We present AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion), a novel navigation approach to address holonomic robot collision avoidance when the robot does not know how cooperative the other agents in the environment are. AVOCADO departs from a Velocity Obstacle's (VO) formulation akin to the Optimal Reciprocal Collision Avoidance method. However, instead of assuming reciprocity, it poses an adaptive control problem to adapt to the cooperation level of other robots and agents in real time. This is achieved through a novel nonlinear opinion dynamics design that relies solely on sensor observations. As a by-product, we leverage tools from the opinion dynamics formulation to naturally avoid the deadlocks in geometrically symmetric scenarios that typically suffer VO-based planners. Extensive numerical simulations show that AVOCADO surpasses existing motion planners in mixed cooperative/non-cooperative navigation environments in terms of success rate, time to goal and computational time. In addition, we conduct multiple real experiments that verify that AVOCADO is able to avoid collisions in environments crowded with other robots and humans.

翻译：本文提出AVOCADO（基于观点驱动的自适应最优避碰方法），这是一种新颖的导航方法，用于解决全向机器人在未知环境中其他智能体合作程度时的避碰问题。AVOCADO的构建基础是类似于最优互惠避碰方法的速度障碍模型。然而，该方法不再假设互惠性，而是构建了一个自适应控制问题，以实时适应其他机器人和智能体的合作水平。这一目标通过一种仅依赖传感器观测的新型非线性观点动力学设计实现。作为衍生成果，我们利用观点动力学框架中的工具，自然避免了传统基于速度障碍的规划器在几何对称场景中常出现的死锁问题。大量数值仿真表明，在混合合作/非合作导航环境中，AVOCADO在成功率、目标抵达时间和计算时间方面均优于现有运动规划器。此外，我们通过多组真实场景实验验证了AVOCADO能够在充满其他机器人和人类的拥挤环境中实现有效避碰。