Abstract

Abstract

We present in detail some of the challenges in developing reusable robotic software. We base that on our experience in developing the CLARAty robotics software, which is a generic object-oriented framework used for the integration of new algorithms in the areas of motion control, vision, manipulation, locomotion, navigation, localization, planning and execution. CLARAty was adapted to a number of heterogeneous robots with different mechanisms and hardware control architectures. In this paper, we also describe how we addressed some of these challenges in the development of the CLARAty software.

Abstract-Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those encountered during planetary or ocean exploration, a further limit is imposed by communication bandwidth and delay.Market based planning can give humans a higher-level interface to multi-robot systems in these scenarios. Operators provide high level tasks and attach a reward to the achievement of each task. The robots then trade these tasks through a market based mechanism. The challenge for the system designer is to create bidding algorithms for the robots that yield high overall system performance.Opportunity cost provides a nice basis for such bidding algorithms since it encapsulates all the costs and benefits we are interested in. Unfortunately, computing it can be difficult. We propose a method of learning opportunity costs in market based planners. We provide analytic results in simplified scenarios and empirical results on our FIRE simulator, which focuses on exploration of Mars by multiple, heterogeneous rovers.Index Terms-Market-based planning, learning, opportunity cost, multi-robot systems.

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