Trajectory Planning for Manipulators on Mobile Bases and in Dynamic Environments, Using Adaptive Models

This study addresses the challenges of trajectory planning for mobile manipulators whose base moves continuously during capture tasks. The main difficulty lies in adapting planning algorithms to dynamic scenarios, where base displacement compromises motion accuracy and efficiency. Traditional algorithms, developed for static or predictable environments, are unsuitable for unpredictable base movements. Key challenges include recalculating trajectories in real time to maintain safety and efficiency, while considering embedded systems’ computational constraints and the need for rapid responses to avoid collisions and optimize manipulation. To overcome these issues, approaches must integrate dynamic data and generate feasible trajectories regardless of base position. This work proposes adapting and evaluating two widely used motion planning algorithms: Rapidly-exploring Random Tree (RTT) and Real-Time Adaptive Motion Planning (RAMP). Both will be modified to account for base displacement, ensuring safe and efficient trajectories. Adjustment strategies will be embedded into the planning process, enabling algorithms to react dynamically to environmental and base position changes. Implementation will occur in a simulated environment, with a manipulator on a mobile base interacting with dynamically modeled objects. Experiments will assess accuracy, response time, and robustness. Expected outcomes include improved adaptation to base variations, reduced execution times, and enhanced object capture performance. These optimizations aim to advance real-world applications of mobile manipulators in industrial and assistive robotics.