{"id":{"repo_id":"stellenbosch","oai_identifier":"oai:scholar.sun.ac.za:10019.1/136098"},"canonical_url":"https://search.dev.ndltd.org/etd/stellenbosch/oai:scholar.sun.ac.za:10019.1/136098","repository":{"repo_id":"stellenbosch","name":"Stellenbosch University","base_url":"https://scholar.sun.ac.za/server/oai/request"},"display":{"title":"Autonomous Racing and Overtaking for the Voyager Unmanned Ground Vehicle","abstract":"This thesis presents the development of a model predictive contouring control (MPCC) system for a differential-drive unmanned ground vehicle (UGV) to perform autonomous racing and dynamic obstacle avoidance. Two obstacle-avoidance formulations are evaluated separately under matched scenarios: (i) a cost-function (soft) penalty on vehicle–to–obstacle proximity and (ii) hard inequality constraints enforcing minimum separation. The vehicle, the racetrack, and the dynamic obstacles are mathematically modelled. The Voy-ager platform is represented by a differential drive kinematic model. The racetrack is mapped using simultaneous localisation and mapping (SLAM), and the resulting occupancy grid is processed to extract the centreline and track widths. The extracted centreline is parameterised to produce a reference trajectory for the vehicle, represented by a piecewise linear spline as a function of a progress variable. The dynamic obstacle is represented by its predicted position trajectory as a function of time. The model predictive contouring control problem is formulated and solved using a numerical optimiser. Simulation experiments are performed in Matlab to tune the cost function weights and to investigate the effects of planning horizon length and obstacle avoidance formulation. Gazebo simulations are performed to evaluate real-time feasibility across different planning horizon lengths. A configuration with a planning horizon of three samples is found to feasibly execute at a control update rate of 10 Hz. Gazebo simulations and practical experiments with the physical Voyager vehicle are per-formed on representative racetracks with a virtual obstacle vehicle that follows the same centreline reference trajectory as the primary vehicle, but at a slower speed and with a small cross-track offset. The results show that the Voyager vehicle successfully performs autonomous racing and overtaking. The primary vehicle navigates the racetrack with minimum lap times while maintaining safe minimum separation distances between the vehicle and the obstacle during overtaking manoeuvres.","abstract_html":"This thesis presents the development of a model predictive contouring control (MPCC) system for a differential-drive unmanned ground vehicle (UGV) to perform autonomous racing and dynamic obstacle avoidance. Two obstacle-avoidance formulations are evaluated separately under matched scenarios: (i) a cost-function (soft) penalty on vehicle–to–obstacle proximity and (ii) hard inequality constraints enforcing minimum separation. The vehicle, the racetrack, and the dynamic obstacles are mathematically modelled. The Voy-ager platform is represented by a differential drive kinematic model. The racetrack is mapped using simultaneous localisation and mapping (SLAM), and the resulting occupancy grid is processed to extract the centreline and track widths. The extracted centreline is parameterised to produce a reference trajectory for the vehicle, represented by a piecewise linear spline as a function of a progress variable. The dynamic obstacle is represented by its predicted position trajectory as a function of time. The model predictive contouring control problem is formulated and solved using a numerical optimiser. Simulation experiments are performed in Matlab to tune the cost function weights and to investigate the effects of planning horizon length and obstacle avoidance formulation. Gazebo simulations are performed to evaluate real-time feasibility across different planning horizon lengths. A configuration with a planning horizon of three samples is found to feasibly execute at a control update rate of 10 Hz. Gazebo simulations and practical experiments with the physical Voyager vehicle are per-formed on representative racetracks with a virtual obstacle vehicle that follows the same centreline reference trajectory as the primary vehicle, but at a slower speed and with a small cross-track offset. The results show that the Voyager vehicle successfully performs autonomous racing and overtaking. The primary vehicle navigates the racetrack with minimum lap times while maintaining safe minimum separation distances between the vehicle and the obstacle during overtaking manoeuvres.","abstract_has_math":false,"creators":["Jeggels, Dean Edward"],"institution":"Stellenbosch : Stellenbosch University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Engelbrecht, J. A. A."],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-03","date_published":"2026-03","updated_at":"2026-07-24T04:40:12Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.sun.ac.za/handle/10019.1/136098","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Engelbrecht, J. A. 