scholarly journals A Set-Theoretic Algorithm for Real-Time Terrain Mapping of Mobile Robots in Outdoor Environments

10.5772/57293 ◽  
2013 ◽  
Vol 10 (11) ◽  
pp. 392 ◽  
Author(s):  
Bo Zhou ◽  
Kun Qian ◽  
Xudong Ma ◽  
Xianzhong Dai
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Terrain Mapping ◽  
Outdoor Environments

Online Modelling and Tracking Control of Mobile Robots with Slippage in Outdoor Environments

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Bo ZHOU ◽  
Xianzhong DAI ◽  
Jianda HAN
Keyword(s):  
Mobile Robots ◽  
Tracking Control ◽  
Outdoor Environments

scholarly journals Monitoring Real-time Personal Locomotion Behaviors over Smart Indoor-outdoor Environments via Body-Worn Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Munkhjargal Gochoo ◽  
Sheikh Badar Ud Din Tahir ◽  
Ahmad Jalal ◽  
Kibum Kim
Keyword(s):  
Real Time ◽  
Outdoor Environments

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

Bio-Inspired, Real-Time Passive Vision for Mobile Robots

2019 ◽  
pp. 33-58
Author(s):  
Piotr Skrzypczyński ◽  
Marta Rostkowska ◽  
Marek Wa̧sik
Keyword(s):  
Mobile Robots ◽  
Real Time
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