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No abstract

Most human-centred robotic applications require robots to follow a certain pre-defined path. This makes the robot's autonomous movements acceptable and predictable for humans. Planning a trajectory for the robot thus involves guiding it along this desired path. The classical approach of segmenting a path into multiple waypoints and tracking them only works well in environments which are obstacle-free or contain fixed stationary obstacles. Movable or dynamic obstacles that can potentially lie directly on waypoints result in deadlock situations causing the robot to oscillate around the desired waypoint without moving forward. This chapter presents a novel approach for trajectory planning in which an Adaptive Virtual Target (AVT) is formulated that follows the desired path irrespective of surrounding obstacles. The AVT essentially plays the role of a moving reference for the trajectory planner to track. Additionally, the AVT velocity can be adapted such that the robot can catch up in case of deviations from the path due to obstacle avoidance manoeuvres. A model predictive control (MPC) based trajectory planner tracks the AVT and accounts for obstacle avoidance. The proposed approach allows the robot to keep moving towards the goal by preventing deadlocks while simultaneously minimizing deviation from the desired path. Simulations based on a medical X-ray robot are provided to validate the approach.INDEX TERMS Trajectory planning, model predictive control, autonomous robots, deadlock prevention.