Parallel MPPI With Gradient-Velocity Modulated SDF Cost for High-Performance Real-Time Dynamic Obstacle Avoidance by Robot Manipulators

Abstract

Real-time motion planning in dynamic environments presents a significant challenge for robotic manipulators. This paper introduces an innovative parallel Model Predictive Path Integral (MPPI) algorithm enabling the robot to navigate swiftly and safely in such environments. Unlike the conventional MPPI methods that rely on a single sequence of Gaussian means for trajectory sampling, the proposed Parallel MPPI (PMPPI) concurrently runs multiple planners with different strategies and adaptively integrates planned paths based on the current state, leveraging the advantages of different strategies and greatly improving the MPPI's exploration capability. Moreover, a Gradient-Velocity Modulated Signed Distance Field (SDF) cost function that dynamically adjusts costs based on the robot's velocity and the SDF gradient is defined, thereby promoting safer and purposeful motion planning. In the implementation, techniques like utilizing inverse kinematics solver for path guidance and Sparse Reward to expedite reaching time are integrated into the MPPI cost function design. Comparative evaluations against the traditional MPPI architecture and standard SDF cost designs demonstrate the superiority of the new method. Real-world experiments, including human-robot interaction, obstacle-crossing, and grasping tasks, validate the robustness and universality of our methodology, with average and maximum end effector speeds of 0.523 m/s and 1.225 m/s respectively.