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In this paper,we design a formation control systrm for multi-unmanned ground vehicles(UGV) from the prospective of path planning and path tracking.The master-slave control is adopted by electing out a main vehicle to address the problem of possible accumulation,tranmission and amplification of errors.In the process of formation transformation,we first generate an expected path by combing the methods of dynamic window and potential energy field.Then a path tracking algorithm based on Hermite curve is adopted to make the formation transformation process more stable and accurate.Finally,the evaluation system of the formation control system is constructed,which combines the expected position,the actual position,the expected speed, the actual speed and the actual acceleration,giving an evalutaion on the performance of the formation transformation,response of the formation driving process and the performance of the formation stability.
Autonomous mobile robots have the potential to solve missions that are either too complex or dangerous to be accomplished by humans. In this paper, we address the design and autonomous deployment of a ground vehicle equipped with a robotic arm for ur
There are many situations for which an unmanned ground vehicle has to work with only partial observability of the environment. Therefore, a feasible nonholonomic obstacle avoidance and target tracking action must be generated immediately based on the
This paper presents a novel data-driven navigation system to navigate an Unmanned Vehicle (UV) in GPS-denied, feature-deficient environments such as tunnels, or mines. The method utilizes Radio Frequency Identification (RFID) tags, also referred to a
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precisio
We propose a high-level planner for a multirotor to chase a ground vehicle, while simultaneously respecting various state and input constraints. Assuming a minimal kinematic model for the ground vehicle, we use data collected online to generate predi