Regelungs- und Systemtheorie
https://kobra.uni-kassel.de:443/handle/123456789/2014042245367
2023-09-24T09:58:59ZConsistent Hierarchical Control in Cooperative Autonomous Driving
https://kobra.uni-kassel.de:443/handle/123456789/13884
This thesis is set within the context of the problem of motion planning in autonomous driving. Due to nonlinear system dynamics and non-convex constraints, this problem is challenging even under simplifying assumptions (e.g. non-cooperating traffic participants obey traffic rules, lossless inter-vehicle communication, absence of measuring errors). Typical solution approaches rely on a decomposition of the problem into subproblems which are easier to solve. This decomposition often results in a hierarchy of subproblems which are to be solved sequentially, where succeeding problems rely on the solution of preceding ones, such that dependencies between subproblems exist. Existing procedures for the design of such approaches have in common that these dependencies are not accounted for by the solution methodology. This can lead to situations where succeeding problems cannot be solved due to unsuitable solutions of preceeding subproblems.
This thesis presents a design methodology which is able to account for such dependencies in a hierarchical solution approach. The methodology is developed under the assumption of a three-layer hierarchical solution approach comprising trajectory planning, tracking control, and a high-level controller which is to determine cooperating groups and their actions. While the major focus of the thesis is on hierarchical consistency, i.e., safe interaction of the framework's layers, computational efficiency is also a main concern.
The basis of the planning problem is its formulation as a mixed-integer optimization problem, whose globally optimal solution guarantees constraint compliance. This approach uses predictions of future values and conducts the optimization frequently anew in the spirit of receding horizon control in order to limit the impact of uncertainty.
The so-called maneuver concept is introduced in order to improve computational efficiency by exploiting structure in on-road traffic. A maneuver characterizes a set of qualitatively similar trajectories of a group of vehicles and is modeled as a hybrid automaton. This allows to compute controllable sets, containing initial states for which a maneuver can be executed. Given these, the high-level controller can assess the feasibility of many different options without computing plans when choosing the most promising one. Approximative set computations allow for both the application to typically high-dimensional state spaces in cooperative autonomous driving and for the approximation of solutions to the planning problem. Thus, no optimization is required online, which reduces computation times.
As vehicles often fail to perfectly follow a reference trajectory, safety margins to obstacles must be provided during planning. Safe interaction between planning and tracking layer then requires to only plan trajectories for which the maximum tracking error does not violate the pre-defined safety margins. The maximum error is determined based on computation of an invariant set of the tracking error dynamics, accounting for a set of possible reference trajectories, constraints on states and inputs, and uncertainty in a vehicle's tire model. This step is only carried out during offline design, while online operation of the tracking controller only requires computationally efficient algebraic operations. Both theoretical results and numerical simulations demonstrate the efficacy of the framework.
2022-01-01T00:00:00ZEilbrecht, Jan MichaelThis thesis is set within the context of the problem of motion planning in autonomous driving. Due to nonlinear system dynamics and non-convex constraints, this problem is challenging even under simplifying assumptions (e.g. non-cooperating traffic participants obey traffic rules, lossless inter-vehicle communication, absence of measuring errors). Typical solution approaches rely on a decomposition of the problem into subproblems which are easier to solve. This decomposition often results in a hierarchy of subproblems which are to be solved sequentially, where succeeding problems rely on the solution of preceding ones, such that dependencies between subproblems exist. Existing procedures for the design of such approaches have in common that these dependencies are not accounted for by the solution methodology. This can lead to situations where succeeding problems cannot be solved due to unsuitable solutions of preceeding subproblems.
This thesis presents a design methodology which is able to account for such dependencies in a hierarchical solution approach. The methodology is developed under the assumption of a three-layer hierarchical solution approach comprising trajectory planning, tracking control, and a high-level controller which is to determine cooperating groups and their actions. While the major focus of the thesis is on hierarchical consistency, i.e., safe interaction of the framework's layers, computational efficiency is also a main concern.
The basis of the planning problem is its formulation as a mixed-integer optimization problem, whose globally optimal solution guarantees constraint compliance. This approach uses predictions of future values and conducts the optimization frequently anew in the spirit of receding horizon control in order to limit the impact of uncertainty.
The so-called maneuver concept is introduced in order to improve computational efficiency by exploiting structure in on-road traffic. A maneuver characterizes a set of qualitatively similar trajectories of a group of vehicles and is modeled as a hybrid automaton. This allows to compute controllable sets, containing initial states for which a maneuver can be executed. Given these, the high-level controller can assess the feasibility of many different options without computing plans when choosing the most promising one. Approximative set computations allow for both the application to typically high-dimensional state spaces in cooperative autonomous driving and for the approximation of solutions to the planning problem. Thus, no optimization is required online, which reduces computation times.
As vehicles often fail to perfectly follow a reference trajectory, safety margins to obstacles must be provided during planning. Safe interaction between planning and tracking layer then requires to only plan trajectories for which the maximum tracking error does not violate the pre-defined safety margins. The maximum error is determined based on computation of an invariant set of the tracking error dynamics, accounting for a set of possible reference trajectories, constraints on states and inputs, and uncertainty in a vehicle's tire model. This step is only carried out during offline design, while online operation of the tracking controller only requires computationally efficient algebraic operations. Both theoretical results and numerical simulations demonstrate the efficacy of the framework.Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators
https://kobra.uni-kassel.de:443/handle/123456789/2014050245405
This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming.
The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex
optimization problem is approximated by a mixed-integer program (MIP). It is further
effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased.
Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to
previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold.
The developed methods have been successfully applied to a developed demonstrator,
where an industrial robot works in the same space as a human operator. The task of the
industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.
2014-05-02T00:00:00ZDing, HaoThis thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming.
The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex
optimization problem is approximated by a mixed-integer program (MIP). It is further
effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased.
Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to
previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold.
The developed methods have been successfully applied to a developed demonstrator,
where an industrial robot works in the same space as a human operator. The task of the
industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.