Low-cost distributed robots suffer from limited onboard computing power, resulting in excessive computation time when navigating in cluttered environments. This paper presents Edge Accelerated Robot Navigation (EARN), to achieve real-time collision avoidance by adopting collaborative motion planning (CMP). As such, each robot can dynamically switch between a conservative motion planner executed locally to guarantee safety (e.g., path-following) and an aggressive motion planner executed non-locally to guarantee efficiency (e.g., overtaking). In contrast to existing motion planning approaches that ignore the interdependency between low-level motion planning and high-level resource allocation, EARN adopts model predictive switching (MPS) that maximizes the expected switching gain with respect to robot states and actions under computation and communication resource constraints. The MPS problem is solved by a tightly-coupled decision making and motion planning framework based on bilevel mixed-integer nonlinear programming and penalty dual decomposition. We validate the performance of EARN in indoor simulation, outdoor simulation, and real-world environments. Experiments show that EARN achieves significantly smaller navigation time and higher success rates than state-of-the-art navigation approaches.

翻译：低成本分布式机器人受限于机载计算能力，在复杂环境中导航时往往面临计算时间过长的问题。本文提出边缘加速机器人导航系统，通过采用协同运动规划实现实时避障。该系统使每个机器人能够动态切换运动规划策略：本地执行保守型规划器以保证安全性，非本地执行激进型规划器以提升效率。与现有忽略底层运动规划与高层资源分配相互依赖关系的方案不同，本系统采用模型预测切换机制，在计算与通信资源约束下，基于机器人状态与动作最大化期望切换收益。该切换问题通过双层混合整数非线性规划与惩罚对偶分解的紧耦合决策-规划框架求解。我们在室内仿真、室外仿真及真实场景中验证了系统性能。实验表明，相比现有先进导航方法，本系统在导航时间与成功率方面均有显著提升。