Pregled bibliografske jedinice broj: 1171281
Convex Iteration for Distance-Geometric Inverse Kinematics
Convex Iteration for Distance-Geometric Inverse Kinematics // IEEE Robotics and Automation Letters, 1 (2022), 1-2 doi:10.1109/lra.2022.3141763 (međunarodna recenzija, članak, ostalo)
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Naslov
Convex Iteration for Distance-Geometric Inverse Kinematics
Autori
Giamou, Matthew ; Maric, Filip ; Rosen, David ; Peretroukhin, Valentin ; Roy, Nicholas ; Petrovic, Ivan ; Kelly, Jonathan
Izvornik
IEEE Robotics and Automation Letters (2377-3766) 1
(2022);
1-2
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo
Ključne riječi
Kinematics ; optimization and optimal control ; manipulation planning
Sažetak
Inverse kinematics (IK) is the problem of finding robot joint configurations that satisfy constraints on the position or pose of one or more end-effectors. For robots with redundant degrees of freedom, there is often an infinite, nonconvex set of solutions. The IK problem is further complicated when collision avoidance constraints are imposed by obstacles in the workspace. In general, closed-form expressions yielding feasible configurations do not exist, motivating the use of numerical solution methods. However, these approaches rely on local optimization of nonconvex problems, often requiring an accurate initialization or numerous re-initializations to converge to a valid solution. In this work, we first formulate inverse kinematics with complex workspace constraints as a convex feasibility problem whose low-rank feasible points provide exact IK solutions. We then present \texttt{;CIDGIK}; (Convex Iteration for Distance-Geometric Inverse Kinematics), an algorithm that solves this feasibility problem with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. Our problem formulation elegantly unifies the configuration space and workspace constraints of a robot: intrinsic robot geometry and obstacle avoidance are both expressed as simple linear matrix equations and inequalities. Our experimental results for a variety of popular manipulator models demonstrate faster and more accurate convergence than a conventional nonlinear optimization-based approach, especially in environments with many obstacles.
Izvorni jezik
Engleski
Znanstvena područja
Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus