Although inverse kinematics of serial manipulators is a well studied problem, challenges still exist in finding smooth feasible solutions that are also collision aware. Furthermore, with collaborative service robots gaining traction, different robotic systems have to work in close proximity. This means that the current inverse kinematics approaches do not have only to avoid collisions with themselves but also collisions with other robot arms. Therefore, we present a novel approach to compute inverse kinematics for serial manipulators that take into account different constraints while trying to reach a desired end-effector pose that avoids collisions with themselves and other arms. Unlike other constraint based approaches, we neither perform expensive inverse Jacobian computations nor do we require arms with redundant degrees of freedom. Instead, we formulate different constraints as weighted cost functions to be optimized by a non-linear optimization solver. Our approach is superior to the state-of-the-art CollisionIK in terms of collision avoidance in the presence of multiple arms in confined spaces with no collisions occurring in all the experimental scenarios. When the probability of collision is low, our approach shows better performance at trajectory tracking as well. Additionally, our approach is capable of simultaneous yet decentralized control of multiple arms for trajectory tracking in intersecting workspace without any collisions.

翻译：尽管串联机械臂的逆运动学问题已得到充分研究，但在寻找同时满足碰撞感知的光滑可行解方面仍存在挑战。此外，随着协作服务机器人的普及，不同机器人系统需在紧密相邻的空间中协同工作，这意味着现有逆运动学方法不仅要避免自碰撞，还需防止与其他机械臂发生碰撞。为此，我们提出一种新型串联机械臂逆运动学计算方法，该方法在满足多重约束的同时，能使末端执行器达到期望位姿，并避免自身及与其他机械臂的碰撞。与现有基于约束的方法不同，本方法既无需昂贵的逆雅可比矩阵计算，也不要求机械臂具有冗余自由度。我们将不同约束表述为加权代价函数，通过非线性优化求解器进行优化。在受限空间的多臂场景中，本方法在碰撞规避方面优于当前最先进的CollisionIK方法，所有实验场景均未发生碰撞。在碰撞概率较低时，本方法在轨迹跟踪方面也展现出更优性能。此外，本方法支持对多机械臂在相交工作空间内进行无碰撞轨迹跟踪的同时去中心化控制。