Christian Reinl

Optimal control of multi-vehicle-systems under communication constraints using mixed-integer linear programming

A new planning method for optimal cooperative control of heterogeneous multi-vehicle systems is investigated which enables to account for each vehicles nonlinear physical motion dynamics in a structured environment as well as for connectivity constraints of wireless communication. A general formulation as nonlinear hybrid optimal control problem (HOCP) is presented. A transformation technique is proposed to reduce the large computational efforts for solving HOCPs towards a future online application of this approach. Hereby the general problem is transcribed to a linearized mixed-integer linear programming problem (MILP) which can be solved much more efficiently. The proposed approach is successfully applied to the numerical solution of a representative, cooperative monitoring problem involving heterogeneous vehicles and conditions.

Model-based off-line compensation of path deviation for industrial robots in milling applications

The scope of applications for industrial robots is limited in cases with strong forces at the end effector and high positioning and path accuracies required. Thus, their use in machining applications as a cost-saving, flexible alternative for machining tools is restricted due to mechanical compliance. A model-based off-line concept is presented to analyze, predict, and compensate the resulting path deviation of the robot under process force in milling applications. For this purpose a rigid multi-body dynamics model of the robot extended with additional joint elasticities and tilting effects is coupled with a material removal simulation providing the process forces. After systematically adjusting model parameters, an efficient simulation-based path correction strategy shows significant improvements of path accuracy. The general framework is applicable to any tree structured robots and allows for sensitivity analysis with respect to arbitrary model parameters.

Predictive Control for Multi-Robot Observation of Multiple Moving Targets Based on Discrete-Continuous Linear Models

Abstract The observation of multiple moving targets by cooperating mobile robots is a key problem in many security, surveillance and service applications. In essence, this problem is characterized by a tight coupling of target allocation and continuous trajectory planning. Optimal control of the multi-robot system generally neither permits to neglect physical motion dynamics nor to decouple or successively process target assignment and trajectory planning. In this paper, a numerically robust and stable model-predictive control strategy for solving the problem in the case of discrete-time double-integrator dynamics is presented. Optimization based on linear mixed logical dynamical system models allows for a flexible weighting of different aspects and optimal control inputs for settings of moderate size can be computed in real-time. By simulating sets of randomly generated situations, one can determine a maximum problem size solvable in real-time in terms of the number of considered robots, targets, and length of the prediction horizon. Based on this information, a decentralized control approach is proposed.

OPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE SYSTEMS USING NONLINEAR HYBRID OPTIMAL CONTROL

Abstract Based on a nonlinear hybrid dynamical systems model a new planning method for optimal coordination and control of multiple unmanned vehicles is investigated. The time dependent hybrid state of the overall system consists of discrete (roles, actions) and continuous (e.g. position, orientation, velocity) state variables of the vehicles involved. The evolution in time of the systems hybrid state is described by a hybrid state automaton. The presented approach enables a tight and formal coupling of discrete and continuous state dynamics, i.e. of dynamic role and action assignment and sequencing as well as of the physical motion dynamics of a single vehicle modeled by nonlinear differential equations. The planning problem of determining optimal hybrid state trajectories that minimize a cost function as time or energy for optimal multi-vehicle cooperation subject to constraints including the vehicles motion dynamics is transformed to a mixed-binary dynamic optimization problem being solved numerically. The numerical method consists of an inner iteration where multiphase optimal control problems are solved using a direct collocation method and an outer iteration based on a branch-and-bound search of the discrete solution space. The approach presented in this paper is applied to the scenarios of optimal simultaneous waypoint or target sequencing and dynamic trajectory planning for a team of unmanned aerial vehicles in a plane and to optimal role assignment and physics-based trajectories in robot soccer.

