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- Bergman, Kristoffer, 1990-
(author)
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Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments
- 2021
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Doctoral thesis (other academic/artistic)abstract
- During the last decades, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars, unmanned aerial vehicles and robotic manipulators. The objective in optimal motion planning problems is to find feasible motion plans that also optimize a performance measure. From a control perspective, the problem is an instance of an optimal control problem. This thesis addresses optimal motion planning problems for complex dynamical systems that operate in unstructured environments, where no prior reference such as road-lane information is available. Some example scenarios are autonomous docking of vessels in harbors and autonomous parking of self-driving tractor-trailer vehicles at loading sites. The focus is to develop optimal motion planning algorithms that can reliably be applied to these types of problems. This is achieved by combining recent ideas from automatic control, numerical optimization and robotics.The first contribution is a systematic approach for computing local solutions to motion planning problems in challenging unstructured environments. The solutions are computed by combining homotopy methods and direct optimal control techniques. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments, where the presented method outperforms a state-of-the-art asymptotically optimal motion planner based on random sampling.The second contribution is an optimization-based framework for automatic generation of motion primitives for lattice-based motion planners. Given a family of systems, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance, the framework computes a library of motion primitives by simultaneously optimizing the motions and the terminal states.The final contribution of this thesis is a motion planning framework that combines the strengths of sampling-based planners with direct optimal control in a novel way. The sampling-based planner is applied to the problem in a first step using a discretized search space, where the system dynamics and objective function are chosen to coincide with those used in a second step based on optimal control. This combination ensures that the sampling-based motion planner provides a feasible motion plan which is highly suitable as warm-start to the optimal control step. Furthermore, the second step is modified such that it also can be applied in a receding-horizon fashion, where the proposed combination of methods is used to provide theoretical guarantees in terms of recursive feasibility, worst-case objective function value and convergence to the terminal state. The proposed motion planning framework is successfully applied to several problems in challenging unstructured environments for tractor-trailer vehicles. The framework is also applied and tailored for maritime navigation for vessels in archipelagos and harbors, where it is able to compute energy-efficient trajectories which complies with the international regulations for preventing collisions at sea.
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| 2. |
- Sahlholm, Per, 1979-
(author)
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Distributed Road Grade Estimation for Heavy Duty Vehicles
- 2011
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Doctoral thesis (other academic/artistic)abstract
- An increasing need for goods and passenger transportation drives continued worldwide growth in traffic. As traffic increases environmental concerns, traffic safety, and cost efficiency become ever more important. Advancements in microelectronics open the possibility to address these issues through new advanced driver assistance systems. Applications such as predictive cruise control, automated gearbox control, predictive front lighting control, and hybrid vehicle state-of-charge control decrease the energy consumption of vehicles and increase the safety. These control systems can benefit significantly from preview road grade information. This information is currently obtained using specialized survey vehicles, and is not widely available. This thesis proposes new methods to obtain road grade information using on-board sensors. The task of creating road grade maps is addressed by the proposal of a framework where vehicles using a road network collect the necessary data for estimating the road grade. The estimation can then be carried out locally in the vehicle, or in the presence of a communication link to the infrastructure, centrally. In either case the accuracy of the map increases over time, and costly road surveys can be avoided. This thesis presents a new distributed method for creating accurate road grade maps for vehicle control applications. Standard heavy duty vehicles in normal operation are used to collect measurements. Estimates from multiple passes along a road segment are merged to form a road grade map, which improves each time a vehicle retraces a route. The design and implementation of the road grade estimator are described, and the performance is experimentally evaluated using real vehicles. Three different grade estimation methods, based on different assumption on the road grade signal, are proposed and compared. They all use data from sensors that are standard equipment in heavy duty vehicles. Measurements of the vehicle speed and the engine torque are combined with observations of the road altitude from a GPS receiver, using vehicle and road models. The operation of the estimators is adjusted during gearshifts, braking, and poor satellite coverage, to account for variations in sensor and model reliability. The estimated error covariances of the road grade estimates are used together with their absolute positions to update a stored road grade map. Highway driving trials show that the proposed estimators produce accurate road grade data. The estimation performance improves as the number of road segment traces increases. A vehicle equipped with the proposed system will rapidly develop a road grade map for its area of operation. Simulations show that collaborative generation of the third dimension for a pre-existing large area two-dimensional map is feasible. The experimental results indicate that road grade estimates from the proposed methods are accurate enough to be used in predictive vehicle control systems to enhance safety, efficiency, and driver comfort in heavy duty vehicles. The grade estimators may also be used for on-line validation of road grade information from other sources. This is important in on-board applications, since the envisioned control applications can degrade vehicle performance if inaccurate data are used.
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