Perceptive whole-body planning of highly maneuverable robots in confined spaces

有限空间内高机动机器人的感知全身规划

• 批准号: RGPIN-2021-02441
• 负责人: RamirezSerrano, Alejandro
• 金额: $ 1.97万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741979
• 关键词: Perceptive; whole; body; planning; highly

Biological systems (e.g. birds, dogs) can adaptively use their interlimb coordination for locomotion to deal with different situations such as flying through varying geometries (e.g. small openings) or crawling inside confined spaces. Neurophysiological studies have revealed that the adaptive coordination emerges from dynamic interactions of neural activities, plasticity, musculoskeletal systems, and the environment. Recently-developed legged robots and highly maneuverable aircraft are exceedingly versatile, having sophisticated control mechanisms allowing them to move and adapt their locomotion. Nevertheless, for autonomous robotic systems, achieving effective and agile maneuvering inside 3D (three-dimensional) complex confined spaces remains a challenge. Because of the computational complexity associated with such robots, no online planners exist for perceptive whole-body locomotion in tight spaces. In this project, new methods for perceptive planning will be developed for robots having multi-degrees of freedom (i.e. multi-legged robots and highly maneuverable drones), which will generate 3D body poses and the associated footholds or flight trajectories for collision avoidance. Measurements from onboard sensors will create a 3D map of the environment around the robot. A unified maneuvering mechanism combining the robot's kinematic and dynamic motion capabilities with knowledge about the environment and disturbances that the robot might encounter will be developed. The approach will target revolutionary agile multi-legged robots and highly maneuverable autonomous aircraft developed by the PI of this grant application. The approach will randomly sample body poses, then smooth the resulting trajectory while satisfying several constraints, such as robot motion and mission maximum time. Footholds and legged swing trajectories for legged robots, and thrust vectoring motions for aerial robots, will be computed based on the 3D environment and the robot's interaction with it. The robot's body pose will be optimized to ensure stable maneuvering behaviours inside confined spaces (e.g., moving down inside a collapsed structure), and collision avoidance while coping with any disturbances encountered. The envisioned whole-body planning will be developed to run online on real robot platforms and generate motion trajectories several meters long - sufficient to progressively move inside confined spaces where perception of only part of the environment is possible. The developments will be analyzed in diverse simulations and experimentally tested via biped, quadruped, and tilt-rotorcraft robots to demonstrate applicability to different robot configurations. Tests will be performed in realistic confined-space scenarios available at emergency rescue training facilities in Canada. The importance for science / industry / Canada are new paradigms for increase robot deployment which will have significant economic impact on Canada's sectors using autonomous systems.

生物系统（例如鸟类、狗）可以适应性地利用其肢体间协调进行运动来处理不同的情况，例如飞过不同的几何形状（例如小开口）或在密闭空间内爬行。神经生理学研究表明，适应性协调是由神经活动、可塑性、肌肉骨骼系统和环境的动态相互作用产生的。最近开发的腿式机器人和高度机动的飞机用途广泛，具有复杂的控制机制，使它们能够移动和适应其运动。然而，对于自主机器人系统来说，在 3D（三维）复杂的有限空间内实现有效且灵活的机动仍然是一个挑战。由于与此类机器人相关的计算复杂性，不存在用于在狭小空间中感知全身运动的在线规划器。在该项目中，将为多自由度机器人（即多足机器人和高机动性无人机）开发感知规划的新方法，这将生成 3D 身体姿势和相关的立足点或飞行轨迹，以避免碰撞。机载传感器的测量结果将创建机器人周围环境的 3D 地图。将开发一种统一的机动机构，将机器人的运动学和动态运动能力与机器人可能遇到的环境和干扰的知识相结合。该方法将针对由本次资助申请的 PI 开发的革命性敏捷多足机器人和高度机动的自主飞机。该方法将随机采样身体姿势，然后平滑所得轨迹，同时满足多个约束，例如机器人运动和任务最大时间。腿式机器人的立足点和腿式摆动轨迹以及空中机器人的推力矢量运动将根据 3D 环境以及机器人与其的交互进行计算。机器人的身体姿势将被优化，以确保在有限空间内稳定的机动行为（例如，在倒塌的结构内向下移动），并在应对遇到的任何干扰时避免碰撞。设想的全身规划将被开发为在真实的机器人平台上在线运行，并生成几米长的运动轨迹——足以在只能感知部分环境的有限空间内逐步移动。这些进展将在不同的模拟中进行分析，并通过双足、四足和倾斜旋翼机器人进行实验测试，以证明其对不同机器人配置的适用性。测试将在加拿大紧急救援训练设施的真实密闭空间场景中进行。对于科学/工业/加拿大来说，重要的是增加机器人部署的新范例，这将对加拿大使用自主系统的部门产生重大经济影响。