94 research outputs found
Voronoi-based trajectory optimization for UGV path planning
© 2017 IEEE. Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of key parameters for path optimization and the algorithm implementation in MATLAB. The developed algorithm is suitable for fast and robust trajectory tuning to a dynamically changing environment and is capable to provide efficient planning for mobile robots
Assistive Planning in Complex, Dynamic Environments: a Probabilistic Approach
We explore the probabilistic foundations of shared control in complex dynamic
environments. In order to do this, we formulate shared control as a random
process and describe the joint distribution that governs its behavior. For
tractability, we model the relationships between the operator, autonomy, and
crowd as an undirected graphical model. Further, we introduce an interaction
function between the operator and the robot, that we call "agreeability"; in
combination with the methods developed in~\cite{trautman-ijrr-2015}, we extend
a cooperative collision avoidance autonomy to shared control. We therefore
quantify the notion of simultaneously optimizing over agreeability (between the
operator and autonomy), and safety and efficiency in crowded environments. We
show that for a particular form of interaction function between the autonomy
and the operator, linear blending is recovered exactly. Additionally, to
recover linear blending, unimodal restrictions must be placed on the models
describing the operator and the autonomy. In turn, these restrictions raise
questions about the flexibility and applicability of the linear blending
framework. Additionally, we present an extension of linear blending called
"operator biased linear trajectory blending" (which formalizes some recent
approaches in linear blending such as~\cite{dragan-ijrr-2013}) and show that
not only is this also a restrictive special case of our probabilistic approach,
but more importantly, is statistically unsound, and thus, mathematically,
unsuitable for implementation. Instead, we suggest a statistically principled
approach that guarantees data is used in a consistent manner, and show how this
alternative approach converges to the full probabilistic framework. We conclude
by proving that, in general, linear blending is suboptimal with respect to the
joint metric of agreeability, safety, and efficiency
Obstacle avoidance strategy based on adaptive potential fields generated by an electronic stick
In our previous work, an obstacle avoidance algorithm, which used potential fields and a similar strategy
to that adopted by a blind person to avoid obstacles whilst walking, was proposed. The problem analyzed consists of an AGV (Autonomous Guided Vehicle) which moves within an office environment with a known floor plan and uses an ”electronic stick” made up of infrared sensors to detect unknown obstacles in its path. Initially, a global potential navigation function, defined for each room in the floor plan, incorporates information about the dimensions of the room and the position of the door which the AGV must use to leave the room. Whilst the AGV moves, this global potential navigation function is properly modified to incorporate information about any newly detected obstacle. The main interesting aspect of the proposed approach is
that the potential function adaptation involves very low computational burden allowing for the use of Ultra-fast
AGVs. Other distinctive features of the algorithm are that it is free from local minima, the obstacles can have any shape, low cost sensors can be used to detect obstacles and an appropriate balance is achieved between the use of the global and the local approaches for collision avoidance. Our present work is a refinement of this strategy that allows for an automatic real time adaptation of the algorithm’s parameters. Now, the algorithm’s functioning requires only that the minimum distance at which the AGV can approach
an obstacle (i.e. the closest it can get to any obstacle) is defined a priori. Aspects of the real implementation of the algorithm are also discussed
A Framework for Controlling Wheelchair Motion by using Gaze Information
Users with severe motor ability are unable to control their wheelchair using standard joystick and hence an alternative control input is preferred. In this paper a method on how to enable the severe impairment user to control a wheelchair via gaze information is proposed. Since when using such an input, the navigation burden for the user is significantly increased, an assistive navigation platform is also proposed to reduce the user burden. Initially, user information is inferred using a camera and a bite-like switch. Then information from the environment is obtained using combination of laser and Kinect sensors. Eventually, both information from the environment and the user is analyzed to decide the final control operation that according to the user intention and safe from collision. Experimental results demonstrate the feasibility of the proposed approach
DEVELOPMENT OF AN ARDUINO-BASED OBSTACLE AVOIDANCE ROBOTIC SYSTEM FOR AN UNMANNED VEHICLE
The use of autonomous systems in the world to perform relevant and delicate task is fast growing. However, its
application in various fields cannot be over emphasized. This paper presents an obstacle detection and avoidance system
for an unmanned Lawnmower. The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller
and a gear DC motor. The ultrasonic and infrared sensors are implemented to detect obstacles on the robot’s path by
sending signals to an interfaced microcontroller. The micro-controller redirects the robot to move in an alternate direction
by actuating the motorsin order to avoid the detected obstacle. The performance evaluation of the system indicates an
accuracy of 85% and 0.15 probability of failure respectively. In conclusion, an obstacle detection circuit was successfully
implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light
and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller
which interprets the output and makes the robot to stop
Localization and Navigation System for Indoor Mobile Robot
Visually impaired people usually find it hard to travel independently in many
public places such as airports and shopping malls due to the problems of
obstacle avoidance and guidance to the desired location. Therefore, in the
highly dynamic indoor environment, how to improve indoor navigation robot
localization and navigation accuracy so that they guide the visually impaired
well becomes a problem. One way is to use visual SLAM. However, typical visual
SLAM either assumes a static environment, which may lead to less accurate
results in dynamic environments or assumes that the targets are all dynamic and
removes all the feature points above, sacrificing computational speed to a
large extent with the available computational power. This paper seeks to
explore marginal localization and navigation systems for indoor navigation
robotics. The proposed system is designed to improve localization and
navigation accuracy in highly dynamic environments by identifying and tracking
potentially moving objects and using vector field histograms for local path
planning and obstacle avoidance. The system has been tested on a public indoor
RGB-D dataset, and the results show that the new system improves accuracy and
robustness while reducing computation time in highly dynamic indoor scenes.Comment: Accepted by the 2023 5th International Conference on Materials
Science, Machine and Energy Engineerin
Obstacle Avoidance and Proscriptive Bayesian Programming
Unexpected events and not modeled properties of the robot environment are some of
the challenges presented by situated robotics research field. Collision avoidance is a basic security
requirement and this paper proposes a probabilistic approach called Bayesian Programming, which
aims to deal with the uncertainty, imprecision and incompleteness of the information handled to
solve the obstacle avoidance problem. Some examples illustrate the process of embodying the
programmer preliminary knowledge into a Bayesian program and experimental results of these
examples implementation in an electrical vehicle are described and commented. A video illustration
of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac
A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance
This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm
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