In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip.

To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

This paper proposes a new approach for solving a generalization of the task scheduling problem for articulated robots (either redundant or non-redundant), where the robot's 2D environment is cluttered with obstacles of arbitrary size, shape and location, while a set of task-points are located in the robot's free-space. The objective is to determine the optimum collision-free robot's tip tour through all task-points passing from each one exactly once and returning to the initial task-point. This scheduling problem combines two computationally NP-hard problems: the optimal scheduling of robot tasks and the collision-free motion planning between the task-points.

The proposed approach employs the bump-surface (B-Surface) concept for the representation of the 2D robot's environment by a B-Spline surface embedded in 3D Euclidean space. The time-optimal task scheduling is being searched on the generated B-Surface using a genetic algorithm (GA) with a special encoding in order to take into consideration the infinite configurations corresponding to each task-point. The result of the GA's searching constitutes the solution to the task scheduling problem and satisfies optimally the task scheduling criteria and objectives. Extensive experimental results show the efficiency and the effectiveness of the proposed method to determine the collision-free motion among obstacles.

In this paper we present a specialized system for temporal reasoning representing possibly uncertain qualitative and quantitative constraints relative to temporal intervals in a homogeneous framework. The underlying logic is that of Allen, which we have extended to include quantitative temporal information. Intervals are still assumed as primitive entities while, to represent metric information, time points are introduced only at the algorithmic level. We illustrate the properties of the temporal representation, the algorithms used for constraint propagation, and their complexity. We show how the system can be used to schedule manufacturing operations in a small factory.