随着不同构型和类别机器人的出现，迫切需要建立一套一致的优化框架用于高效、准确地解决异构多类冗余度机器人手臂的运动学解析问题。本项目致力于开发能够达到此目的的规划策略。首先，根据不同结构和类型的冗余度机器人手臂按照需执行的任务，确定其特定的优化策略。接着，将这些不同层、不同优化目标的策略一致化为一个典型的二次型规划问题。然后，研究与开发递归神经网络或数值算法求解二次型规划问题。最后，使用物理机械臂进行实验验证。具体研究工作为（1）研究固定基座冗余度机器人手臂的加速度层容错策略、加速度层三判据策略等。（2）研究移动冗余度机械臂的协调优化策略，可充分考虑稳定性和可操作性等性能指标。（3）提出并研究多冗余度机械臂的解析策略，比如如何协调完成共同的任务；（4）研究类人机器人左右手臂像人一样运动的策略。

An effective, accurate and unified optimization strategy is proposed to handle the redundancy resolution problem of various redundant manipulators with different structures. Firstly, the optimization strategies are determined according to different tasks performed by these various redundant robot arms. Secondly, All the strategies with different levels and different criteria are unified into a classic quadratic programming (QP) formulation. Thirdly, the recurrent neural networks or some numerical algorithms are exploited to solve the resulted QP problem. Finally, the strategies are performed to the practical robots to verify the effectiveness of the proposed strategy. The specific research work are as follows. (1) Tri-criteria schemes and acceleration-level fault-tolerant schemes are proposed. (2) stability and manipuility can be considered into the coordinate strategy of the mobile redundant robot arms. (3) The redundancy resolution schemes of multiple redundant robot arms are proposed, which should collaborate to finish the tasks. (4) The dual-arms human-like coordinate scheme of humanoid robots.