AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

The Changing Waves of Migration from the Balkans to Turkey: A Historical Account

Ahmet İçduygu and Deniz Sert tell the history of migration from the Balkans to Turkey from the end of the nineteenth century to the present. They relate this history to nation-building, but also to economic conditions and specific Turkish concerns, such as the perceived need for immigration to compensate for a declining population at that time. They also demonstrate that after 1990, ethnic migration decreased and irregular labour migration became more important

Composite slabs with expanded metal used as metal decking

The feasibility of the use of expanded metal as metal decking in reinforced concrete slabs was investigated experimentally. 8 specimens either flat or having different corrugation geometries were tested. Flat or corrugated geometry of the specimen, strong or weak axis of expanded metal were chosen to be the variables of the test program. It was observed that the material develops excellent bond:with concrete and cracking takes place at significantly high load levels. Finite element models were set up and nonlinear analyses were carried out. The analyses results were in good agreement with the experimental data

Partial Satisfaction Planning under Time Uncertainty with Control on When Objectives Can Be Aborted

In real world planning problems, it might not be possible for an automated agent to satisfy all the objectives assigned to it because available resources are limited. When objectives cannot all be satisfied, classical planning returns no plan. In partial satisfaction planning, it is possible to satisfy only a subset of the objectives. To solve this kind of problems, an agent could select the objectives subset and the plan that maximizes the net benefit, i.e. the sum of satisfied objectives utilities minus the sum of the cost of actions. This approach has been experimented for deterministic planning. This paper extends partial satisfaction planning for problems with uncertainty on time. For problems under uncertainty, the best subset of objectives may not be calculated at planning time. The effective duration of actions at execution time may dynamically influence the achievable subset of objectives. Our approach introduces special actions to explicitly abort objectives. This enables control on when an objective is aborted