Fan Zheng

This brief presents real-time configuration estimation and motion planning for the industrial tractor–trailers vehicle composed of a full-scale car-like tractor and multiple full trailers. For the real-life vehicle, determining the configuration is challenging. With only on-tractor sensors, we solve this problem by fusing information from system dynamics propagation, geometrical constraints among the articulated units, and matching with the prebuilt environment map. The solution is efficiently achieved by formulating and solving a maximum a posterior (MAP) estimation problem in the pose-graph optimization framework. With the complicated tractor–trailers’ structure, small mismatch between actual and planned trajectories is crucial to inherit obstacle-free guarantee from planning to execution. We consider the dynamics and focus on facilitating the reduction of the mismatch by proposing a controller-based smoothing method to perform online motion planning. The given waypoint path is smoothed by forward propagation using a deliberately designed controller to generate the trajectory. The efficiently computed trajectories are also obstacle-free and dynamically feasible. The controller is also applied in execution to precisely reproduce the planned system evolvement. We demonstrate the performance with the real-life industrial tractor–trailers’ vehicle.

Visual inertial fusion is the problem to estimate vehicle states using visual and inertial measurements, and has recently been an active research topic. Existing visual inertial fusion approaches formulate their estimation algorithms for mobile robots in general 3-D space, and cannot satisfactorily model the nearly planar motion of ground vehicles. This paper proposed a new visual inertial fusion framework for ground vehicles, based on an SE(2)-constrained pose parameterization, which maximally conforms to the realistic situations of the ground vehicle motion. The estimation system is formulated as a graph optimization problem, and the SE(2)-constrained parameterization is implemented by introducing specially designed edges on the vehicle poses in the graph. Keyframe-based optimization and marginalization help to ensure the real-time performance. Experimental tests were conducted on challenging datasets with illumination changes and sharp-turn motion to validate the better accuracy of the proposed system compared to the state-of-the-art visual inertial estimation approaches.

This paper focuses on the motion estimation problem of ground vehicles using odometry and monocular visual sensors. While the keyframe-based batch optimization methods become the mainstream approach in mobile vehicle localization and mapping, the keyframe poses are usually represented by SE(3) in vision-based methods or SE(2) in methods based on range scanners. For a ground vehicle, this paper proposes a new SE(2)-constrained SE(3) parameterization of its poses, which can be easily achieved in the batch optimization framework using specially formulated edges. Utilizing such a parameterization of poses, a complete odometry-vision-based motion estimation system is developed. The system is designed in a commonly used structure of graph optimization, providing high modularity and flexibility for further implementation or adaptation. Its superior performance in terms of accuracy on a ground vehicle platform is validated by real-world experiments in industrial indoor environments.

This paper presents a brief review on recent work on machine intelligence for real-world applications of robots. To act in a real world environment, a robot should possess a broad sense of intelligence including speech, perception, reasoning, action, etc. In this paper, we particularly deal with the intelligence involving action or body motion. The intelligence related to robot action/motion can be classified into two categories: manipulation intelligence and mobility intelligence. The manipulation intelligence means the skill/intelligence of reliably manipulating objects according to tasks and the mobility intelligence corresponds to the ability of autonomously moving, or flying, and or jumping in a natural environment. Human-robot interaction is another important topic for real-world applications. In addition to reviewing the major approaches, this paper also gives an overview on our efforts in these important topics.