Sensor-Based Trajectory Generation for Advanced Driver Assistance System

Shackleton, Christopher James; Kala, Rahul; Warwick, Kevin

doi:10.3390/robotics2010019

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Christopher James Shackleton, Rahul Kala, and Kevin Warwick [21] have discussed the investigation of the problem of generating a collision free trajectory for assisting drivers. The current state of the vehicle is determined in order to do so.…”

Section: Literature Surveymentioning

confidence: 99%

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

2018

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Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment.

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Section: Literature Surveymentioning

confidence: 99%

Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

2018

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“…In recent years, automatic driving control has attracted attention, and various studies on this topic have been undertaken [1,2]. In automatic driving systems that achieve lane-keeping and obstacle avoidance, the traveling route is predicted based on the data of the driving environment collected using sensor information from cameras and other equipment mounted on the vehicle [3,4]. Acceleration and braking are then controlled to move the vehicle along the predicted traveling route.…”

Section: Introductionmentioning

confidence: 99%

Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

Minami

Iwai

2018

Robotics

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In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

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