Path planning to generate an appropriate time sequence of positions for a complex trajectory is an open challenge in robotics. This paper proposes an optimization method with the integration of an improved ant colony algorithm and a high-order spline interpolation technique. The optimization process can be modelled as the travelling salesman problem. The greatest features of this method include: (1) automatic generation for complex trajectory and a new idea of selecting the nearest start point instead of using the traditional way of human operation; (2) an optimized motion sequence of the manipulator with the shortest length of the free-load path improves efficiency by nearly 65% and (3) trajectories both in Cartesian space and joint space are interpolated with good smoothness to reduce shocks and vibrations. Simulations and experiments are conducted to demonstrate the good properties of this method.

Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging task in robotics literature, where safety and reliability are the critical issues for successfully autonomous applications of robots. Classically, the SLAM problem is tackled via probabilistic or optimization methods (such as EKF-SLAM, Fast-SLAM, and Graph-SLAM). Despite their strong performance in real-world scenarios, these methods may exhibit inconsistency, which is caused by the inherent characteristic of model linearization or Gaussian noise assumption. In this paper, we propose an alternative monocular SLAM algorithm which theoretically relies on interval analysis (iMonoSLAM), to pursue guaranteed rather than probabilistically defined solutions. We consistently modeled and initialized the SLAM problem with a bounded-error parametric model. The state estimation process is then cast into an Interval Constraint Satisfaction Problem (ICSP) and resolved through interval constraint propagation techniques without any linearization or Gaussian noise assumption. Furthermore, we theoretically prove the obtained consistency and propose a versatile method for numerical validation. To the best of our knowledge, this is the first time such a proof has been proposed. A plethora of numerical experiments are carried to validate the consistency, and a preliminary comparison with classical EKF-SLAM in different noisy situations is also presented. Our proposed iMonoSLAM shows outstanding performance in obtaining reliable solutions, highlighting the potential application prospect in safety-critical scenarios of mobile robots.

Manipulators often face challenges of reliable trajectory planning under limited visibility. This paper proposes an obstacle avoidance grasping path planning method based on monocular vision. A multi-layer neural network model is to recognize target features, enabling rapid environmental perception. The environment topology is segmented using convex hulls, and multi-objective optimization is applied to achieve agile obstacle avoidance for the robot. The main contributions include the following: (1) an improved mask region convolutional neural network architecture is constructed to predict features under limited information, and (2) an innovative path planning strategy that combines Gaussian sampling with Hopfield neural networks is developed to improve the non-dominated sorting genetic algorithm. The algorithm achieves an identification accuracy of 99.50 % in complex scenarios, and the optimized trajectory shows improvements in smoothness and motion efficiency by approximately 60 % and 10 %, respectively. Through simulations and experiments, the significant effects on enhancing the robot's operability in complex environments have been validated.