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

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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.