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 and service robots gaining traction, different robotic systems have to work in close proximity. This means that the current inverse kinematics approaches have to not only 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 position and/or orientation 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 detected collisions at all 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广泛研究，但在寻找兼顾碰撞感知的光顺可行解时仍面临挑战。此外，随着协作机器人与服务机器人的普及，不同机器人系统需在近距离协同工作，这意味着现有逆运动学方法不仅要避免机械臂自身碰撞，还需规避与其他机械臂的碰撞。为此，我们提出一种新型串联机械臂逆运动学计算方法，该方法能在满足多种约束条件的同时，求解末端执行器期望位姿且避免与自身及其他机械臂发生碰撞。与现有基于约束的方法不同，本方法无需执行昂贵的逆雅可比计算，也不要求机械臂具备冗余自由度。我们将不同约束建模为加权代价函数，通过非线性优化求解器进行优化。在狭窄空间多臂共存场景下的碰撞避免方面，本方法在所有实验场景中均未检测到任何碰撞，性能优于当前最先进的CollisionIK方法。当碰撞概率较低时，本方法在轨迹跟踪方面同样表现出更优性能。此外，本方法可在无碰撞前提下，对相交工作空间内多个机械臂实现同步去中心化轨迹跟踪控制。