Analysis of Industrial Robot Structure and Milling Process Interaction for Path Manipulation

Industrial robots are used in a great variety of applications for handling, welding, assembling and milling operations. Especially for machining operations, industrial robots represent a cost-saving and flexible alternative compared to standard machine tools. Reduced pose and path accuracy, especially under process force load due to the high mechanical compliance, restrict the use of industrial robots for machining applications with high accuracy requirements. In this chapter, a method is presented to predict and compensate path deviation of robots resulting from process forces. A process force simulation based on a material removal calculation is presented. Furthermore, a rigid multi-body dynamic system’s model of the robot is extended by joint elasticities and tilting effects, which are modeled by spring-damper-models at actuated and additional virtual axes. By coupling the removal simulation with the robot model the interaction of the milling process with the robot structure can be analyzed by evaluating the path deviation and surface structure. With the knowledge of interaction along the milling path a general model-based path correction strategy is introduced to significantly improve accuracy in milling operations.

Optimalsteuerung kooperierender Mehrfahrzeugsysteme Optimal Control of Cooperative Multi-Vehicle Systems

Zusammenfassung Nichtlineare hybride dynamische Systemmodelle kooperativer Optimalsteuerungsprobleme ermöglichen eine enge und formale Kopplung von diskreter und kontinuierlicher Zustandsdynamik, d. h. von dynamischer Rollen-, Aktionszuweisung mit wechselnder physikalischer Bewegungsdynamik. In den resultierenden gemischt-ganzzahligen Mehrphasen-Optimalsteuerungsproblemen können Beschränkungen an diskrete und kontinuierliche Zustands- und Steuervariablen berücksichtigt werden, z. B. Formations- oder Kommunikationsanforderungen. Zwei numerische Verfahren werden untersucht: ein Dekompositionsansatz mit Branch-and-Bound und direktem Kollokationsverfahren sowie die Approximation durch große gemischt-ganzzahlige lineare Optimierungsaufgaben. Die Verfahren werden auf exemplarische Problemstellungen angewendet: Die simultane Wegpunktreihenfolge- und Trajektorienoptimierung von Luftfahrzeugen sowie die Optimierung von Rollenverteilung und Trajektorien im Roboterfußball.

MM-ulator: Towards a Common Evaluation Platform for Mixed Mode Environments

We investigate the interaction of mobile robots, relying on information provided by heterogeneous sensor nodes, to accomplish a mission. Cooperative, adaptive and responsive monitoring in Mixed-Mode Environments (MMEs) raises the need for multi-disciplinary research initiatives. To date, such research initiatives are limited since each discipline focusses on its domain specific simulation or testbed environment. Existing evaluation environments do not respect the interdependencies occurring in MMEs. As a consequence, holistic validation for development, debugging, and performance analysis requires an evaluation tool incorporating multi-disciplinary demands. In the context of MMEs, we discuss existing solutions and highlight the synergetic benefits of a common evaluation tool. Based on this analysis we present the concept of the MM-ulator : a novel architecture for an evaluation tool incorporating the necessary diversity for multi-agent hard-/software-in-the-loop simulation in a modular and scalable way.

Simulation and evaluation of mixed-mode environments: towards higher quality of simulations

For rescue and surveillance scenarios, the Mixed-Mode Environments (MMEs) for data acquisition, processing, and dissemination have been proposed. Evaluation of the algorithms and protocols developed for such environments before deployment is vital. However, there is a lack of realistic testbeds for MMEs due to reasons such as high costs for their setup and maintenance. Hence, simulation platforms are usually the tool of choice when testing algorithms and protocols for MMEs. However, existing simulators are not able to fully support detailed evaluation of complex scenarios in MMEs. This is usually due to lack of highly accurate models for the simulated entities and environments. This affects the results which are obtained by using such simulators. In this paper, we highlight the need to consider the Quality of Simulations (QoSim), in particular aspects such as accuracy, validity, certainty, and acceptability. The focus of this paper is to understand the gap between real-world experiments and simulations for MMEs. The paper presents key QoSim concepts and characteristics for MMEs simulations, describing the aspects of contents of simulation, processing of simulation, and simulation outputs. Eventually, a road map for improving existing simulation environments is proposed.