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A simple robotics framework.

Adapting TED-A* to AGGLPlanner

In TED-A* we’ve operator chunks consisting of several operators and each chunk has it’s own run time. TED-A* consists of many cycles, in each cycle A* search is performed. Each cycle takes different amount of operator chunks into consideration. New operator chunks are introduced every cycle. In a cycle, A* is performed using an operator chunk for time equal to its run time.

Let’s say in a cycle we’ve:

operator_chunk1, operator_chunk2, .., operator_chunkn

having corresponding run-times:

run_time1, run_time2, .., run_timen

In TED-A* algorithm we’ll perform A* search using an operator_chunki for it’s run_timei. We’ll do it for all operator chunks under consideration in the current cycle.

Three important things to note are: i) In TED-A, we assume all operator chunks to be disjoint. ii) Number of *operator chunk under consideration will be superset of it’s preceeding cycles. iii) When no longer operator chunk can be considered, a cycle will saturate. Every cycle after that will contain all possible operator chunk and each chunk will be run for it’s corresponding run time.

When introducing TED-A* to the current implementation of AGGLPlanner, we tweak the proposed TED-A* algorithm a bit for the ease of implementation. Now for every cycle we’ll have only one operator chunk (which is union of all operator chunks for a cycle in TED-A). Also now, instead of *operator chunk, every cycle will have a time chunk.

The only difference is the time allocation pattern to each operator chunk.

Lashit Jain