We present a robust control scheme for skid-steered vehicles that enables high-speed path following on challenging terrains. First, a kinematic model with experimentally identified parameters is constructed to describe the terrain-dependent motion of skid-steered vehicles. Using Lyapunov theory, a nonlinear control law is defined, guaranteeing the convergence of the vehicle to the path. To allow smooth and accurate motion at higher speeds, an additional linear velocity control scheme is proposed, which takes actuator saturation, path following error, and reachable curvatures into account. The combined solution is experimentally evaluated and compared against two state-of-the-art algorithms, by using two different robots on several different terrain types, at different speeds. A Robotnik Summit XL robot is tested on three different terrain types and two different paths at speeds up to [Formula: see text] m/s. A Segway RMP 440 robot is tested on three different terrain types and two different path types at speeds up to [Formula: see text] m/s.

No abstract

Recent research deals more and more with the application of ultra high frequency (UHF) radio-frequency identification (RFID) on mobile robots. However, the sensing characteristics between the reader and the tag (i.e. detections and signal strength) are challenging to model due to the influence of environmental effects (e.g. tag density, reflection, diffraction, or absorption). In this paper, we address the problem of dynamic objects tracking with a mobile agent using the signal strength from UHF RFID tags attached to objects. Our solution estimates the positions of RFID tags under a Bayesian framework. More precisely, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. This approach is then tested on a Scitos G5 mobile robot through various experiments.978-1-4799-6934-0/14/$31.00 ©2014 IEEE

Service robots have shown an impressive potential in providing assistance and guidance in various environments, such as supermarkets, shopping malls, homes, airports, and libraries. Due to the low cost and contactless way of communication, radiofrequency identification (RFID) technology provides a solution to overcome the difficulties (e.g., occlusions) that the traditional line-of-sight sensors (e.g., cameras and laser range finders) face. In this paper, we address the applications of using passive ultrahigh frequency (UHF) RFID as a sensing technology for mobile robots to track dynamic objects. More precisely, we combine a two-stage dynamic motion model with the dual particle filter to capture the dynamic motion of the object and to quickly recover from failures in tracking. The state estimation from the particle filter is used in combination with the VFH+ (Vector Field Histogram) to guide the robot towards the target. This is then integrated into a framework, which allows the robot to search for both static and dynamic tags, follow them, and maintain the distance between them. We finally tested our approach on a SCITOS G5 service robot through various experiments.

No abstract