Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this work, we consider the multi-goal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multi-goal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM* with an informed sampler to accelerate collision-free path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain.

翻译：机器人探索或监测任务要求移动机器人在潜在挑战性环境中自主安全地导航至多个目标位置。当前此类多目标任务常依赖人类设计一组机器人需遵循的路径或航点形式的行动方案。本文考虑访问一组预定义目标的多目标问题，其中每个目标可从多个潜在位置进行访问。为提升此类任务的自主性，我们提出一种安全多目标规划器（SMUG Planner），可生成访问这些目标的最优运动路径。为增强安全性与效率，我们提出一种层级状态有效性检查方案，该方案利用仿真中学习的机器人特定可通行性。我们采用带启发式采样器的LazyPRM*算法加速无碰撞路径生成。所提出的迭代动态规划算法使规划器能在数秒内生成访问超过十个目标的路径。此外，相较于纯体素碰撞检测，层级状态有效性检查方案将规划时间降低30%，并通过避开高风险区域提升安全性。我们将SMUG规划器部署于四足机器人ANYmal，验证其在崎岖地形上完全自主引导机器人完成多目标任务的能力。