile robot. The proposed mobile robot contains a GPS system for navigation and sensors utrasonic for obstacle avoidance system. The Mobile robot navigates to the waypoint specified by the user through communication system xbee and avoids the obstacles in its way to destination. Mobile Robot can navigate through desired waypoint and at the same time apply the obstacle avoidance rule
A comprehensive obstacle avoidance system of mobile robots using an adaptive threshold clustering and the morphin algorithm
To solve the problem of obstacle avoidance for a mobile robot in unknown environment, a comprehensive obstacle avoidance system (called ATCM system) is developed. It integrates obstacle detection, obstacle classification, collision prediction and obstacle avoidance. Especially, an Adaptive-Threshold Clustering algorithm is developed to detect obstacles, and the Morphin algorithm is applied for path planning when the robot predicts a collision ahead. A dynamic circular window is set to continuously scan the surrounding environment of the robot during the task period. The simulation results show that the obstacle avoidance system enables robot to avoid any static and dynamic obstacles effectively
Wavefront Propagation and Fuzzy Based Autonomous Navigation
Path planning and obstacle avoidance are the two major issues in any
navigation system. Wavefront propagation algorithm, as a good path planner, can
be used to determine an optimal path. Obstacle avoidance can be achieved using
possibility theory. Combining these two functions enable a robot to
autonomously navigate to its destination. This paper presents the approach and
results in implementing an autonomous navigation system for an indoor mobile
robot. The system developed is based on a laser sensor used to retrieve data to
update a two dimensional world model of therobot environment. Waypoints in the
path are incorporated into the obstacle avoidance. Features such as ageing of
objects and smooth motion planning are implemented to enhance efficiency and
also to cater for dynamic environments
Implementation of Robot Operating System in Beaglebone Black based Mobile Robot for Obstacle Avoidance Application
The Robot Operating System (ROS) is a collection of tools, libraries, and conventions that focus on simplifying the task of creating a complex and advanced robotics system. Its standard framework can be shared with another robotics system that has a similar platform and suitable for being introduced as an educational tool in robotics. However, the problems found out in the current robot platform available in the market are expensive and encapsulated. The development of an open source robot platform is encouraged. Therefore, this research is carried out to design and develop an ROS based obstacle avoidance system for existing differential-wheeled mobile robot. The ROS was installed under Ubuntu 14.04 on a Beaglebone Black embedded computer system. Then, the ROS was implemented together with the obstacle avoidance system to establish the communication between program nodes. The mobile robot was then designed and developed to examine the obstacle avoidance application. The debugging process was carried out to check the obstacle avoidance system application based on the communication between nodes. This process is important in examining the message publishing and subscribing from all nodes. The obstacle avoidance mobile robot has been successfully tested where the communication between nodes was running without any problem
Trajectory tracking of a differentially driven wheeled mobile robot in the presence of obstacles
A trajectory following and obstacle avoidance mechanism for a mobile robot is presented for situations where the robot has to follow a specific target trajectory but the task might not be completely possible due to obstacles in the way, which the robot must avoid. After avoiding an obstacle, the robot should catch up with the target trajectory. In the proposed system, this objective is reached by combining a nonlinear control method with an Artificial Potential Function method, leading to trajectory tracking control with obstacle avoidance capabilities.peer-reviewe
Danger-aware Adaptive Composition of DRL Agents for Self-navigation
Self-navigation, referred as the capability of automatically reaching the
goal while avoiding collisions with obstacles, is a fundamental skill required
for mobile robots. Recently, deep reinforcement learning (DRL) has shown great
potential in the development of robot navigation algorithms. However, it is
still difficult to train the robot to learn goal-reaching and
obstacle-avoidance skills simultaneously. On the other hand, although many
DRL-based obstacle-avoidance algorithms are proposed, few of them are reused
for more complex navigation tasks. In this paper, a novel danger-aware adaptive
composition (DAAC) framework is proposed to combine two individually
DRL-trained agents, obstacle-avoidance and goal-reaching, to construct a
navigation agent without any redesigning and retraining. The key to this
adaptive composition approach is that the value function outputted by the
obstacle-avoidance agent serves as an indicator for evaluating the risk level
of the current situation, which in turn determines the contribution of these
two agents for the next move. Simulation and real-world testing results show
that the composed Navigation network can control the robot to accomplish
difficult navigation tasks, e.g., reaching a series of successive goals in an
unknown and complex environment safely and quickly.Comment: 7 pages, 9 figure
Neural Controller for a Mobile Robot in a Nonstationary Enviornment
Recently it has been introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a differential-drive robot through an unsupervised learning-by-doing cycle. This article introduces an obstacle avoidance module that is integrated into the neural controller. This module makes use of sensory information to determine at each instant a desired angle and distance that causes the robot to navigate around obstacles on the way to a final target. Obstacle avoidance is performed in a reactive manner by representing the objects and target in the robot's environment as Gaussian functions. However, the influence of the Gaussians is modulated dynamically on the basis of the robot's behavior in a way that avoids problems with local minima. The proposed module enables the robot to operate successfully with different obstacle configurations, such as corridors, mazes, doors and even concave obstacles.Air Force Office of Scientific Research (F49620-92-J-0499
Optimal path planning algorithm for swarm robots using bat algorithm with mutation (bam)
In robot navigation, path planning is always the most crucial problem where robots should be able to move from starting position to goal position without colliding into any obstacle. This is because robot is unable to plan an optimum path in a known situation and obstacles available increases the difficulty for robot to move according to the planned path in an environment. The current research in robot navigation is to implement an obstacle avoidance algorithm to a single mobile robot to realize the path planning of a mobile robot. However, there is still room for improvement such as implementing the obstacle avoidance algorithm into swarm robot. The objective of this study is to propose Bat Algorithm with Mutation (BAM) for solving the problem of obstacle avoidance of mobile robots. This project is completed by creating a wheeled mobile robot where the robot uses a P controller. Next, robot is trained to travel from one point to another point and inserted into a virtual environment with static obstacle. The obstacle avoidance algorithm is then implemented to the robot. Lastly, it can be seen that the robot is able to move in the planned path without colliding with the obstacle in the environment
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