Path Constrained Trajectory Planning
Background:
A tool needs to follow a path defined on the surface of a part to carry out this operation. The relative motion among the objects to carry out certain tasks can be defined as path-constraints. Robotic finishing, painting, inspection, fiber placement, etc are some of the operations where relative motion of objects can be encoded as path-constrains. The constrains include but are not limited to:
- General end-effector constraints
- Avoiding collision and singularities
- Maintaining desired velocity and force at the tool-tip
- Complying to the joint limits.
These path-constraints or the motion of the objects can be generated using traditional motion planners.
However, if we want to automate these tasks using robots, then we need to generate configuration space trajectories for robots in addition to generating the motion plans for the objects. The problem of generating such trajectories is known as path constraint trajectory generation problem. Traditionally, a version of the problem which imposes several path constraints is solved using graph search techniques. However, there are limitations of the approach such as discontinuities cannot be handled by the planner, methods to define path consistency are not included, and large problems cannot be solved.
Our laboratory’s focus:
We are working on an approach where a reduced graph can be successively constructed by using fundamental rules which prune nodes and edges in the graph.
Mobile Manipulator Trajectory Planning
Background:
Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. The task of object transportation has several sub-tasks to it, namely; moving towards the object to be transported, picking-up the object, carrying the object and finally depositing it.
Our laboratory’s focus:
Our work focuses on the first three parts of this task, from the point of view of efficient mobile base motion planning, followed by efficient manipulator planning for grasping and finally planning for gripper motion for efficient grasping. Moreover, we also focus on motion planning for carrying large objects with a mobile manipulator in cluttered environments. For motion planning of the mobile base, we use systematic search with motion primitives with specialized heuristics so that the mobile manipulator goes from the starting configuration to goal configuration while picking-up the object in an resolution-optimal manner. This work is followed by motion planning for the manipulator on a moving mobile base in cluttered environments for picking-up objects. Here, we use sampling-based algorithms with specialized techniques to accelerate the motion planning process. Thereafter, in the next work we move the gripper at a speed to grasp objects while moving. The gripper speed needs to be selected to ensure that the pick-up operation does not fail due to uncertainties in part pose estimation. This, in turn, affects the mobile base trajectory.
For this we have developed an active learning based approach to construct a meta-model to estimate the probability of successful part pick-up for a given level of uncertainty in the part pose estimate. Using this model, we develop an optimization-based framework to generate time-optimal trajectories that satisfy the given level of success probability threshold for picking-up the part.
Robot and Part Placement Planning
Background:
The workspace of a serial manipulator is limited. Additionally, complexities exist due to the joint limit and self-collision-avoidance constraints. The robot workspace includes singularities, and it is generally preferred for a robot to carry out tasks away from singularities. Different locations in the robot workspace impose different constraints on achievable forces and velocities. Variations in achievable forces and velocities can be quite large. Moreover, there might not exist a continuous path between two points in the robot workspace that are far apart. Workspace characteristics can be quite complex for redundant manipulators, and unfortunately, there is no simple model to capture these. Many complex manufacturing tasks such as welding, robotic finishing, composite layup, painting, 3D printing, etc. use manipulators to follow continuous paths under constraints on the workpiece. It is important to find the right location and orientation for the workpiece with respect to the robot to meet all the task constraints. This problem is called a workpiece placement problem.
Our laboratory’s focus:
We formulate the problem of identifying a feasible placement as a non-linear optimization problem over the constraint violation functions. This is a computationally challenging problem. Our approach includes successively searching for the solution by incrementally applying different constraints.