This paper presents a trajectory planner for autonomous driving based on a Nonlinear Model Predictive Control (NMPC) algorithm that accounts for Pacejka's nonlinear lateral tyre dynamics as well as for zero speed conditions through a novel slip angle calculation. In the NMPC framework, road boundaries and obstacles (both static and moving) are taken into account with soft and hard constraints implementation. The numerical solution of the NMPC problem is carried out using ACADO toolkit coupled with the quadratic programming solver qpOASES. The effectiveness of the proposed NMPC trajectory planner has been tested using CarMaker multibody models. The formulation of vehicle, road and obstacles' models has been specifically tailored to obtain a continuous and differentiable optimisation problem. This allows to achieve a computationally efficient implementation by exploiting automatic differentiation. Moreover, robustness is improved by means of a parallelised implementation of multiple instances of the planning algorithm with different spatial horizon lengths. Time analysis and performance results obtained in closed-loop simulations show that the proposed algorithm can be implemented within a real-time control framework of an autonomous vehicle.

In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density~(GM-PHD) filter with an Unscented Kalman Filter~(UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. We employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit.

Bioinspired walking hexapod robots are a relatively young branch of robotics. Despite the high degree of flexibility and adaptability derived from their redundant design, open-source implementations do not fully utilize this potential. This paper proposes an exhaustive description of a hexapod robot-specific control architecture based on open-source code that allows for complete control over a robot’s speed, body orientation, and walk gait type. Furthermore, terrain interaction is deeply investigated, leading to the development of a terrain-adapting control algorithm that allows the robot to react swiftly to the terrain shape and asperities, such as non-linearities and non-continuity within the workspace. For this purpose, a dynamic model derived from interpreting the hexapod movement is presented and validated through a Matlab SimMechanics^TM simulation. Furthermore, a feedback control system is developed, which is able to recognize leg–terrain touch and react accordingly to ensure movement stability. Finally, the results from an experimental campaign based on the PhantomX AX Metal Hexapod Mark II robotic platform by Trossen Robotics^TM are reported.

Focusing on autonomous driving algorithm development, this paper proposes a novel real-time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) algorithm. The mathematical formulation of the problem is deeply reported and discussed. The numerical solution of the NMPC problem is the result of a novel genetic algorithm strategy that represents the innovative aspect of the work proposed. The aim of this paper is also to show how genetic algorithm can be a valid approach for motion planning strategies. Numerical results are discussed through simulations that show a reasonable behaviour of the proposed strategy in the presence of moving obstacles as well as in a wide range of road friction conditions. Moreover, a real-time implementation for research purposes is assumed as possible by considering computational time analysis reported.