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Advanced Perception, Navigation and Planning for Autonomous InWater Ship Hull Inspection
"... Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the aco ..."
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Cited by 16 (13 self)
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Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closedloop control relative to features such as weldlines and biofouling. In the complex area, we implemented new largescale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships. 1
Minimum Time Point Assignment for Coverage by Two Constrained Robots
"... Abstract — This paper focuses on the assignment of discrete points to two robots, in the presence of geometric and kinematic constraints between the robots. The individual points have differing processing times, and the goal is to identify an assignment of points to the robots so that the total proc ..."
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Abstract — This paper focuses on the assignment of discrete points to two robots, in the presence of geometric and kinematic constraints between the robots. The individual points have differing processing times, and the goal is to identify an assignment of points to the robots so that the total processing time is minimized. The assignment of points to the robots is the first step in the path generation process for the robots. This work is motivated by an industrial microelectronics manufacturing system with two robots, with square footprints, that are constrained to translate along a common line while satisfying proximity and collision avoidance constraints. The N points lie on a planar base plate that can translate along the plane normal to the direction of motion of the robots. The geometric constraints on the motions of the two robots lead to constraints on points that can be processed simultaneously. We show that the point assignment for processing problem can be converted to a maximum weighted matching problem on a graph and solved optimally in O(N 3) time. Since this is too slow for large datasets, we present a O(N 2) time greedy algorithm and prove that the greedy solution is within a factor of 3/2 of the optimal solution. Finally, we provide computational results for the greedy algorithm on typical industrial datasets. I.
Coverage of a Planar Point Set with Multiple Constrained Robots
, 2007
"... An important problem that arises in many applications is: Given k robots with known processing footprint to process a set of N points in the plane, find trajectories for each robot satisfying the geometric, kinematic, and dynamic constraints such that the time required to cover the points (processi ..."
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Cited by 2 (2 self)
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An important problem that arises in many applications is: Given k robots with known processing footprint to process a set of N points in the plane, find trajectories for each robot satisfying the geometric, kinematic, and dynamic constraints such that the time required to cover the points (processing time plus travel time) is minimized. This problem is a hybrid discretecontinuous optimization problem and is hard to solve optimally even for k = 1. One approach is to treat this as a two stage problem where the first stage is to find the best possible path satisfying the geometric constraints and then convert it into a trajectory satisfying the differential constraints. In this paper, we consider an industrial microelectronics manufacturing system of k( = 2) robots, with square footprints, that are constrained to translate along a line while satisfying proximity constraints. The points lie on a planar base plate that can translate along the plane normal to the direction of motion of the robots. We solve the geometric problem of path generation for the robots using a two step approach that yields a suboptimal solution: 1) Minimize the number of k−tuples subject to geometric constraints. 2) Solve a Traveling Salesman Problem (TSP) in the k−tuple space with an appropriately defined metric to minimize the total travel cost. We show that for k = 2, step 1 can be converted to a maximum cardinality matching problem on a graph and solved optimally in polynomial time. The matching algorithm takes O(N 3) time in general and is too slow for large datasets. Therefore, we also provide a greedy algorithm for step 1 that takes O(N log N) time. We provide computational results comparing the two approaches and show that the greedy algorithm is very close to the optimal solution for large datasets. We also provide local search based heuristics to improve the TSP tour in the pair space and give preliminary implementation results showing an improvement of 1 % to 2 % in the resultant tour.
MultiGoal Feasible Path Planning Using Ant Colony Optimization
"... Abstract — A new algorithm for solving multigoal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its wellknown application, the traveling salesman problem (TSP), to that of multigoal feasible path planning for inspection and surveillance ..."
