Obstacle Avoidance of an Unmanned Ground Vehicle using a Combined Approach of Model Predictive Control and Proportional Navigation

Obstacle Avoidance of an Unmanned Ground Vehicle using a Combined Approach of Model Predictive Control and Proportional Navigation. Shaw, R. December 2018. Accepted: 2018-12-06T20:02:39Z

Paper abstract bibtex

This thesis presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). A special focus was placed on moving obstacles that interfere with the planned path of the vehicle, this is due to the fact that the majority of obstacle avoidance research has been completed on stationary objects. An obstacle avoidance algorithm was developed using an integrated system involving Proportional Navigation (Pro-Nav) and a Nonlinear Model Predictive Controller (NMPC). An obstacle avoidance variant of the ideal proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the Pro-Nav. The NMPC utilizes a lateral vehicle dynamic model along with a nonlinear tire model in order to issue control inputs. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. Another application of a Pro-Nav and NMPC algorithm was tested for leader/follower situations. The performance of the leader/follower and obstacle avoidance algorithm is evaluated through diﬀerent simulations. Simulation of the performance of the PNCAG and NMPC algorithm was conducted us ing two diﬀerent simulation environments; MATLAB and Simulink Vehicle Dynamics Block set. The MATLAB simulation validated the algorithm showing that it could be used to accomplish obstacle avoidance. With the algorithm shown to be eﬀective, it was placed into the Vehicle Dynamics Blockset. The Vehicle Dynamics Blockset provided a higher ﬁdelity vehicle model to provide a more realistic simulation environment. In addition to obstacle avoidance, simulation results veriﬁed the performance of a modiﬁed version of the PNCAG and NMPC algorithm in a leader/follower scenario. The results show, the algorithm handled the leader/follower and collision avoidance with reasonable error. Overall the algorithm was also able to follow a lead vehicle throughout a double lane change as well as avoid collision with a moving obstacle in four diﬀerent scenarios.

@unpublished{shaw_obstacle_2018,
	title = {Obstacle {Avoidance} of an {Unmanned} {Ground} {Vehicle} using a {Combined} {Approach} of {Model} {Predictive} {Control} and {Proportional} {Navigation}},
	url = {https://etd.auburn.edu//handle/10415/6538},
	abstract = {This thesis presents a new approach for the guidance and control of a UGV (Unmanned
Ground Vehicle). A special focus was placed on moving obstacles that interfere with the
planned path of the vehicle, this is due to the fact that the majority of obstacle avoidance
research has been completed on stationary objects. An obstacle avoidance algorithm was
developed using an integrated system involving Proportional Navigation (Pro-Nav) and a
Nonlinear Model Predictive Controller (NMPC). An obstacle avoidance variant of the ideal
proportional navigation law generates command lateral accelerations to avoid obstacles, while
the NMPC is used to track the reference trajectory given by the Pro-Nav. The NMPC utilizes
a lateral vehicle dynamic model along with a nonlinear tire model in order to issue control
inputs. In this application an obstacle avoidance algorithm can take over the control of a
vehicle until the obstacle is no longer a threat. Another application of a Pro-Nav and NMPC
algorithm was tested for leader/follower situations. The performance of the leader/follower
and obstacle avoidance algorithm is evaluated through diﬀerent simulations.

Simulation of the performance of the PNCAG and NMPC algorithm was conducted us
ing two diﬀerent simulation environments; MATLAB and Simulink Vehicle Dynamics Block
set. The MATLAB simulation validated the algorithm showing that it could be used to
accomplish obstacle avoidance. With the algorithm shown to be eﬀective, it was placed into
the Vehicle Dynamics Blockset. The Vehicle Dynamics Blockset provided a higher ﬁdelity
vehicle model to provide a more realistic simulation environment. In addition to obstacle
avoidance, simulation results veriﬁed the performance of a modiﬁed version of the PNCAG
and NMPC algorithm in a leader/follower scenario. The results show, the algorithm handled
the leader/follower and collision avoidance with reasonable error. Overall the algorithm was
also able to follow a lead vehicle throughout a double lane change as well as avoid collision
with a moving obstacle in four diﬀerent scenarios.},
	language = {en},
	urldate = {2024-06-25},
	author = {Shaw, Ryan},
	month = dec,
	year = {2018},
	note = {Accepted: 2018-12-06T20:02:39Z},
}

