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有深入研究，但在寻找兼顾碰撞感知的光滑可行解方面仍存在挑战。此外，随着协作机器人和服务机器人的普及，不同机器人系统需在近邻环境下协同工作，这意味着现有逆运动学方法不仅要避免机械臂与自身的碰撞，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求解方法，该方法在达到期望末端执行器位姿的同时，能够考虑多重约束并避免机械臂自身及多臂间碰撞。与现有基于约束的方法不同，本方法无需进行昂贵的逆雅可比计算，也不要求机械臂具有冗余自由度，而是将不同约束构建为加权代价函数，通过非线性优化求解器进行优化。在密闭空间多臂协同实验中，本方法在全场景下实现零碰撞检测，其避碰性能显著优于当前最先进的CollisionIK方法；在低碰撞概率场景中，本方法的轨迹跟踪表现亦更优。此外，本方法可对位于交叉工作空间的多机械臂实现去中心化同步控制，在零碰撞条件下完成轨迹跟踪任务。