Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

BALANCE MOTION PLANNING FOR HUMANOID ROBOT BASED ON 3D INVERTED PENDULUM MODEL

Researches on humanoid robots are alway attractive to many researchers in robotics field. One of considerable challenges of humanoid robots is to keep balance and stability of their movement. Because a humanoid robot moves by two legs, most of time of the step period of the humanoid robot is be in one leg touching on the floor and the other leg swinging forward. This posture is similar to a three dimension (3D) inverted pendulum model. This papers presents the dynamic model of a 3D inverted pendulum model and applies to balanced motion planning for a humanoid robot. The obtained results show that the robot is able to keep balance during its movements

Mediating Between Contact Feasibility and Robustness of Trajectory Optimization Through Chance Complementarity Constraints

As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our approach in two simple examples: a push-block system for benchmarking and a single-legged hopper. We demonstrate that chance constraints alone produce trajectories similar to those produced using strict complementarity constraints; however, when equipped with a robust objective, we show the chance constraints can mediate a trade-off between robustness to uncertainty and strict constraint satisfaction. Thus, our study may represent an important step towards reasoning about contact uncertainty in motion planning.