trajectory-optimized-active-search

The code folder contains two folders; discrete_probing and continuous_probing.

discrete_probing

run the demo.m file for generating stiffness map using all the algorithms discussed the paper.
run the incremental_demo.m file for generating stiffness map using all the algorithms discussed the paper in an incremental fashion (you can see the points being probed sequentially). This code also saves all the results at after each probe in an automatically generated results folder.
run the recall_plot_randomized_groundTruths.m and recall_plot_same_groundTruth.m files to generate the recall plot of Fig. 4.

estimating stiffness map using sampling-based trajectory planning to generate continuous palapation paths.
replicating the results of Fig.5 in the paper.

run the test.m file for generating stiffness map using any of the algorithms discussed the paper (you can change which algorithm you want to test inside the code)
run the demo.m file for generating stiffness map using all the algorithms discussed the paper. This code also saves all the results at after each probe in an automatically generated results folder.
run the recall_plot_randomized_groundTruths.m and recall_plot_same_groundTruth.m files to generate the recall plot of Fig. 8.

Code for the ICRA2018 paper "Trajectory-Optimized Sensing for Active Search of Tissue Abnormalities in Robotic Surgery"

MIT License

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