This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.
- Dynamic Systems and Control Division
Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance
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Shaw, RP, & Bevly, DM. "Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Atlanta, Georgia, USA. September 30–October 3, 2018. V003T37A004. ASME. https://doi.org/10.1115/DSCC2018-9080
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