  A vision system attached to a manipulator excels at tracing a moving target
object while effectively handling obstacles, overcoming limitations arising
from the camera's confined field of view and occluded line of sight. Meanwhile,
the manipulator may encounter certain challenges, including restricted motion
due to kinematic constraints and the risk of colliding with external obstacles.
These challenges are typically addressed by assigning multiple task objectives
to the manipulator. However, doing so can cause an increased risk of driving
the manipulator to its kinematic limits, leading to failures in object tracking
or obstacle avoidance. To address this issue, we propose a novel visual
tracking control method for a redundant manipulator that takes the kinematic
constraints into account via a reachability measure. Our method employs an
optimization-based controller that considers object tracking, occlusion
avoidance, collision avoidance, and the kinematic constraints represented by
the reachability measure. Subsequently, it determines a suitable joint
configuration through real-time inverse kinematics, accounting for dynamic
obstacle avoidance and the continuity of joint configurations. To validate our
approach, we conducted simulations and hardware experiments involving a moving
target and dynamic obstacles. The results of our evaluations highlight the
significance of incorporating the reachability measure.
