scholarly journals Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots

2020 ◽  
Vol 39 (12) ◽  
pp. 1419-1469
Author(s):  
Shreyas Kousik ◽  
Sean Vaskov ◽  
Fan Bu ◽  
Matthew Johnson-Roberson ◽  
Ram Vasudevan
Keyword(s):  
Real Time ◽  
Autonomous Robots ◽  
Tracking Error ◽  
Random Trees ◽  
Model Complexity ◽  
Trajectory Design ◽  
Receding Horizon ◽  
Decomposition Approach ◽  
System Decomposition ◽  
Hardware Platforms

To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the next plan. However, creating safe, dynamically feasible trajectories in real time is challenging, and planners must ensure persistent feasibility, meaning a new trajectory is always available before the previous one has finished executing. Existing approaches make a tradeoff between model complexity and planning speed, which can require sacrificing guarantees of safety and dynamic feasibility. This work presents the Reachability-based Trajectory Design (RTD) method for trajectory planning. RTD begins with an offline forward reachable set (FRS) computation of a robot’s motion when tracking parameterized trajectories; the FRS provably bounds tracking error. At runtime, the FRS is used to map obstacles to parameterized trajectories, allowing RTD to select a safe trajectory at every planning iteration. RTD prescribes an obstacle representation to ensure that obstacle constraints can be created and evaluated in real time while maintaining safety. Persistent feasibility is achieved by prescribing a minimum sensor horizon and a minimum duration for the planned trajectories. A system decomposition approach is used to improve the tractability of computing the FRS, allowing RTD to create more complex plans at runtime. RTD is compared in simulation with rapidly-exploring random trees and nonlinear model-predictive control. RTD is also demonstrated in randomly crafted environments on two hardware platforms: a differential-drive Segway and a car-like Rover. The proposed method is safe and persistently feasible across thousands of simulations and dozens of real-world hardware demos.

Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

2019 ◽  
Author(s):  
Shreyas Kousik ◽  
Patrick Holmes ◽  
Ram Vasudevan
Keyword(s):  
Tracking Error ◽  
Search And Rescue ◽  
The Other ◽  
Trajectory Design ◽  
Limited Information ◽  
Receding Horizon ◽  
Cluttered Environments ◽  
Online Planning ◽  
Infrastructure Inspection ◽  
Horizon Planning

Abstract Quadrotors can provide services such as infrastructure inspection and search-and-rescue, which require operating autonomously in cluttered environments. Autonomy is typically achieved with receding-horizon planning, where a short plan is executed while a new one is computed, because sensors receive limited information at any time. To ensure safety and prevent robot loss, plans must be verified as collision free despite uncertainty (e.g, tracking error). Existing spline-based planners dilate obstacles uniformly to compensate for uncertainty, which can be conservative. On the other hand, reachability-based planners can include trajectory-dependent uncertainty as a function of the planned trajectory. This work applies Reachability-based Trajectory Design (RTD) to plan quadrotor trajectories that are safe despite trajectory-dependent tracking error. This is achieved by using zonotopes in a novel way for online planning. Simulations show aggressive flight up to 5 m/s with zero crashes in 500 cluttered, randomized environments.

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

scholarly journals Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

2021 ◽  
pp. 107754632110191
Author(s):  
Farzam Tajdari ◽  
Naeim Ebrahimi Toulkani
Keyword(s):  
Real Time ◽  
Tracking Error ◽  
Parallel Robot ◽  
Torque Control ◽  
Test Bed ◽  
Linear Quadratic ◽  
Control Effort ◽  
The Real ◽  
Unknown Disturbances ◽  
Quadratic Integral

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

Real time terrain identification of autonomous robots using machine learning

2020 ◽  
Vol 4 (3) ◽  
pp. 265-277
Author(s):  
M. G. Harinarayanan Nampoothiri ◽  
P. S. Godwin Anand ◽  
Rahul Antony
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Autonomous Robots

Two Mixed Logical Dynamical Real-Time Receding Horizon Control Schemes for Microgrids Operation Optimization

2021 ◽  
Author(s):  
Seyed Shahab Kheradmand ◽  
Reyhaneh Haghpanah ◽  
Mojtaba Barkhordary Yazdi ◽  
Malihe Maghfouri Farsangi
Keyword(s):  
Real Time ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Operation Optimization ◽  
Control Schemes ◽  
Mixed Logical Dynamical

Synergistic real-time compensation of tracking and contouring errors for precise parametric curved contour following systems

2017 ◽  
Vol 232 (19) ◽  
pp. 3367-3383 ◽  
Author(s):  
De-Ning Song ◽  
Jian-Wei Ma ◽  
Zhen-Yuan Jia ◽  
Feng-Ze Qin ◽  
Xiao-Xuan Zhao
Keyword(s):  
Real Time ◽  
High Precision ◽  
Tracking Error ◽  
Estimation Algorithm ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
System Structure ◽  
Parametric Curve ◽  
Contouring Error ◽  
Contour Following

The tracking and contouring errors are inevitable in real computer numerical control contour following because of the reasons such as servo delay and dynamics mismatch. In order to improve the motion accuracy, this paper proposes a synergistic real-time compensation method of tracking and contouring errors for precise parametric curve following of the computer numerical control systems. The tracking error for each individual axis is first compensated, by using the feed-drive models with the consideration of model uncertainties, to enhance the tracking performances of all axes. Further, the contouring error is estimated and compensated to improve the contour accuracy directly, where a high-precision contouring-error estimation algorithm, based on spatial circular approximation of the desired contour neighboring the actual motion position, is presented. Considering that the system structure is coupled after compensation, the stability of the coupled system is analyzed for design of the synergistic compensator. Innovative contributions of this study are that not only the contouring-error can be estimated with a high precision in real time, but also the tracking and contouring performances can be simultaneously improved although there exist modeling errors and disturbances. Simulation and experimental tests demonstrate the effectiveness and advantages of the proposed method.

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

scholarly journals Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Indoor Environments ◽  
Safe Distance ◽  
Field Robots ◽  
Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

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