This paper presents a software application developed in Python (v3.9) for obstacle avoidance trajectory planning in the RoboDK (v6.0) virtual environment. The proposed method automatically scans the virtual station, identifies obstacles, discretizes the workspace into a three-dimensional free-space-graph (FSG) and searches for candidate routes between start and finish points. Each route is then verified at the robot level by inverse kinematics and collision control, and the validated solutions can be transformed into preview curves, intermediate points and motion programs executable by the robot. The study includes an initial test scenario performed with the ABB IRB 6650-125/3.2 robot and randomly generated obstacles, followed by a series of benchmark tests performed in different virtual scenarios and with four different robot models. In the comparative tests, the proposed method was evaluated together with a rapidly exploring random tree (RRT) reference planner and the native probabilistic roadmap (PRM) planner, embedded RoboDK. The final scenario included a robotic cell with realistic objects. The results show that the application can identify valid executable routes and, in some cases, several alternative variants for the same pair of target points. Overall, the benchmark suggests that the analyzed methods have different strengths and should be viewed as complementary solutions. In the tested scenarios, RRT was the method with the lowest computational times, while the proposed method offered the possibility of generating several alternative routes. At the current stage, the application can thus be used as an offline programming tool but also as a research and analysis tool for planning robotic trajectories in the presence of static obstacles.
Cristoiu et al. (Mon,) studied this question.