Downloads: 0

{"_id":"FDoY3NyYcHxEgddDv","bibbaseid":"shaw-obstacleavoidanceofanunmannedgroundvehicleusingacombinedapproachofmodelpredictivecontrolandproportionalnavigation-2018","author_short":["Shaw, R."],"bibdata":{"bibtype":"unpublished","type":"unpublished","title":"Obstacle Avoidance of an Unmanned Ground Vehicle using a Combined Approach of Model Predictive Control and Proportional Navigation","url":"https://etd.auburn.edu//handle/10415/6538","abstract":"This thesis presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). A special focus was placed on moving obstacles that interfere with the planned path of the vehicle, this is due to the fact that the majority of obstacle avoidance research has been completed on stationary objects. An obstacle avoidance algorithm was developed using an integrated system involving Proportional Navigation (Pro-Nav) and a Nonlinear Model Predictive Controller (NMPC). An obstacle avoidance variant of the ideal proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the Pro-Nav. The NMPC utilizes a lateral vehicle dynamic model along with a nonlinear tire model in order to issue control inputs. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. Another application of a Pro-Nav and NMPC algorithm was tested for leader/follower situations. The performance of the leader/follower and obstacle avoidance algorithm is evaluated through diﬀerent simulations. Simulation of the performance of the PNCAG and NMPC algorithm was conducted us ing two diﬀerent simulation environments; MATLAB and Simulink Vehicle Dynamics Block set. The MATLAB simulation validated the algorithm showing that it could be used to accomplish obstacle avoidance. With the algorithm shown to be eﬀective, it was placed into the Vehicle Dynamics Blockset. The Vehicle Dynamics Blockset provided a higher ﬁdelity vehicle model to provide a more realistic simulation environment. In addition to obstacle avoidance, simulation results veriﬁed the performance of a modiﬁed version of the PNCAG and NMPC algorithm in a leader/follower scenario. The results show, the algorithm handled the leader/follower and collision avoidance with reasonable error. Overall the algorithm was also able to follow a lead vehicle throughout a double lane change as well as avoid collision with a moving obstacle in four diﬀerent scenarios.","language":"en","urldate":"2024-06-25","author":[{"propositions":[],"lastnames":["Shaw"],"firstnames":["Ryan"],"suffixes":[]}],"month":"December","year":"2018","note":"Accepted: 2018-12-06T20:02:39Z","bibtex":"@unpublished{shaw_obstacle_2018,\n\ttitle = {Obstacle {Avoidance} of an {Unmanned} {Ground} {Vehicle} using a {Combined} {Approach} of {Model} {Predictive} {Control} and {Proportional} {Navigation}},\n\turl = {https://etd.auburn.edu//handle/10415/6538},\n\tabstract = {This thesis presents a new approach for the guidance and control of a UGV (Unmanned\nGround Vehicle). A special focus was placed on moving obstacles that interfere with the\nplanned path of the vehicle, this is due to the fact that the majority of obstacle avoidance\nresearch has been completed on stationary objects. An obstacle avoidance algorithm was\ndeveloped using an integrated system involving Proportional Navigation (Pro-Nav) and a\nNonlinear Model Predictive Controller (NMPC). An obstacle avoidance variant of the ideal\nproportional navigation law generates command lateral accelerations to avoid obstacles, while\nthe NMPC is used to track the reference trajectory given by the Pro-Nav. The NMPC utilizes\na lateral vehicle dynamic model along with a nonlinear tire model in order to issue control\ninputs. In this application an obstacle avoidance algorithm can take over the control of a\nvehicle until the obstacle is no longer a threat. Another application of a Pro-Nav and NMPC\nalgorithm was tested for leader/follower situations. The performance of the leader/follower\nand obstacle avoidance algorithm is evaluated through diﬀerent simulations.\n\nSimulation of the performance of the PNCAG and NMPC algorithm was conducted us\ning two diﬀerent simulation environments; MATLAB and Simulink Vehicle Dynamics Block\nset. The MATLAB simulation validated the algorithm showing that it could be used to\naccomplish obstacle avoidance. With the algorithm shown to be eﬀective, it was placed into\nthe Vehicle Dynamics Blockset. The Vehicle Dynamics Blockset provided a higher ﬁdelity\nvehicle model to provide a more realistic simulation environment. In addition to obstacle\navoidance, simulation results veriﬁed the performance of a modiﬁed version of the PNCAG\nand NMPC algorithm in a leader/follower scenario. The results show, the algorithm handled\nthe leader/follower and collision avoidance with reasonable error. Overall the algorithm was\nalso able to follow a lead vehicle throughout a double lane change as well as avoid collision\nwith a moving obstacle in four diﬀerent scenarios.},\n\tlanguage = {en},\n\turldate = {2024-06-25},\n\tauthor = {Shaw, Ryan},\n\tmonth = dec,\n\tyear = {2018},\n\tnote = {Accepted: 2018-12-06T20:02:39Z},\n}\n\n\n\n","author_short":["Shaw, R."],"key":"shaw_obstacle_2018","id":"shaw_obstacle_2018","bibbaseid":"shaw-obstacleavoidanceofanunmannedgroundvehicleusingacombinedapproachofmodelpredictivecontrolandproportionalnavigation-2018","role":"author","urls":{"Paper":"https://etd.auburn.edu//handle/10415/6538"},"metadata":{"authorlinks":{}}},"bibtype":"unpublished","biburl":"https://bibbase.org/zotero-group/keb0115/5574615","dataSources":["kDK6fZ4EDThxNKDCP"],"keywords":[],"search_terms":["obstacle","avoidance","unmanned","ground","vehicle","using","combined","approach","model","predictive","control","proportional","navigation","shaw"],"title":"Obstacle Avoidance of an Unmanned Ground Vehicle using a Combined Approach of Model Predictive Control and Proportional Navigation","year":2018}