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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759771
• 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）可以适应地使用其跨越的协调来进行运动，以应对不同情况，例如飞越不同的几何形状（例如，小开口）或在密闭空间内爬行。神经生理学研究表明，适应性协调来自神经活动，可塑性，肌肉骨骼系统和环境的动态相互作用。最近开发的腿部机器人和高度机动的飞机具有极大的用途，具有复杂的控制机制，使其可以移动并适应其运动。然而，对于自主机器人系统，在3D（三维）复杂的限制空间内实现有效而敏捷的操纵仍然是一个挑战。由于与此类机器人相关的计算复杂性，因此在紧密空间中没有在线规划师可以进行全身运动。在这个项目中，将为具有多重自由度的机器人（即多腿机器人和高度可操作的无人机）开发新的知觉计划方法，该机器人将产生3D身体姿势以及相关的立足点或相关的飞行轨迹，以避免碰撞。车载传感器的测量将创建机器人周围环境的3D图。将机器人的运动学和动态运动能力与有关机器人可能遇到的环境和干扰的知识相结合的统一机制。该方法将针对革命性的敏捷多腿机器人和该赠款应用程序PI开发的高度操纵的自动驾驶飞机。该方法将随机样本体姿势，然后平滑所得的轨迹，同时满足几个约束，例如机器人运动和任务最大时间。将根据3D环境和机器人与它的相互作用来计算腿部机器人的立足和腿部摆动轨迹，并为空中机器人进行推力矢量。机器人的身体姿势将得到优化，以确保狭窄的空间内（例如，在崩溃的结构内移动），并在应对遇到的任何干扰时避免碰撞。将开发设想的全身计划以在实际机器人平台上进行在线运行，并生成几米长的运动轨迹 - 足以逐步移动在限制空间内，在这些空间中，只有一部分环境的感知是可能的。这些发展将在不同的模拟中进行分析，并通过双头，四倍和倾斜旋转机器人进行实验测试，以证明对不同机器人配置的适用性。在加拿大的紧急救援培训设施可用的现实限制空间场景中，将进行测试。对科学 /工业 /加拿大的重要性是增加机器人部署的新范式，这将对加拿大使用自主系统产生重大的经济影响。