# Sensor-Guided Assembly of Segmented Structures with Industrial Robots
## 2 Related Work  
In assembly manufacturing, fixtures are vastly used in the joining process to hold, position, and support parts at designated locations. The performance of fixtures usually dictates the overall manufacturing result. Therefore, 10-20% of the total manufacturing costs are usually invested in engineering the fixture systems [8]. To reduce the cost and the required knowledge to configure the fixture system, many research works are devoted to computer-aided fixture design [9; 10]. A new trend of active/intelligent fixtures integrating sensors and actuators in the fixture system has gained attention by actively changing the clamping force or position for different parts [11]. There are also efforts developing reconfigurable fixtures [12] or embedding fixtures directly into the joined parts [13], admitting the high-cost and inflexibility of using hardware fixtures [14].  
Ever since [PERSON] introduce the term RFA in 1988 [15], the concept of replacing physical fixtures by sensor-driven robots has emerged. The new technical challenges may arise at the grasp planning strategy [16], gripper design [17; 18], and the sensing system. Neural networks are widely used to estimate the object pose from the 2D vision system for grasping when fixture is not available [19; 20; 21]. [PERSON] also used neural network technique to teach a robot to manipulate parts based on both vision and force feedback in [22]. [PERSON] experimentally presented a small scale aero-fuselage assembly workcell using non-contact laser sensing for part deformation and misalignment [23]. [PERSON] presented a similar workcell with two robots assembling a strut and a panel using an external laser measurement system to achieve high accuracy alignment [24]. [PERSON] proposed an interesting strategy for intuitive peg-in-hole assembly using solely compliance control [25]. [PERSON] presented a dual-arm robot assembling a ball pen using visual information in [26].  
In this paper, we tackle the task of assembling full-scale composite blade panels, which tend to vary in size, shape, and weight. The flexible panels may also trammel the fixture design to prevent vibration or over-clamping [11]. Instead of fabricating special fixtures to accommodate different parts, we adopt RFA using both vision and force guidance to handle the challenges without the positioning and supporting from fixtures.  
Since we use external guided motion rather than the inverse kinematics method, collision avoidance can also be achieved. We further address the issue of moving the large object safely and reliably in a tight workcell without colliding with the wall or swinging the panel upside down. It is not preferred to use the Open Motion Planning Library (OMPL) [27], providing sampling-based planners in ROS directly, due to the random nature and undesirable paths that can possibly lead to danger. Optimization-based planners, such as CHOMP [28], STOMP [29], and TrajOpt [30], have received much attention in recent years due to their relatively simple solution to problems with higher degrees of freedom (DOF) in a narrow space. Since the generated path is optimized over the initial ones, the result is more consistent and reliable in every iteration. This type of planners are also gaining popularity and featured in the next generation MoveIt2.  
While these planners focus on the global optimization, we propose a locally optimized planner based on our previous safe teleoperation controller in [31]. It generates a feasible path through sparse waypoints given from human knowledge, and optimizes them in every stepping iteration. By solving the quadratic programming problem with inequality constraints of joint limits and collision distance, the resulting path can follow the human guided path while avoiding collision and singularities.