Page 22 - Fister jr., Iztok, Andrej Brodnik, Matjaž Krnc and Iztok Fister (eds.). StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference. Koper: University of Primorska Press, 2019
P. 22
ce, since the checking of colliding movements can be 4. CONCLUSIONS
interrupted upon finding the first collision.
The paper gave an overview of the computational challenges
Computational experiments were performed on a set of 5000 in continuous collision detection for articulated industrial
continuous robot movements arising when building a PRM robots, and presented alternative approaches to tackling this
on the above work cell with 1000 random robot configura- challenge. An efficient implementation of the sampling and
tions and 5 neighbors per node. The average length of the the conservative advancement approaches was introduced,
robot movements was 45–50◦ in each robot joint. with various improvements compared to earlier algorithms in
the literature. In computational experiments, the proposed
Four different collision detection techniques were compared: sampling-based algorithm achieved a 23 times speedup com-
sampling in RoboDK and in the proposed implementation, pared to a similar algorithm of a commercial software, whereas
as well as CA in the proposed implementation with the orig- an improved displacement bound for conservative advance-
inal displacement bound of [6] and its improved version. ment resulted in a nearly three times speedup w.r.t. using
the earlier bound from the literature.
3.2 Experimental Results
The presented collision detection library is a key compo-
The computational results are displayed in Table 1, which nent of a process planning and path planning toolbox for
displays the key parameters, as well as the results achieved industrial robots under development. Future work will fo-
by the four algorithms. Both sampling-based approaches cus on the completion of the robotic path planning algo-
used a 1◦ sampling rate for the joint movements, without rithms, especially PRM and RRT, on top of the presented
giving a formal guarantee of the geometrical feasibility of the collision detection library. We plan to apply this library to
checked motions or maintaining a safety distance. With this process planning in various industrial applications, includ-
sampling rate, our implementation classified 2 out of 5000 ing a camera-based robotic pick-and-place work cell and the
colliding robot motions incorrectly as collision-free. A higher assembly of electric components. A research challenge is
number of mistakes by RoboDK probably stems from the dif- the handling of constraints and performance measurements
ferent geometrical models used.The efficient implementation defined in the Cartesian task space, such as linear motions
resulted in a 23 times speedup compared to RoboDK. or Cartesian speed limits, while planning in the robot joint
configuration space.
In contrast, the two CA implementations both provided a
guarantee of geometrical feasibility and could maintain a 5. ACKNOWLEDGMENTS
safety distance. At the same time, in order to facilitate
a comparison between CA and sampling, a safety distance This research has been supported by the ED 18-2-2018-0006
of 0 mm was used in the experiments. Moreover, allowing grant on “Research on prime exploitation of the potential
a relative tolerance of 3% in the PQP distance queries re- provided by the industrial digitalisation” and the GINOP-
sulted in a considerable speedup of the algorithm, without 2.3.2-15-2016-00002 grant on an “Industry 4.0 research and
any incorrect classifications on this test set. As a result, innovation center of excellence”. A. Kova´cs acknowledges
the two CA implementations returned correct and identi- the support of the Ja´nos Bolyai Research Fellowship.
cal classifications. The improved displacement upper bound
resulted in a 2.89 times speedup compared to the original 6. REFERENCES
upper bound, and computation times only 19% higher than
for sampling. We regard this as a favorable tradeoff for the [1] L. E. Kavraki, P. Svestka, J. C. Latombe, and M. H.
formal guarantee on the feasibility of the robot motions. Overmars. Probabilistic roadmaps for path planning in
high-dimensional configuration spaces. IEEE
Table 1: Experimental Results Transactions on Robotics and Automation,
12(4):566–580, 1996.
Sampling Sampling CA CA
(impr.) [2] E. Larsen, S. Gottschalk, M. C. Lin, and D. Manocha.
(RoboDK) (own) (orig.) Fast proximity queries with swept sphere volumes. In
- Proc. IEEE Int. Conf. Robot. Autom., pages 3719–3726,
Sampling 1◦ 1◦ - 0 mm 2000.
Safety dist. - - 0 mm 02:00 [3] S. M. Lavalle and J. J. Kuffner. Rapidly-exploring
random trees: Progress and prospects. In Algorithmic
Guarantee - - and Computational Robotics: New Directions, pages
293–308, 2000.
Time [mm:ss] 38:08 01:41 05:47
[4] J. Pan, S. Chitta, and D. Manocha. FCL: A general
purpose library for collision and proximity queries. In
IEEE International Conference on Robotics and
Automation, pages 3859–3866, 2012.
[5] RoboDK. Simulation and OLP for robots, 2019.
https://robodk.com/.
[6] F. Schwarzer, M. Saha, and J.-C. Latombe. Exact
Collision Checking of Robot Paths, pages 25–41.
