Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. Unlike on a highway, it has to deal with pedestrians, traffic lights and slower vehicles while maintaining smoothness in its drive. To tackle obstacle avoidance for this vehicle, sensor feedback based strategies have been formulated for smooth drive and obstacle avoidance. A full mathematical model for the vehicle is formulated and simulated in MATLAB environment. The mathematical model uses velocity control for obstacle avoidance without steering control. The obstacle avoidance is attained through velocity control and strategies are formulated with velocity profiling. Innovative techniques are formulated in creating the simulated sensory feed-backs of the environment. Using these feed-backs, correct velocity profiling is autonomously created for giving velocity profile input to the velocity controller. Proximity measurements are assumed to be available for the vehicle in its given range of drive. Novelty is attained by manipulating velocity profile without prior knowledge of the environment. Four different type of obstacles are modeled for simulated environment of the vehicle. These obstacles are randomly placed in the path of the vehicle and autonomous velocity profiling is verified in simulated environment. The simulated results obtained show satisfactory velocity profiling for controller input. The current technique helps to tune the existing controller and in designing of a better velocity controller for the autonomous vehicle and bridges the gap between sensor feed-back and controller input. Moreover, accurate input profiling creates less strain on the system and brings smoothness in drive for an overall safer environment.

中文翻译：

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基于模拟传感器的自动车辆FURBOT速度仿形避障策略

货运城市机器人车辆（FURBOT）是一种自动驾驶车辆，旨在将最后一英里的货物运输到指定的城市车站。这是一种缓慢的车辆，旨在完全自主地应对城市环境。慢速车辆在避开障碍物方面可能会有略微不同的策略。与高速公路不同，它必须应对行人，交通信号灯和速度较慢的车辆，同时保持行驶平稳。为了解决该车辆的避障，已经制定了基于传感器反馈的策略，以实现平稳驾驶和避障。在MATLAB环境中制定并仿真了车辆的完整数学模型。该数学模型使用速度控制来避开障碍物而无需转向控制。通过速度控制来实现避障，并通过速度分析来制定策略。在创建模拟的环境感官反馈方面制定了创新技术。使用这些反馈，可以自动创建正确的速度分析，以将速度曲线输入到速度控制器。假定在给定的驱动范围内可对车辆进行接近度测量。在不事先了解环境的情况下，通过操纵速度曲线可获得新颖性。针对车辆的模拟环境对四种不同类型的障碍物进行了建模。这些障碍物随机放置在车辆的路径中，并在模拟环境中验证了自动速度分析。获得的模拟结果显示了令人满意的控制器输入速度分析。当前的技术有助于调整现有的控制器，并有助于设计用于自动驾驶汽车的更好的速度控制器，并弥合传感器反馈和控制器输入之间的差距。此外，准确的输入配置文件可减轻系统负担，并为整体上更安全的环境带来驱动平稳性。