The estimation of the pose of a differential drive mobile robot from noisy odometer, compass and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment. https://doi.org/10.4018/IJAIML.2020010101 https://www.linkedin.com/in/john-bolte-0856134/
The increase in the number and complexity of crime activities in our nation together with shortage in human resources in the safety and security domain is putting extra pressure on emergency responders. The emergency responders are constantly confronted with sophisticated situations that urgently require professional, safe, and rapid handling to contain and conclude the situation to minimize the danger to public and the emergency responders. Recently, Dutch emergency responders have started to experiment with various types of robots to improve the responsiveness and the effectiveness of their responses. One of these robots is the Boston Dynamic’s Spot Robot Dog, which is primarily appealing for its ability to move in difficult terrains. The deployment of the robot in real emergencies is at its infancy. The main challenge that the robot dog operators are facing is the high workload. It requires the full attention to operate the robot itself. As such, the professional acts entirely as a robot operator rather than a domain expert that critically examines and addresses the main safety problems at hand. Therefore, there is an urgent request from these emergency response professionals to develop and integrate key technologies that enable the robot dog to operate more autonomously. In this project, we explore on how to increase the autonomy level of the robot dog in order to reduce the workload of the operator, and eventually help the operator remain domain expert. Therefore, we will explore the ability of the robot to autonomously 3D-map unknown confined areas. The results of this project will lead to new practical knowledge and a follow-up project that will focus on further developing the technologies that increase the autonomy of the robot for eventual deployment in operational environments. This project will also have direct contribution to education through involvement of students and lecturers.
