Research Papers

Optimal Paths for Polygonal Robots in SE(2)

[+] Author and Article Information
Monroe Kennedy, III

GRASP Laboratory,
MEAM Department,
University of Pennsylvania,
Philadelphia, PA 19104
e-mail: kmonroe@seas.upenn.edu

Dinesh Thakur

GRASP Laboratory,
MEAM Department,
University of Pennsylvania,
Philadelphia, PA 19104
e-mail: tdinesh@seas.upenn.edu

M. Ani Hsieh

GRASP Laboratory,
MEAM Department,
University of Pennsylvania,
Philadelphia, PA 19104
e-mail: m.hsieh@seas.upenn.edu

Subhrajit Bhattacharya

CSE Department,
Lehigh University,
Bethlehem, PA 18015
e-mail: sub216@lehigh.edu

Vijay Kumar

GRASP Laboratory,
MEAM Department,
University of Pennsylvania,
Philadelphia, PA 19104
e-mail: kumar@seas.upenn.edu

1Corresponding author.

Manuscript received September 22, 2017; final manuscript received December 14, 2017; published online February 1, 2018. Assoc. Editor: Andrew P. Murray.

J. Mechanisms Robotics 10(2), 021005 (Feb 01, 2018) (8 pages) Paper No: JMR-17-1313; doi: 10.1115/1.4038980 History: Received September 22, 2017; Revised December 14, 2017

We consider planar navigation for a polygonal, holonomic robot in an obstacle-filled environment in SE(2). To determine the free space, we first represent obstacles as point clouds in the robot configuration space (C). A point-wise Minkowski sum of the robot and obstacle points is then calculated in C using obstacle points and robot convex hull points for varying robot configurations. Using graph search, we obtain a seed path, which is used in our novel method to compute overlapping convex regions for consecutive seed path chords. The resulting regions provide collision-free space useful for finding feasible trajectories that optimize a specified cost functional. The key contribution is the proposed method's ability to easily generate a set of convex, overlapping polytopes that effectively represent the traversable free space. This, in turn, lends itself to (a) efficient computation of optimal paths in 3 and (b) extending these basic ideas to the special Euclidean space SE(2). We provide simulated examples and implement this algorithm on a KUKA youBot omnidirectional base.

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Grahic Jump Location
Fig. 3

Convex corridor generation: obstacle point ϕ is distance r1, r2 from planes that define seed path chord segment (pi, pi+1) and ϕv=ϕ−pi defines ϕv,⊥ (constraint plane normal). Figures 3(b) and 3(c) show 2D overlapping regions between consecutive chords.

Grahic Jump Location
Fig. 2

Minkowski pointwise sum for robot centroid in C with convex hull shown in ℝ2 and ℝ3. The triangles represent the initial and goal pose of a polygonal robot, the stacked polytopes represent the Minkowski sum in different robot orientations (here we show three orientations θ0, θ120, θ240). This provides a representation of the SE(2) free configuration space in ℝ3.

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Fig. 1

Minkowski sum and pointwise representation. With a dense point cloud, the pointwise sum can be used to approximate the Minkowski sum, which is indicated by the dashed line. This dashed line shows how close the robot centroid can approach the obstacle without collision.

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Fig. 4

Simulated trajectory: rectangle is robot hull, orientation is shown with arrows and height. Sparse connected arrows and densly connected arrows are seed path and optimal path, respectively.

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Fig. 5

Alternate map with same representation as Fig. 4

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Fig. 6

Simulated examples demonstrating robustness in tight corridor scenarios: (a) map 1 and (b) map 2

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Fig. 7

Implementation on youBot with known obstacles equivalent to Fig. 6(a)

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Fig. 8

Smooth trajectory of Fig. 6(a). Points show the seed path, dashed lines are the desired trajectory and solid lines represent the actual odometry. (a) X position versus normed time, (b) Y position versus normed time, and (c) θ position versus normed time.

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Fig. 9

General method comparison of free space: single chord indicated by arrow, 30 generated obstacles and free space characterized by each method: (a) IRIS SDP method, (b) proposed method, and (c) Liu method



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