Springer, 2004.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 22
Koper, Slovenia, 10 October
interrupted upon finding the first collision.
The paper gave an overview of the computational challenges
Computational experiments were performed on a set of 5000 in continuous collision detection for articulated industrial
continuous robot movements arising when building a PRM robots, and presented alternative approaches to tackling this
on the above work cell with 1000 random robot configura- challenge. An efficient implementation of the sampling and
tions and 5 neighbors per node. The average length of the the conservative advancement approaches was introduced,
robot movements was 45–50◦ in each robot joint. with various improvements compared to earlier algorithms in
the literature. In computational experiments, the proposed
Four different collision detection techniques were compared: sampling-based algorithm achieved a 23 times speedup com-
sampling in RoboDK and in the proposed implementation, pared to a similar algorithm of a commercial software, whereas
as well as CA in the proposed implementation with the orig- an improved displacement bound for conservative advance-
inal displacement bound of [6] and its improved version. ment resulted in a nearly three times speedup w.r.t. using
the earlier bound from the literature.
3.2 Experimental Results
The presented collision detection library is a key compo-
The computational results are displayed in Table 1, which nent of a process planning and path planning toolbox for
displays the key parameters, as well as the results achieved industrial robots under development. Future work will fo-
by the four algorithms. Both sampling-based approaches cus on the completion of the robotic path planning algo-
used a 1◦ sampling rate for the joint movements, without rithms, especially PRM and RRT, on top of the presented
giving a formal guarantee of the geometrical feasibility of the collision detection library. We plan to apply this library to
checked motions or maintaining a safety distance. With this process planning in various industrial applications, includ-
sampling rate, our implementation classified 2 out of 5000 ing a camera-based robotic pick-and-place work cell and the
colliding robot motions incorrectly as collision-free. A higher assembly of electric components. A research challenge is
number of mistakes by RoboDK probably stems from the dif- the handling of constraints and performance measurements
ferent geometrical models used.The efficient implementation defined in the Cartesian task space, such as linear motions
resulted in a 23 times speedup compared to RoboDK. or Cartesian speed limits, while planning in the robot joint
configuration space.
In contrast, the two CA implementations both provided a
guarantee of geometrical feasibility and could maintain a 5. ACKNOWLEDGMENTS
safety distance. At the same time, in order to facilitate
a comparison between CA and sampling, a safety distance This research has been supported by the ED 18-2-2018-0006
of 0 mm was used in the experiments. Moreover, allowing grant on “Research on prime exploitation of the potential
a relative tolerance of 3% in the PQP distance queries re- provided by the industrial digitalisation” and the GINOP-
sulted in a considerable speedup of the algorithm, without 2.3.2-15-2016-00002 grant on an “Industry 4.0 research and
any incorrect classifications on this test set. As a result, innovation center of excellence”. A. Kova´cs acknowledges
the two CA implementations returned correct and identi- the support of the Ja´nos Bolyai Research Fellowship.
cal classifications. The improved displacement upper bound
resulted in a 2.89 times speedup compared to the original 6. REFERENCES
upper bound, and computation times only 19% higher than
for sampling. We regard this as a favorable tradeoff for the [1] L. E. Kavraki, P. Svestka, J. C. Latombe, and M. H.
formal guarantee on the feasibility of the robot motions. Overmars. Probabilistic roadmaps for path planning in
high-dimensional configuration spaces. IEEE
Table 1: Experimental Results Transactions on Robotics and Automation,
12(4):566–580, 1996.
Sampling Sampling CA CA
(impr.) [2] E. Larsen, S. Gottschalk, M. C. Lin, and D. Manocha.
(RoboDK) (own) (orig.) Fast proximity queries with swept sphere volumes. In
- Proc. IEEE Int. Conf. Robot. Autom., pages 3719–3726,
Sampling 1◦ 1◦ - 0 mm 2000.
Safety dist. - - 0 mm 02:00 [3] S. M. Lavalle and J. J. Kuffner. Rapidly-exploring
random trees: Progress and prospects. In Algorithmic
Guarantee - - and Computational Robotics: New Directions, pages
293–308, 2000.
Time [mm:ss] 38:08 01:41 05:47
[4] J. Pan, S. Chitta, and D. Manocha. FCL: A general
purpose library for collision and proximity queries. In
IEEE International Conference on Robotics and
Automation, pages 3859–3866, 2012.
[5] RoboDK. Simulation and OLP for robots, 2019.
https://robodk.com/.
[6] F. Schwarzer, M. Saha, and J.-C. Latombe. Exact
Collision Checking of Robot Paths, pages 25–41.
Springer, 2004.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 22
Koper, Slovenia, 10 October
