Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints

Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the model predictive control (MPC) paradigm. Instead, in this paper, we present a unified MPC-based trajectory planning and control scheme that guarantees feasibility with respect to road boundaries, the static and dynamic environment, and enforces passenger comfort constraints. The scheme is evaluated rigorously in a variety of scenarios focused on proving the effectiveness of the optimal control problem (OCP) design and real-time solution methods. The prototype code will be released at https://github.com/WATonomous/control

An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic

A systematic approach to cooperative driving systems based on optimal control allocation

This dissertation proposes a systematic approach to vehicle dynamic control, where interaction between the human driver and on-board automated driving systems is considered a fundamental part of the overall control design. The hierarchical control system is to address motion control in three regions. First is normal driving, where the vehicle stays within the linear region of the tyre. Second is limit driving, where the vehicle stays within the nonlinear region of the tyre. Third is over-limit driving, where the driver demands go beyond the tyre force limits. The third case is addressed by a proposed control moderator (CM). The aim is to consider all three cases within a consistent hierarchical chassis control framework. The upper-level of the hierarchical control structure relates to both optimal vehicle control under normal and limit driving, and saturating driver demands for over-limit driving, these corresponding to a fully autonomous controller and driver assistance controller respectively. Model Predictive Control (MPC) is used as the core control technique for path following under normal driving conditions, and a Moderated Particle Reference (MPR) control strategy is proposed for the road departure mitigation during limit and over-limit driving. The MPR model is validated to ensure predictable and stable operation near the friction limits, maintaining controllability for curvature and speed tracking, which effectively limits demands on the vehicle while preserving the control interaction of the driver. In the next level of the hierarchical control structure, a novel control allocation (CA) approach based on pseudo-inverse method is proposed, while a general linearly constrained quadratic programming (CQP) approach is considered as a benchmark. From extended simulation experiments, it is found that the proposed Pseudo-Inverse CA (PICA) method can achieve a close match to CQP performance in normal driving conditions. This applies for multiple control targets (including path tracking, energy-efficient, etc.) and PICA is found to achieve improved performance in limit and over-limit driving, again addressing multiple control targets (including road departure mitigation, energyefficient, etc.). Furthermore, the PICA method shows its inherent advantages of achieving the same control performance with much less computational cost and is guaranteed to provide a feasible control target for the actuators to track during the highly dynamic driving scenarios. In addition, it can effectively solve the constrained optimal control problem with additional mechanical and electronic actuator constraints. Thus, the proposed PICA method, which uses Control Re-Allocation (making multiple calls to the pseudo-inverse operator) can be considered a feasible and novel alternative approach to control allocation, with advantages over the standard CQP method. Finally, in the lower-level of the hierarchical control structure, the desired tyre control variables are obtained through an analytical inverse tyre model and a sliding mode controller (SMC) is employed for the actuators to track the control target. The proposed hierarchical control system is validated with both driving simulator studies and from testing a real vehicle, considering a wide range of driving scenarios, from low-speed path tracking to safety-critical vehicle dynamic control. It therefore opens up a systematic approach to extended vehicle control applications, from fully autonomous driving to driver assistance systems and control objects from passenger cars to vehicles with higher centre of gravity (CoG) like SUVs, trucks and etc. . .

Time Distance: A Novel Collision Prediction and Path Planning Method

Motion planning is an active field of research in robot navigation and autonomous driving. There are plenty of classical and heuristic motion planning methods applicable to mobile robots and ground vehicles. This paper is dedicated to introducing a novel method for collision prediction and path planning. The method is called Time Distance (TD), and its basis returns to the swept volume idea. However, there are considerable differences between the TD method and existing methods associated with the swept volume concept. In this method, time is obtained as a dependent variable in TD functions. TD functions are functions of location, velocity, and geometry of objects, determining the TD of objects with respect to any location. Known as a relative concept, TD is defined as the time interval that must be spent in order for an object to reach a certain location. It is firstly defined for the one-dimensional case and then generalized to 2D space. The collision prediction algorithm consists of obtaining the TD of different points of an object (the vehicle) with respect to all objects of the environment using an explicit function which is a function of TD functions. The path planning algorithm uses TD functions and two other functions called Z-Infinity and Route Function to create the collision-free path in a dynamic environment. Both the collision prediction and the path planning algorithms are evaluated in simulations. Comparisons indicate the capability of the method to generate length optimal paths as the most effective methods do

Collision avoidance and dynamic modeling for wheeled mobile robots and industrial manipulators

Collision Avoidance and Dynamic Modeling are key topics for researchers dealing with mobile and industrial robotics. A wide variety of algorithms, approaches and methodologies have been exploited, designed or adapted to tackle the problems of finding safe trajectories for mobile robots and industrial manipulators, and of calculating reliable dynamics models able to capture expected and possible also unexpected behaviors of robots. The knowledge of these two aspects and their potential is important to ensure the efficient and correct functioning of Industry 4.0 plants such as automated warehouses, autonomous surveillance systems and assembly lines. Collision avoidance is a crucial aspect to improve automation and safety, and to solve the problem of planning collision-free trajectories in systems composed of multiple autonomous agents such as unmanned mobile robots and manipulators with several degrees of freedom. A rigorous and accurate model explaining the dynamics of robots, is necessary to tackle tasks such as simulation, torque estimation, reduction of mechanical vibrations and design of control law

Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms

Advanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution.Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.Te authors would like to acknowledge the ESCEL Project ENABLE-S3 (with Grant no. 692455-2) for the support in the development of this work

Nonsmooth Control Barrier Functions for Obstacle Avoidance between Convex Regions

In this paper, we focus on non-conservative obstacle avoidance between robots with control affine dynamics with strictly convex and polytopic shapes. The core challenge for this obstacle avoidance problem is that the minimum distance between strictly convex regions or polytopes is generally implicit and non-smooth, such that distance constraints cannot be enforced directly in the optimization problem. To handle this challenge, we employ non-smooth control barrier functions to reformulate the avoidance problem in the dual space, with the positivity of the minimum distance between robots equivalently expressed using a quadratic program. Our approach is proven to guarantee system safety. We theoretically analyze the smoothness properties of the minimum distance quadratic program and its KKT conditions. We validate our approach by demonstrating computationally-efficient obstacle avoidance for multi-agent robotic systems with strictly convex and polytopic shapes. To our best knowledge, this is the first time a real-time QP problem can be formulated for general non-conservative avoidance between strictly convex shapes and polytopes.Comment: 17 page

Environment Modeling, Action Classification, and Control for Urban Automated Driving

This thesis discusses the design and implementation of WATonomous' Automated Driving Stack (ADS), which is capable of performing robo-taxi services in specific operational domains when deployed to WATonomous' research vehicle (Bolty). Three ADS modules are discussed in detail: (1) mapping, environment modeling, and behavioral planning, (2) action classification in video streams, and (3) trajectory planning and control. Additionally, the software architecture within which the ADS is developed and deployed, and the ADS data pipeline itself, are outlined. The thesis begins with preliminaries on WATonomous' Dockerized software architecture (coined watod) which runs and orchestrates the communication of the ADS modules. The watod ecosystem, due to its Dockerized and cloud-based design, enables rapid prototyping of new software modules, rapid onboarding of new team members, and parallel execution of many ADS development instances on the WATonomous server cluster's Virtual Machines (VMs). Cloud-based CARLA simulation development of the ADS and deployment to the Bolty research vehicle are also encapsulated in and facilitated by the watod ecosystem. The ADS can be developed in simulation and deployed to the physical research vehicle without modifications to the ADS modules due to the replication of the physical platform in the Carla ROS Bridge sensor configuration. The design of the ADS data pipeline is also presented, from raw sensor input to the Controlled Area Network Bus (CAN Bus) interface, as well as the human-computer interface. The first ADS module discussed is the mapping and environment modeling module. Environment modeling is the backbone of how autonomous agents understand the world, and therefore has significant implications for decision-making and verification. Motivated by the success of relational mapping tools such as Lanelet2, we present the Dynamic Relation Graph (DRG). The DRG is a novel method for extending prior relational maps to include online observations, creating a unified environment model which incorporates both prior and online data sources. Our prototype implementation models a finite set of heterogeneous features including road signage and pedestrian movement. However, the methodology behind the DRG can be expanded to a wider range of features in a fashion that does not increase the complexity of behavioral planning. Simulated stress tests indicate the DRG's effectiveness in decreasing decision-making complexity, and deployment to the WATonomous research vehicle demonstrates its practical utility. The prototype code is available at https://github.com/WATonomous/DRG. The second ADS module discussed is the action classification module. When applied in the context of Autonomous Vehicles (AVs), action classification algorithms can help enrich an AV's environment model and understanding of the world to improve behavioral planning decisions. Towards these improvements in AV decision-making, we propose a novel online action recognition system, coined the Road Action Detection Network (RAD-Net). RAD-Net formulates the problem of active agent detection and adapts ideas about actor-context relations from human activity recognition in a straightforward two-stage pipeline for action detection and classification. We show that our proposed scheme can outperform the baseline on the ICCV 2021 Road Challenge dataset. Furthermore, by integrating RAD-Net with the ADS' perception stack and the DRG, we demonstrate how a higher-order understanding of agent actions in the environment can improve decisions on a real AV system. The last ADS module discussed is trajectory planning and control. Trajectory planning and control have historically been separated into two modules in automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding obstacles and staying on the road surface, whereas the controller tries its best to follow an ever changing reference trajectory. We argue that this separation is (1) flawed due to the mismatch between planned trajectories and what the controller can feasibly execute, and (2) unnecessary due to the flexibility of the Model Predictive Control (MPC) paradigm. Instead, in this thesis, we present a unified MPC-based trajectory planning and control scheme that guarantees feasibility with respect to road boundaries, the static and dynamic environment, and enforces passenger comfort constraints. The scheme is evaluated rigorously in a variety of scenarios focused on proving the effectiveness of the OCP design and real-time solution methods. The prototype code is available at github.com/WATonomous/control

Aeronautical engineering: A continuing bibliography with indexes (supplement 250)

This bibliography lists 420 reports, articles, and other documents introduced into the NASA scientific and technical information system in February, 1990. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics