Design and implementation of a leader-follower controller for a wheel-legged robot
Bibliographic record
Abstract
Leader-follower formation control of mobile robots has been investigated by many researchers in the robotics community to control multi-robot systems. This control method can coordinate a team of unmanned vehicles by manoeuvering each robot to maintain its desired position with respect to the leader of the formation. The leader-follower control strategy can also be applied to implement human-follower behaviours on mobile robots. In this thesis, a leader-follower controller was developed for the Micro-Hydraulic Toolkit (MHT), a quadruped wheel-legged robot designed by Defence Research and Development Canada (DRDC) at the Suffield Research Centre. Previously, a velocity-based inverse kinematics controller was designed and implemented on the vehicle to control its posture. However, since the MHT does not have a steering mechanism for its wheels, this controller is unable to execute turning manoeuvres with the robot. Therefore, a separate controller was developed in order to steer the MHT to achieve leader-follower formation control. The purpose of the leader-follower controller is to compute the desired wheel velocities of the robot to reach and maintain a desired range and bearing with respect to a designated leader. To implement the controller on the physical robot, a vision algorithm was developed to measure the range and bearing of the leader with respect to the MHT using a monocular camera. A wide range of leader-follower scenarios was executed in simulation using a high fidelity physics-based model of the Toolkit and on the physical platform of the MHT to assess the performance of the leader-follower controller. The results of these tests demonstrated that the controller developed is successfully capable of executing leader-follower behaviours with the MHT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".