Biologically inspired UAV obstacle avoidance and control using monocular optical flow & divergence templates

2011 ◽  
Author(s):  
John Stowers ◽  
Michael Hayes ◽  
Andrew Bainbridge-Smith
Keyword(s):  
Optical Flow ◽  
Obstacle Avoidance ◽  
Biologically Inspired ◽  
And Control

Control and Research of Bionic Robotic Fish Based on Arduino

2013 ◽  
Vol 706-708 ◽  
pp. 691-694
Author(s):  
Tian Lin Song ◽  
Ya Ping Lu ◽  
Hai Qing Liu
Keyword(s):  
Control System ◽  
Obstacle Avoidance ◽  
Color Discrimination ◽  
Robotic Fish ◽  
And Control

It adopts the Arduino controller and control system of the extension circuit to complete all kinds of motions for the more joints’ bionic robotic fish of the different structures. Adding different kinds of sensors, it can also achieve many functions, such as obstacle avoidance, trace, color discrimination, etc.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

scholarly journals A Triboelectric-Based Artificial Whisker for Reactive Obstacle Avoidance and Local Mapping

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Xu ◽  
Xingyu Wang ◽  
Siyuan Wang ◽  
Tianyu Chen ◽  
Jianhua Liu ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Robots ◽  
Tactile Sensor ◽  
Triboelectric Nanogenerator ◽  
Tactile Sensors ◽  
Biologically Inspired ◽  
External Stimulation ◽  
Autonomous Surface Vehicles ◽  
Electrostatic Induction ◽  
Contact Position

Since designing efficient tactile sensors for autonomous robots is still a challenge, this paper proposes a perceptual system based on a bioinspired triboelectric whisker sensor (TWS) that is aimed at reactive obstacle avoidance and local mapping in unknown environments. The proposed TWS is based on a triboelectric nanogenerator (TENG) and mimics the structure of rat whisker follicles. It operates to generate an output voltage via triboelectrification and electrostatic induction between the PTFE pellet and copper films (0.3 mm thickness), where a forced whisker shaft displaces a PTFE pellet (10 mm diameter). With the help of a biologically inspired structural design, the artificial whisker sensor can sense the contact position and approximate the external stimulation area, particularly in a dark environment. To highlight this sensor’s applicability and scalability, we demonstrate different functions, such as controlling LED lights, reactive obstacle avoidance, and local mapping of autonomous surface vehicles. The results show that the proposed TWS can be used as a tactile sensor for reactive obstacle avoidance and local mapping in robotics.

Illustration of Centralized Command and Control for Flocking Behavior

2014 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Sami Oweis ◽  
Subramaniam Ganesan ◽  
Ka C Cheok
Keyword(s):  
Obstacle Avoidance ◽  
Command And Control ◽  
Flow Chart ◽  
Controlled Systems ◽  
Swarm Robots ◽  
Group A ◽  
Swarming Behavior ◽  
And Control ◽  
Flocking Behavior ◽  
Ground Systems

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

The Basis for the Perception and Control of Altitude: Splay & Depression Angle Components of Optical Flow

1994 ◽  
Vol 38 (19) ◽  
pp. 1275-1279 ◽  
Author(s):  
Sheila A. Garness ◽  
John M. Flach ◽  
Terry Stanard ◽  
Rik Warren
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Optic Flow ◽  
Superior Performance ◽  
Rate Condition ◽  
Depression Angle ◽  
Dependent Variables ◽  
Texture Type ◽  
Constant Altitude ◽  
And Control

This study evaluated subjects ability to track a constant altitude as a function of the structure in the optical flow field. Optic flow was manipulated by using four different types of ground texture (splay angle, depression angle, random dot, and block textures) crossed with two global optical flow (GOF) rates (0 and 3 eyeheights/s). The subjects were asked to maintain a constant altitude while wind disturbances randomly perturbed them on vertical, lateral, and fore-aft axes. The critical independent variables were texture type and GOF rate. Texture type was a within-subjects variable while GOF rate was a between-subjects variable. The main dependent variables included RMS height error and the correlation between subjects stick activity and the three wind disturbances. For both dependent variables, an interaction was found in that the depression angle texture provided superior performance in a hover or 0 GOF rate condition. The splay angle texture provided a constant level of performance for both GOF rates, being superior to depression angle in the higher GOF rate. These results are consistent with Flach et al.'s (1992) hypothesis that the ability to pick-up altitude information from the optic flow field depends upon the amount of optical activity that is specific to changes in altitude (signal) rather than specific to changes in lateral or fore-aft position (noise). This hypothesis provides a higher order explanation for previous results on the control of altitude which had been thought to be inconsistent.

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