ASCON: A Hybrid Path Planning Algorithm for Manipulators in Strongly Constrained Narrow Passages

Path planning for high-DOF robotic manipulators in highly constrained environments (e.g., narrow passages) remains challenging due to poor configuration-space (C-space) connectivity, low computational efficiency, and susceptibility to local minima. This paper proposes a hybrid planner, termed ASCON, which couples the directional guidance of an improved Artificial Potential Field (APF) with the global exploration capability of RRT-Connect to achieve robust planning in non-convex, strongly constrained workspaces. A smoothed potential-field formulation is introduced to suppress oscillations and improve motion smoothness, while a link-radius-based envelope collision-checking strategy is incorporated to ensure safety margins for real deployment. The evaluation is conducted in two benchmark scenarios—dual-layer stacked obstacles and a 100 mm narrow passage—with 50 independent trials per method per scenario; a run is considered successful only if a collision-free feasible path is found within preset iteration/time limits using fixed hyperparameters. Results show that, compared with conventional APF, ASCON reduces average planning time by 66.0%, decreases iteration count by 80.5%, shortens path length by 13.5%, and lowers peak jerk by 40.3%. Physical experiments further validate practical feasibility by guiding a real manipulator through a 100 mm narrow passage in a collision-free manner, demonstrating efficient, smooth, and robust planning under extreme constraints.