Kinematic Control System for Car-Like Vehicles

Abstract. In this paper, we have highlighted the importance of the WMR model for designing control strategies. In this sense, the differential model has been used as reference model in order to design the control algorithm. After the control has been design, new actions will be generated for each additional wheel of the real vehicle (non-differential model). This new approach simplifies the overall control systems design procedure. The examples included in the paper, illustrate the more outstanding issues of the designed control. Moreover, we have particularized this control for the line tracking based on a vision system. A velocity control in the longitudinal coordinate has been implemented instead of a position control because we have no longitudinal information. Also, we have simulated and validated this control, studying the effect of the sampling time on the WMR behavior

Distributed formation tracking control of multiple car-like robots

In this thesis, distributed formation tracking control of multiple car-like robots is studied. Each vehicle can communicate and send or receive states information to or from a portion of other vehicles. The communication topology is characterized by a graph. Each vehicle is considered as a vertex in the graph and each communication link is considered as an edge in the graph. The unicycles are modeled firstly by both kinematic systems. Distributed controllers for vehicle kinematics are designed with the aid of graph theory. Two control algorithms are designed based on the chained-form system and its transformation respectively. Both algorithms achieve exponential convergence to the desired reference states. Then vehicle dynamics is considered and dynamic controllers are designed with the aid of two types of kinematic-based controllers proposed in the first section. Finally, a special case of switching graph is addressed considering the probability of vehicle disability and links breakage

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

Racing car chassis

Cílem této bakalářské práce je analýza současných konceptů podvozků závodních okruhových aut. V první části práce je zpracován historický vývoj, charakteristika kol a pneumatik s reprezentací dobře známých produktů. V druhé části je popsán systém odpružení, pružné média a tlumící členy. Systémy odpružení je zde rozdělen na nezávisle a polozávislé zavěšení kol a odpružení pevných náprav. Následující oddíl této práce je zaměřený na standardní kontrolní systémy, jako jsou ABS, ESC a TSC. Závěr přináší rychlé shrnutí této problematiky.The aim of this bachelor thesis is to analyse contemporary concepts of circuit race car chassis. In the first part of the thesis, the historical evolution is described and then wheels and tires characteristic within some well-known brand products are represented. The second important part includes the suspension systems, springing medium and damping members. The suspension systems are further divided to independent and semi-independent solutions and rigid axle suspensions. The end of this thesis deals with the standard braking control systems, such as ABS, ESC and TCS. The conclusion brings the quick summary of this subject.

AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure

Assistive trajectories for human-in-the-loop mobile robotic platforms

Autonomous and semi-autonomous smoothly interruptible trajectories are developed which are highly suitable for application in tele-operated mobile robots, operator on-board military mobile ground platforms, and other mobility assistance platforms. These trajectories will allow a navigational system to provide assistance to the operator in the loop, for purpose built robots or remotely operated platforms. This will allow the platform to function well beyond the line-of-sight of the operator, enabling remote operation inside a building, surveillance, or advanced observations whilst keeping the operator in a safe location. In addition, on-board operators can be assisted to navigate without collision when distracted, or under-fire, or when physically disabled by injury