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This research introduces a constrained motion planning framework that enhances robot trajectory generation for human-robot interaction by integrating collision avoidance and self-collision avoidance into the planning process. By employing Rapidly-exploring Random Tree (RRT) and RRT* algorithms to create a dataset of feasible trajectories, the authors train a diffusion model that efficiently generates constraint-satisfying trajectories through guided sampling. The application of consistency distillation and a joint-weighted jerk regularization term significantly improves the speed and smoothness of trajectory generation, achieving 150 candidates in under 100 ms while maintaining high success rates and reduced jerk.
Generating 150 feasible robot trajectories in under 100 ms while ensuring safety and smoothness could revolutionize human-robot interaction dynamics.
This research proposes a constrained motion planning framework for robot manipulators in human-robot interaction (HRI). For a non-redundant manipulator with a fully specified end-effector pose, additional requirements such as collision avoidance and self-collision avoidance are difficult to handle as simple null-space secondary tasks. This limitation makes it challenging to generate feasible joint-space trajectories in HRI environments where safety and kinematic constraints must be considered simultaneously. To address this limitation, collision- and self-collision-aware trajectories are generated using Rapidly-exploring Random Tree (RRT) and RRT* algorithms, and the resulting dataset is used to train a diffusion model that generates constraint-satisfying trajectories through guided sampling. To reduce the inference time required for iterative diffusion sampling, consistency distillation is applied, and a joint-weighted jerk regularization term is incorporated into the loss function to promote smoother trajectories by penalizing abrupt changes in joint acceleration. Simulation results show that the consistency model generates 150 trajectory candidates in less than 100 ms, maintains a high episode success rate, and substantially reduces joint and end-effector jerk when jerk regularization is applied.