Algorithms for Sensor-based Robotics : Sampling-based Path PlanningPlaku, Erion
Introduction to Mobile Robotics: Iterative Closest Point Algorithm FastSLAM 1.0 This is a feature based SLAM example using FastSLAM 1.0. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. The red points are particles of Fast...
state-of-the-art in the field of swarm robotics utilizing reinforcement learning. Additionally, we review the various simulators used to train, validate and simulate swarms ofunmanned vehicles. We finalize our review by discussing our findings and the possible directions for future research. Overall,...
algorithm for finding multiple noisy radiation sources with spatial and communication constraints with an emulated environment. The algorithm tries to detect the source(s) of radiation with some robots in the monitoring fields. Each robot has a sensor mounted to detect the radiation concentration. T...
The technology paves the way for micro-localization applications in a wide range of domains: from secure (keyless) access and AR/VR gaming to asset tracking and robotics. Ultra wideband technology is perfectly suited to support a variety of high accuracy and secure wireless ranging use-cases. ...
Smart Sensor networks (SSNs) have surfaced as one of the most promising technologies of the Twenty First century by revolutionising the way in which the dynamic environments are monitored. The availability of economical micro sensors and the knowledge of
Sensor Networks Federated Edge Learning Cross-Layer Optimization with AI Programmable Service Interfaces Wireless Communication Technologies Underwater and Underground Networks Artificial Intelligence and Machine Learning Information Processing and Data Management Algorithms, Systems, and Applications of Internet of ...
Introduction to Mobile Robotics: Iterative Closest Point Algorithm FastSLAM 1.0 This is a feature based SLAM example using FastSLAM 1.0. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. The red points are particles of Fast...
Introduction to Mobile Robotics: Iterative Closest Point Algorithm FastSLAM 1.0 This is a feature based SLAM example using FastSLAM 1.0. The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. The red points are particles of Fast...
The development and testing of algorithms for robotics applications typically requires evaluations in both simulated and physical environments. Some algorithms, however, can be difficult to deploy in simple hardware experiments, ...