Planning collision-free trajectories is essential for wheeled mobile robots operating in dynamic environments safely and efficiently. Most current trajectory generation methods focus on achieving optimal trajectories in static maps and considering dynamic obstacles as static depending on the precise motion estimation of the obstacles. However, in realistic applications, dealing with dynamic obstacles that have low reliable motion estimation is a common situation. Furthermore, inaccurate motion estimation leads to poor quality of motion prediction. To generate safe and smooth trajectories in such a dynamic environment, we propose a motion planning algorithm called trend-aware motion planning (TAMP) for dynamic obstacle avoidance, which combines with timed-elastic band. Instead of considering dynamic obstacles as static, our planning approach predicts the moving trends of the obstacles based on the given estimation. Subsequently, the approach generates a trajectory away from dynamic obstacles, meanwhile, avoiding the moving trends of the obstacles. To cope with multiple constraints, an optimization approach is adopted to refine the generated trajectory and minimize the cost. A comparison of our approach against other state-of-the-art methods is conducted. Results show that trajectories generated by TAMP are robust to handle the poor quality of obstacles’ motion prediction and have better efficiency and performance.

中文翻译：

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在动态环境中运行的轮式移动机器人的趋势感知运动规划

规划无碰撞轨迹对于在动态环境中安全高效地运行的轮式移动机器人至关重要。大多数当前的轨迹生成方法侧重于在静态地图中实现最佳轨迹，并根据障碍物的精确运动估计将动态障碍物视为静态。然而，在实际应用中，处理运动估计可靠性低的动态障碍物是一种常见的情况。此外，不准确的运动估计导致较差的运动预测质量。为了在这样的动态环境中生成安全平滑的轨迹，我们提出了一种称为趋势感知运动规划 (TAMP) 的运动规划算法，用于动态避障，它与定时弹性带相结合。与其将动态障碍视为静态障碍，我们的规划方法根据给定的估计预测障碍物的移动趋势。随后，该方法生成远离动态障碍物的轨迹，同时避免障碍物的移动趋势。为了应对多个约束，采用优化方法来细化生成的轨迹并最小化成本。将我们的方法与其他最先进的方法进行比较。结果表明，TAMP 生成的轨迹对于处理质量较差的障碍物运动预测具有鲁棒性，并且具有更好的效率和性能。采用优化方法来细化生成的轨迹并最小化成本。将我们的方法与其他最先进的方法进行比较。结果表明，TAMP 生成的轨迹对于处理质量较差的障碍物运动预测具有鲁棒性，并且具有更好的效率和性能。采用优化方法来细化生成的轨迹并最小化成本。将我们的方法与其他最先进的方法进行比较。结果表明，TAMP 生成的轨迹对于处理质量较差的障碍物运动预测具有鲁棒性，并且具有更好的效率和性能。