Optimal low-thrust trajectories to asteroids through an algorithm based on differential dynamic programming

In this paper an optimisation algorithm based on Differential Dynamic Programming is applied to the design of rendezvous and fly-by trajectories to near Earth objects. Differential dynamic programming is a successive approximation technique that computes a feedback control law in correspondence of a fixed number of decision times. In this way the high dimensional problem characteristic of low-thrust optimisation is reduced into a series of small dimensional problems. The proposed method exploits the stage-wise approach to incorporate an adaptive refinement of the discretisation mesh within the optimisation process. A particular interpolation technique was used to preserve the feedback nature of the control law, thus improving robustness against some approximation errors introduced during the adaptation process. The algorithm implements global variations of the control law, which ensure a further increase in robustness. The results presented show how the proposed approach is capable of fully exploiting the multi-body dynamics of the problem; in fact, in one of the study cases, a fly-by of the Earth is scheduled, which was not included in the first guess solution

Perseus: Randomized Point-based Value Iteration for POMDPs

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agents belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems

Using a Dynamic Domain-Specific Modeling Language for the Model-Driven Development of Cross-Platform Mobile Applications

There has been a gradual but steady convergence of dynamic programming languages with modeling languages. One area that can benefit from this convergence is modeldriven development (MDD) especially in the domain of mobile application development. By using a dynamic language to construct a domain-specific modeling language (DSML), it is possible to create models that are executable, exhibit flexible type checking, and provide a smaller cognitive gap between business users, modelers and developers than more traditional model-driven approaches. Dynamic languages have found strong adoption by practitioners of Agile development processes. These processes often rely on developers to rapidly produce working code that meets business needs and to do so in an iterative and incremental way. Such methodologies tend to eschew “throwaway” artifacts and models as being wasteful except as a communication vehicle to produce executable code. These approaches are not readily supported with traditional heavyweight approaches to model-driven development such as the Object Management Group’s Model-Driven Architecture approach. This research asks whether it is possible for a domain-specific modeling language written in a dynamic programming language to define a cross-platform model that can produce native code and do so in a way that developer productivity and code quality are at least as effective as hand-written code produced using native tools. Using a prototype modeling tool, AXIOM (Agile eXecutable and Incremental Objectoriented Modeling), we examine this question through small- and mid-scale experiments and find that the AXIOM approach improved developer productivity by almost 400%, albeit only after some up-front investment. We also find that the generated code can be of equal if not better quality than the equivalent hand-written code. Finally, we find that there are significant challenges in the synthesis of a DSML that can be used to model applications across platforms as diverse as today’s mobile operating systems, which point to intriguing avenues of subsequent research