An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one, and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher and Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer/discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

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In this report, we present an approach to optimal planning and flexible execution for a set of spatially distributed tasks related by temporal ordering constraints such as precedence, synchronization, or non-overlapping constraints. We integrate an optimal planner for task allocation and scheduling with cross-schedule dependencies with a flexible, distributed plan execution strategy. The integrated system performs optimal task allocation and scheduling for tasks related by temporal constraints, and ensures that plans are executed smoothly in the face of real-world variations in operation speed and task execution time. It also ensures that plan execution degrades gracefully in the event of task failure. We demonstrate the capabilities of our approach on a team of three pioneer robots operating in an indoor environment. Experimental results focus on the flexible execution strategy and illustrate that it effectively enables execution of the optimal plan and prevents constraint violations. The overall approach is thus demonstrated to be effective for constrained planning and execution in the face of realworld variations.

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Abstract-This paper presents an approach for deploying a team of mobile sensor nodes to form a sensor network in indoor environments. The challenge in this work is that the mobile sensor nodes have no ability for localization or obstacle avoidance. Thus, our approach entails the use of more capable "helper" robots that "herd" the mobile sensor nodes into their deployment positions. To extensively explore the issues of heterogeneity in multi-robot teams, we employ the use of two types of helper robots -one that acts as a leader and a second that: 1) acts as a follower and 2) autonomously teleoperates the mobile sensor nodes. Due to limited sensing capabilities, neither of these helper robots can herd the mobile sensor nodes alone; instead, our approach enables the team as a whole to successfully accomplish the sensor deployment task. Our approach involves the use of line-of-sight formation keeping, which enables the follower robot to use visual markers to move the group along the path executed by the leader robot. We present results of the implementation of this approach in simulation, as well as results to date in the implementation on physical robot systems. To our knowledge, this is the first implementation of robot herding using such highly heterogeneous robots, in which no single type of robot could accomplish the sensor network deployment task, even if multiple copies of that robot type were available. I. INTRODUCTIONIn this paper, we address the issue of robot team heterogeneity in the context of mobile sensor net deployment in an indoor environment. In general, if all mobile sensor nodes have the ability to locomote and to sense other robots and obstacles in the environment, then a distributed dispersion algorithm based on potential fields (e.g., [1]) would be an appropriate solution strategy for deploying the mobile sensor network. However, if some of the robots do not have the sensing capability to detect obstacles or other robots (but they do have locomotion capabilities and special-purpose sensors needed in the sensor network, such as acoustic or chemical sensors), then such a solution strategy would no longer work. On the other hand, if some of the robot team members were highly capable robots that could help navigate the less capable robots, then a workable solution strategy would be for the more capable robots to guide the less capable robots to a deployment position. This is the approach we present in this paper.Section II provides an overview to our approach and the behaviors of the various robots. In Section III, we discuss our approach to vision-based detection of robot ID and relative pose using visual markers. Section IV discusses our approach to maintaining line-of-sight formations. Our approach for planning for sensor net deployment is briefly discussed in Section V. We present the results of our integrated approach

Abstract-d Caves on other planetary bodies offer sheltered habitat for future human explorers and numerous clues to a planet's past for scientists. While recent orbital imagery provides exciting new details about cave entrances on the Moon and Mars, the interiors of these caves are still unknown and not observable from orbit. Multi-robot teams offer unique solutions for exploration and modeling subsurface voids during precursor missions. Robot teams that are diverse in terms of size, mobility, sensing, and capability can provide great advantages, but this diversity, coupled with inherently distinct low-level behavior architectures, makes coordination a challenge. This paper presents a framework that consists of an autonomous frontier and capability-based task generator, a distributed market-based strategy for coordinating and allocating tasks to the different team members, and a communication paradigm for seamless interaction between the different robots in the system. Robots have different sensors, (in the representative robot team used for testing: 2D mapping sensors, 3D modeling sensors, or no exteroceptive sensors), and varying levels of mobility. Tasks are generated to explore, model, and take science samples. Based on an individual robot's capability and associated cost for executing a generated task, a robot is autonomously selected for task execution. The robots create coarse online maps and store collected data for high resolution offline modeling. The coordination approach has been field tested at a mock cave site with highly-unstructured natural terrain, as well as an outdoor patio area. Initial results are promising for applicability of the proposed multi-robot framework to exploration and modeling of planetary caves.

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An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.