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This paper presents a novel local planning approach called Agile and SAfe Pursuit (ASAP) for autonomous mobile robots. It aims to enable agile path following and safe collision avoidance in cluttered environments, while ensuring computational efficiency for real-time performance in embedded systems with limited computational power. For agile path following, the proposed approach utilizes a local path that includes a line path, arc path, and in-place rotation, and generates a target velocity based on the kinematic constraints of the robot. For safe collision avoidance, the proposed approach uses obstacle information to generate safety corners, which represent points in free space to circumvent obstacles with arbitrary shapes, and selects the best safety corner with the minimum travel time. To reach the target velocity as quickly as possible, the proposed approach uses a normalized velocity space to calculate control velocity that achieves the ratio of the linear and angular components of the target velocity in the shortest possible time period. For end-users to easily adapt a robot’s behavior to different environments, the proposed approach is designed to require only a few tuning parameters. The proposed algorithm’s agile control, rigorous collision avoidance, and computational efficiency were demonstrated through experimental results from hardware-in-the-loop simulations under various scenarios and real-robot tests in cluttered environments. Remarkably, the proposed approach achieves computational speeds that are 25 to 200 times faster than other existing algorithms. |
