RoboTSP – A Fast Solution to the Robotic Task Sequencing Problem

Abstract

In many industrial robotics applications, such as spot-welding, spray-painting or drilling, the robot is required to visit successively multiple targets. The robot travel time among the targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the order of visit of the targets, and by the robot configurations used to reach each target. Therefore, it is crucial to optimize these two elements, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). Our contribution in this paper is two-fold. First, we propose a fast, near-optimal, algorithm to solve RTSP. The key to our approach is to exploit the classical distinction between task space and configuration space, which, surprisingly, has been so far overlooked in the RTSP literature. Second, we provide an open-source implementation of the above algorithm, which has been carefully benchmarked to yield an efficient, ready-to-use, software solution. We discuss the relationship between RTSP and other Traveling Salesman Problem (TSP) variants, such as the Generalized Traveling Salesman Problem (GTSP), and show experimentally that our method finds motion sequences of the same quality but using several orders of magnitude less computation time than existing approaches.

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

“…More recently, the RoboTSP algorithm [15] was shown to solve large sequencing problems by several orders of magnitude less computation time while producing solutions of similar quality when compared to other existing approaches. It achieved this by first solving for a task space tour of the goal points as a TSP and then determining the best robot configuration for each target point by applying Dijkstra's algorithm to a graph composed of configurations connected to those that correspond to the predecessor/successor target points in the resulting tour.…”

“…where w j is a positive weight for joint j and is derived from the relative maximum displacement of any point on the robot when actuated at the corresponding joint [15]. Now suppose Q is the population of all valid configurations comprising of the IK solutions ∀p i ∈ P n .…”

“…2) Configuration clustering: Given the input set Q , an optimal sequence S could be obtained by adopting a TSP formulation. However, numerous works have observed that the complexity of a TSP is exponential in practice [15], [19], [20]. The Cluster-RTSP overcomes these challenges by employing clustering techniques to partition Q into smaller sequencing sub-problems, which are collectively faster to solve.…”

Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm

“…Therefore, the third part of each chromosome is variable. However, joint angular velocities are defined as a constant in [20][21][22][23][24][25][26]41], leading to joint trajectories being depicted with the linear functions, and the third part of each chromosome being depicted with a constant. In AL2, joint angular velocity is defined as 0.8 rad/s (45.8 deg/s) [41].…”

“…In [23], each chromosome consists of three parts, the waypoint sequence, the sequence of joint configurations, and the robot placement. The technique of dividing each chromosome into several parts was also employed in [24][25][26]. A common point of IGAs in [22][23][24][25][26] is that the parameter denoting the joint angular velocity is predefined as a constant.…”