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E. 2026. Autonomous Racing and Overtaking for the Voyager Unmanned Ground Vehicle. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/90214323-2063-4faa-8f8c-89728deba084"]},{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents the development of a model predictive contouring control (MPCC) system for a differential-drive unmanned ground vehicle (UGV) to perform autonomous racing and dynamic obstacle avoidance. Two obstacle-avoidance formulations are evaluated separately under matched scenarios: (i) a cost-function (soft) penalty on vehicle–to–obstacle proximity and (ii) hard inequality constraints enforcing minimum separation. The vehicle, the racetrack, and the dynamic obstacles are mathematically modelled. The Voy-ager platform is represented by a differential drive kinematic model. The racetrack is mapped using simultaneous localisation and mapping (SLAM), and the resulting occupancy grid is processed to extract the centreline and track widths. The extracted centreline is parameterised to produce a reference trajectory for the vehicle, represented by a piecewise linear spline as a function of a progress variable. The dynamic obstacle is represented by its predicted position trajectory as a function of time. The model predictive contouring control problem is formulated and solved using a numerical optimiser. Simulation experiments are performed in Matlab to tune the cost function weights and to investigate the effects of planning horizon length and obstacle avoidance formulation. Gazebo simulations are performed to evaluate real-time feasibility across different planning horizon lengths. A configuration with a planning horizon of three samples is found to feasibly execute at a control update rate of 10 Hz. Gazebo simulations and practical experiments with the physical Voyager vehicle are per-formed on representative racetracks with a virtual obstacle vehicle that follows the same centreline reference trajectory as the primary vehicle, but at a slower speed and with a small cross-track offset. The results show that the Voyager vehicle successfully performs autonomous racing and overtaking. The primary vehicle navigates the racetrack with minimum lap times while maintaining safe minimum separation distances between the vehicle and the obstacle during overtaking manoeuvres."]},{"key":"dc:title","label":"Title","values":["Autonomous Racing and Overtaking for the Voyager Unmanned Ground Vehicle"]}]}],"canonical_facts":{"dc:contributor.advisor":["Engelbrecht, J. A. A."],"dc:contributor.other":["Stellenbosch University. Faculty of Engineering. 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The vehicle, the racetrack, and the dynamic obstacles are mathematically modelled. The Voy-ager platform is represented by a differential drive kinematic model. The racetrack is mapped using simultaneous localisation and mapping (SLAM), and the resulting occupancy grid is processed to extract the centreline and track widths. The extracted centreline is parameterised to produce a reference trajectory for the vehicle, represented by a piecewise linear spline as a function of a progress variable. The dynamic obstacle is represented by its predicted position trajectory as a function of time. The model predictive contouring control problem is formulated and solved using a numerical optimiser. Simulation experiments are performed in Matlab to tune the cost function weights and to investigate the effects of planning horizon length and obstacle avoidance formulation. Gazebo simulations are performed to evaluate real-time feasibility across different planning horizon lengths. A configuration with a planning horizon of three samples is found to feasibly execute at a control update rate of 10 Hz. Gazebo simulations and practical experiments with the physical Voyager vehicle are per-formed on representative racetracks with a virtual obstacle vehicle that follows the same centreline reference trajectory as the primary vehicle, but at a slower speed and with a small cross-track offset. The results show that the Voyager vehicle successfully performs autonomous racing and overtaking. The primary vehicle navigates the racetrack with minimum lap times while maintaining safe minimum separation distances between the vehicle and the obstacle during overtaking manoeuvres."],"dc:identifier.uri":["https://scholar.sun.ac.za/handle/10019.1/136098"],"dc:language.iso":["en"],"dc:publisher":["Stellenbosch : Stellenbosch University"],"dc:title":["Autonomous Racing and Overtaking for the Voyager Unmanned Ground Vehicle"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T04:40:12Z"}