RoboTSP - A Fast Solution to the Robotic Task Sequencing Problem

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.Comment: 6 pages, 7 figures, 1 tabl

Expressivity in Natural and Artificial Systems

Roboticists are trying to replicate animal behavior in artificial systems. Yet, quantitative bounds on capacity of a moving platform (natural or artificial) to express information in the environment are not known. This paper presents a measure for the capacity of motion complexity -- the expressivity -- of articulated platforms (both natural and artificial) and shows that this measure is stagnant and unexpectedly limited in extant robotic systems. This analysis indicates trends in increasing capacity in both internal and external complexity for natural systems while artificial, robotic systems have increased significantly in the capacity of computational (internal) states but remained more or less constant in mechanical (external) state capacity. This work presents a way to analyze trends in animal behavior and shows that robots are not capable of the same multi-faceted behavior in rich, dynamic environments as natural systems.Comment: Rejected from Nature, after review and appeal, July 4, 2018 (submitted May 11, 2018

Performance study of single-query motion planning for grasp execution using various manipulators

This paper identifies high performing motion planners among three manipulators when carrying out grasp executions. Simultaneously, this paper presents useful benchmarking data. Sampling-based motion planners of OMPL available for use in MoveIt! are compared by performing several grasping-related motion planning problems. The performance of the planners is measured by means of solved runs, computing time and path length. Based on the results, recommendations are made for planner choice that shows high performance for the used manipulators

Performance study of single-query motion planning for grasp execution using various manipulators

This paper identifies high performing motion planners among three manipulators when carrying out grasp executions. Simultaneously, this paper presents useful benchmarking data. Sampling-based motion planners of OMPL available for use in MoveIt! are compared by performing several grasping-related motion planning problems. The performance of the planners is measured by means of solved runs, computing time and path length. Based on the results, recommendations are made for planner choice that shows high performance for the used manipulators.Accepted Author ManuscriptRobot Dynamic

WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles

Rapid advances in every sphere of autonomous driving technology have intensified the need to be able to benchmark and compare different approaches. While many benchmarking tools tailored to different sub-systems of an autonomous vehicle, such as perception, already exist, certain aspects of autonomous driving still lack the necessary depth and diversity of coverage in suitable benchmarking approaches - autonomous vehicle motion planning is one such aspect. While motion planning benchmarking tools are abundant in the robotics community in general, they largely tend to lack the specificity and scope required to rigorously compare algorithms that are tailored to the autonomous vehicle domain. Furthermore, approaches that are targeted at autonomous vehicle motion planning are generally either not sensitive enough to distinguish subtle differences between different approaches, or not able to scale across problems and operational design domains of varying complexity. This work aims to address these issues by proposing WiseBench, an autonomous vehicle motion planning benchmark framework aimed at comprehensively uncovering fine and coarse-grained differences in motion planners across a wide range of operational design domains. WiseBench outlines a robust set of requirements for a suitable autonomous vehicle motion planner. These include simulation requirements that determine the environmental representation and physics models used by the simulator, scenario-suite requirements that govern the type and complexity of interactions with the environment and other traffic agents, and comparison metrics requirements that are geared towards distinguishing the behavioral capabilities and decision making processes of different motion planners. WiseBench is implemented using a carefully crafted set of scenarios and robust comparison metrics that operate within an in-house simulation environment, all of which satisfy these requirements. The benchmark proved to be successful in comparing and contrasting two different autonomous vehicle motion planners, and was shown to be an effective measure of passenger comfort and safety in a real-life experiment. The main contributions of our work on WiseBench thus include: a scenario creation methodology for the representative scenario suite, a comparison methodology to evaluate different motion planning algorithms, and a proof-of-concept implementation of the WiseBench framework as a whole