Universal Trajectory Optimization Framework for Differential Drive Robot Class
Mengke Zhang, Nanhe Chen, Hu Wang, Qiu JianXiong, Zhichao Han, Qiuyu Ren, Chao Xu, Fei Gao, Yanjun Cao
AI summary
Problem
Existing trajectory planners for differential drive robots struggle to balance computational efficiency, trajectory smoothness, and accurate modeling of nonholonomic dynamics and lateral slip, hindering real-time navigation in crowded environments.
Approach
The authors parameterize robot orientation and forward arc length as polynomials to inherently satisfy kinematic constraints and model lateral slip, then optimize these motion states with numerical integration to efficiently compute Cartesian trajectories.
Key results
- Novel motion-state trajectory representation capturing nonholonomic dynamics and lateral slip
- Efficient polynomial optimization method for smooth, feasible trajectory generation
- Robust planning and control framework with online kinematic parameter estimation
- Validated through simulations and real-world tests on three differential drive robot types
Why it matters
Enables reliable, real-time navigation for diverse differential drive platforms in complex environments, advancing autonomous mobile robotics.
Abstract
Differential drive robots are widely used in var- ious scenarios thanks to their straightforward principle, from household service robots to disaster response field robots. The nonholonomic dynamics and possible lateral slip of these robots lead to difficulty in getting feasible and high-quality trajecto- ries. Although there are several types of driving mechanisms for real-world applications, they all share a similar driving principle, which involves controlling the relative motion of independently actuated tracks or wheels to achieve both linear and angular movement. Therefore, a comprehensive trajectory optimization to compute trajectories efficiently for various kinds of differential drive robots is highly desirable. In this paper, we propose a universal trajectory optimization framework, enabling the generation of high-quality trajectories within a restricted computational timeframe for these robots. We introduce a novel trajectory representation based on polynomial parameterization of motion states or their integrals, such as an- gular and linear velocities, which inherently matches the robots’ motion to the control principle. The trajectory optimization problem is formulated to minimize computation complexity while prioritizing safety and operational efficiency. We then build a full-stack autonomous planning and control system to demonstrate its feasibility and robustness. We conduct extensive simulations and real-world testing in crowded environments with three kinds of differential drive robots to validate the effectiveness of our approach.