Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

Tanveer, M. Hassan; Recchiuto, Carmine Tommaso; Sgorbissa, Antonio

doi:10.1017/s0263574718000875

Cited by 20 publications

(12 citation statements)

References 66 publications

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“…The simulated forest contains five trees consisting of two species, which is the same scene shown in Fig 5(d) . The flying route is produced by using the path-following algorithm of [ 52 ]. On the flying route, a total of 12 sampling points were taken.…”

Section: Resultsmentioning

confidence: 99%

A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles

Tanveer

Thomas

et al. 2020

PLoS ONE

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We introduce a unified simulation framework that generates natural sensing environments and produces biosonar echoes under various sensing scenarios. This framework produces rich sensory data with environmental information completely known, thus can be used for the training of robotic algorithms for biosonar-based Unmanned Aerial Vehicles. The simulated environment consists of random trees with full geometry of the tree foliage. To simulate a single tree, we adopt the Lindenmayer system to generate the initial branching pattern and integrate that with the available measurements of the 3D computer-aided design object files to create natural-looking branches, sub-branches, and leaves. A forest is formed by simulating trees at random locations generated by using an inhomogeneous Poisson process. While our simulated environments can be generally used for testing other sensors and training robotic algorithms, in this study we focus on testing bat-inspired Unmanned Aerial Vehicles that recreate bat’s flying behavior through biosonar sensors. To this end, we also introduce an foliage echo simulator that produces biosonar echoes while mimicking bat’s biosonar system. We demonstrate the application of the proposed simulation framework by generating real-world scenarios with multiple trees and computing the resulting impulse responses under static or dynamic motions of an Unmanned Aerial Vehicle.

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Section: Resultsmentioning

confidence: 99%

A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles

Tanveer

Thomas

et al. 2020

PLoS ONE

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“…A low contact compact foot trajectory planning method using high order polynomial curve was used to carry out the foot trajectory planning of the robot for foot-end trajectory planning, and the equation of the foot trajectory was deduced by a polynomial. From Equations (9), (11), and (13), the foot-end trajectory has six constraints in the walking direction (X direction), and the foot-end trajectory in the walking direction x is a quintic polynomial:…”

Section: Foot-end Trajectory Planning Of Swing Phasementioning

confidence: 99%

“…Substituting Equations (9), (11), and (13) into Equation (15), the equation of the foot-end trajectory curve of the quadruped robot can be calculated as follows:…”

Section: Foot-end Trajectory Planning Of Swing Phasementioning

confidence: 99%

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Design of bionic goat quadruped robot mechanism and walking gait planning

Zhang¹,

Chen²,

Guo³

et al. 2020

International Journal of Agricultural and Biological Engineering

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In order to improve the walking stability and obstacle jumping ability of the robot on the slope, the goat was taken as the bionic prototype, a kind of bionic goat quadruped robot has been designed. By the bionic principle, the mechanical structure of the quadruped robot was designed, and the Denavit-Hartenberg (D-H) method was used to build the kinematic model, through which the forward and inverse kinematics of the robot was calculated, thus the equations of velocity and acceleration of the joint angle change were obtained during the quadruped robot motion, providing control theory foundation for robots. During walk gait planning, a low contact compact foot trajectory planning method using a high order polynomial curve was used to carry out the foot trajectory planning of the robot for the swing phase and stance phase. Through MATLAB software simulation, the simulation results of MATLAB software showed that the trajectory of the foot of the quadruped robot was semi-elliptic in a gait cycle, which were basically consistent with the foot trajectory obtained from the previous goat slope walking test. Therefore, the simulation results by MATLAB software showed that the foot trajectory was reasonable. Based on this, a gait plan was carried out, and the sequence of the lift leg steps of the robot was obtained. The test platform for the whole machine was built. The results showed that the height of the leg of the quadruped robot was 7.37 cm, and the length of the gait was 28.40 cm. Compared with the planned average gait length S=30 cm and average step height H=7 cm, the error of the leg height and the gait length was 5.28% and 5.33%, respectively. The designed gait planning achieved the expected results. The knee joint angle varied from 126.3° to 156.1°, and the hip joint angle varied from 103.9° to 138.4°. The changes in the joint angle of the quadruped robot could prove the correctness of the foot motion trajectory planning, which will provide a theoretical basis for the structural design and gait planning of the quadruped robot.

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“…Mujahed et al 7 proposed the concept of admissible gap (AG) methodology for reaction-based collision avoidance. 8 This AG methodology gave direct shapes and kinematics to the robot to target the goal. Gualda et al 9 proposed a simultaneous calibration and navigation algorithm using multiple ultrasonic local positioning systems.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

Pandey

Parhi

2019

Robotica

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SUMMARY Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.

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Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

Cited by 20 publications

References 66 publications

A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles

A simulation framework for bio-inspired sonar sensing with Unmanned Aerial Vehicles

Design of bionic goat quadruped robot mechanism and walking gait planning

Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

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