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Abstract — A new algorithm for solving multigoal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its wellknown application, the traveling salesman problem (TSP), to that of multigoal feasible path planning for inspection and surveillance applications. Specifically, the ant colony framework is combined with a samplingbased pointtopoint planning algorithm; this is compared with two successful samplingbased multigoal planning algorithms in an obstaclefilled twodimensional environment. Total mission time, a function of computational cost and the duration of the planned mission, is used as a basis for comparison. In our application of interest, autonomous undewater inspections, the ACO algorithm is found to be the bestequipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality. I.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1 Coverage of a Planar Point Set With Multiple Robots Subject to Geometric Constraints
"... Abstract—This paper focuses on the assignment of discrete points among geometrically constrained robots and determination of the order in which the points should be processed by the robots. This path planning problem is directly motivated by an industrial laser drilling system with two robots that a ..."
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Abstract—This paper focuses on the assignment of discrete points among geometrically constrained robots and determination of the order in which the points should be processed by the robots. This path planning problem is directly motivated by an industrial laser drilling system with two robots that are constrained to translate along a common line while satisfying collision avoidance constraints. The points lie on a planar base plate that translates normal to the axis of motion of the robots. The geometric constraints on the motions of the robots lead to constraints on points that can be processed simultaneously. We use a two step approach to solve the path planning problem: 1) Splitting Problem: Assign the points to the robots, subject to geometric constraints, to maximize parallel processing of the points. 2) Ordering Problem: Find an order of processing the split points by formulating and solving a multidimensional
PlanningCurvatureConstrainedPathstoMultipleGoals
"... Abstract—We present a new samplingbased method for planning optimal, collisionfree, curvatureconstrained paths fornonholonomicrobotstovisitmultiplegoalsinanyorder. Ratherthansamplingconfigurationsasinstandardsamplingbasedplanners,weconstructaroadmapbysamplingcircles ofconstantcurvatureandthengene ..."
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Abstract—We present a new samplingbased method for planning optimal, collisionfree, curvatureconstrained paths fornonholonomicrobotstovisitmultiplegoalsinanyorder. Ratherthansamplingconfigurationsasinstandardsamplingbasedplanners,weconstructaroadmapbysamplingcircles ofconstantcurvatureandthengeneratingfeasibletransitions betweenthesampledcircles.Weprovideaclosedformformula for connecting the sampled circles in 2Dand generalize the approach to 3D workspaces. We then formulate the multigoalplanningproblemasfindingaminimumdirectedSteiner treeovertheroadmap.Sinceoptimallysolvingthemultigoal planningproblemrequiresexponentialtime,weproposegreedy heuristicstoefficientlycomputeapaththatvisitsmultiplegoals. Weapplytheplannerinthecontextofmedicalneedlesteering wheretheneedletipmustreachmultiplegoalsinsofttissue, acommonrequirementforclinicalproceduressuchasbiopsies,drugdelivery,andbrachytherapycancertreatment.We demonstratethatourmultigoalplannersignificantlydecreases tissuethatmustbecutwhencomparedtosequentialexecution ofsinglegoalplans.
Thesis Abstract: Pathplanning for Industrial Robots Among Multiple Underspecified Tasks
"... Nowadays industrialized countries with their high labor costs have to rely on production automation to keep their competitive advantage. One of the most flexible and powerful automation technology available today is industrial ..."
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Nowadays industrialized countries with their high labor costs have to rely on production automation to keep their competitive advantage. One of the most flexible and powerful automation technology available today is industrial
unknown title
, 2009
"... Timeoptimal task scheduling for two robotic manipulators operating in a threedimensional environment ..."
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Timeoptimal task scheduling for two robotic manipulators operating in a threedimensional environment
Structures
, 2012
"... Path planning is an essential capability for autonomous robots, and many applications impose challenging constraints alongside the standard requirement of obstacle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known worksp ..."
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Path planning is an essential capability for autonomous robots, and many applications impose challenging constraints alongside the standard requirement of obstacle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For twodimensional environments, optimal algorithms are documented and wellunderstood. For threedimensional structures, however, few of the available heuristics succeed over occluded regions and lowclearance areas. This thesis makes several contributions to samplingbased coverage path planning, for use on complex threedimensional structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry than the wellknown dual sampling method, especially when highquality solutions are desired. Second, we present an improvement procedure that iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage constraints. Third, we propose a modular algorithm that allows the simple components