Abstract-A new planning method for optimal cooperative control of heterogeneous multi-vehicle systems is investigated which enables to account for each vehicle's 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.

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.

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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. I. MOTIVATIONMajor fields of machining applications for industrial robots are automated pre-machining, deburring and fettling of cast parts or trimming of carbon fiber reinforced laminate. Due to a kinematic structure with usually six axes industrial robots can cover a large working space and are able to reach difficult work piece positions, so that they can be applied to perform complex machining operations. Therefore, compared to standard machine tools, industrial robots on the one hand offer an economic machining while they do only reach a limited absolute and repeat accuracy on the other hand; e.g. the repeat accuracy of the industrial robot used for the research presented in this paper is ±0.06 mm [11].Under high process load, as appears in milling operations, an additional deviation of the tool center point (TCP) occurs. Measured deflections of 0.25 mm under loads of 100 N in earlier tests [2] confirmed the expected compliance, which is resulting from the low structural stiffness of the serial robot kinematics.In milling applications, the effective process forces lead to significant trajectory deviations that are resulting in a variation of the cutting condition at the cutter. Thus, milling with industrial robots is characterized by the strong interaction of the cutting process and the mechanical robot structure. It is observed, that deviations mainly consist of a static offset

Abstract. 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.