Model-based Reactive Programming of Cooperative Vehicles for Mars Exploration

Abstract

In the future webs of unmanned vehicles will act together to robustly achieve elaborate missions within uncertain environments. This web may be a distributed satellite system forming an interferometer, or may be a heterogenous set of rovers and blimps exploring Mars. We coordinate these systems by introducing a reactive model-based programming language (RMPL) that combines within a single unified representation the flexibility of embedded programming and reactive execution languages, and the deliberative reasoning power of temporal planners. To support fast mission planning as graph search, the KIRK planner compiles an RMPL program into a temporal plan network (TPN), similar to those used by temporal planners, but extended for symbolic constraints and decisions. To robustly coordinate air vehicle or rover maneuvers we combine the Kirk planning algorithm with randomized algorithms for kinodynamic path planning. Finally, we describe our Mars exploration testbed, including four RWI ATRV vehicles. 1 Model-based Programming The recent spread of advanced processing to embedded systems has created vehicles that execute complex missions with increasing levels of autonomy, in space, on land and in the air. These vehicles must respond to uncertain and often unforgiving environments, both with a fast response time and with a high assurance of first time success. The future looks to the creation of cooperative robotic networks. For example, giant space telescopes are being deployed that are composed of satellites carrying the telescope’s different optical components. These satellites act in concert to image planets around other stars, or unusual weather events on earth. In addition, the 2000 Mars Program Independent Assessment Team recommended an exploration architecture that adopts a more global view. For example, a heterogenous set of vehicles, such as orbiters, rovers and blimps might work in concert to identify and evaluate sites of greatest scientific interest. The creation of robotic networks cannot be supported by the current programming practice alone. Recent mission failures, such as the Mars Climate Orbiter and Polar Landers, highlight the challenge of creating highly capable vehicles within realistic budget limits. Due to cost constraints, spacecraft flight software teams often do not have time to think through all the plausible situations that might arise, encode the appropriate responses within their software and then validate that software with high assurance. To break through this barrier we need to invent a new programming paradigm. In this paper we advocate the creation of embedded, model-based programming languages that support the ability to specify global strategies for multivehicle coordination. First, we argue that the programmer should retain control for the overall success of a mission, by programming game plans and contingencies that in the programmer’s experience will ensure a high degree of success. The programmer should be able to program these game plans using features of the best embedded programming languages available. For example, reactive synchronous languages[5], like Esterel, Lustre and Signal, offer a rich set of constructs for interacting with sensors and actuators, for creating complex behaviors involving concurrency and preemption, and for modularizing these behaviors using all the standard encapsulation mechanisms. Model-based programming extends this style of reactive language with a minimal set of constructs neccessary to perform flexible mission coordination, while hiding its reasoning capabilities under the hood of the language’s interpreter or compiler. Second, we argue that model-based programming languages should focus on elevating the programmer’s thinking, by automating the process of reasoning about low-level system interactions. Many recent space mission failures, such as Mars Climate Orbiter and Mars Polar Lander, can be isolated to difficulties in reasoning through low-level system int