This paper presents a comprehensive approach to designing and optimizing a Hierarchical Rule-Base Reduction (HRBR) based Adaptive-Network-Based Fuzzy Inference System (ANFIS) for symmetric linguistic variables. Specifically, the linguistic joint membership functions that underlie the ANFIS are defined, focusing on symmetrical inputs/outputs and jointly optimized trapezoid membership functions to reduce the number of training parameters. Further optimizations for the ANFIS were derived based on design assumptions, including training the membership functions on closed or single-sided domains. The optimal output membership weights based on mean square error optimization were also symbolically obtained. The online training of the ANFIS’s input/output membership functions was performed using the DDPG (Deep Deterministic Policy Gradient) algorithm. A simulated skid-steered vehicle was used to validate the approach and performed waypoint-to-waypoint path following. Experimental results using the Clearpath Jackal demonstrated that the ANFIS model converged quickly, typically within 6 to 10 episodes of training, from an initial MAE and RMSE of 0.88 and 1.02 meters, respectively, to a final MAE and RMSE of 0.087 and 0.10 meters. The results highlight the effectiveness of the ANFIS approach for vehicular robotics applications and suggest promising avenues for future research and development.

A novel wheelchair called PURE (Personalized Unique Rolling Experience) that uses hands-free (HF) torso lean-to-steer control has been developed for manual wheelchair users (mWCUs). PURE addresses limitations of current wheelchairs, such as the inability to use both hands for life experiences instead of propulsion. PURE uses a ball-based robot drivetrain to offer a compact, self-balancing, omnidirectional mobile device. A custom sensor system converts rider torso motions into direction and speed commands to control PURE, which is especially useful if a rider has minimal torso range of motion. We explored whether PURE’s HF control performed as well as a traditional joystick (JS) human-robot interface and mWCUs, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor settings. Each participant adjusted ride sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the number of collisions, completion time, NASA TLX scores except physical demand, and index of performances were similar for HF and JS control and between mWCUs and ABUs for all sections. This suggests that PURE is effective for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion.Link for supplementary material (appendix, short descriptive video): https://uofi.box.com/s/hbrs0bbbxez4hr51potzywy3p9iupryu

We present a Multiple Input Multiple Output fuzzy path tracking controller that reduced its rule-base using Hierarchical Rule Base Reduction. A stability proof and outdoor results are also presented.

With the advent of connected vehicle technology and widespread long-distance communication, leveraging data from real-world operational cases to identify and characterize vehicle dynamics and control is now possible. However, there exists a limited number of studies in the field of off-road vehicle identification as compared to on-road vehicle identification. This study explores the applications of Dynamic Mode Decomposition with Control (DMDc) for linear vehicle dynamics, linear human-driver-controller identification, and possible use for off-road vehicle identification problems. An implementation is tested using real-world data collected from the University of Illinois Modified R-Gator(UIMR), an autonomy-capable, drive-by-wire, 6x4, Ackerman Steer, wheeled off-road vehicle platform. The UIMR, partially developed by the University of Illinois at Urbana-Champaign’s Autonomous and Unmanned Vehicle Systems Lab, part of the Industrial and Systems Engineering department, is presented for the first time.

A new form of waypoint navigation controller for a skid-steer vehicle is presented, which consisted of a multiple input-single output nonlinear fuzzy angular velocity controller. The mem- bership functions of the fuzzy controller employed a trape- zoidal structure with a completely symmetric rule-base. No- tably, Hierarchical Rule-Base Reduction (HRBR) was incorpo- rated into the controller to select only the rules most influen- tial on state errors. This was done by selecting inputs/outputs and generating a hierarchy of inputs using a Fuzzy Relations Control Strategy (FRCS). Similar to some traditional fuzzy con- trollers, the system provided coverage for the global operat- ing environment. However, a rule for every possible combi- nation of variables and states was no longer necessary. Con- sequently, HRBR fuzzy controllers effectively increase both the number of inputs and their associated fidelity without the rule-base dramatically increasing. To contextualize the performance of the controller, a background on vehicle dy- namic modeling methodologies and an in-depth explanation of the related simulation model are provided. An examina- tion of the proposed controller is then completed employing test courses. The test courses examine the effects of steer- ing disturbance, phase lag, and overshoot as expressed in Root Mean Square Error (RMSE), Max Error (ME), and Course Completion Time (CCT). Finally, simulation and experimental results for the controller’s performance were compared with a state-of-the-art waypoint navigation vehicle controller, ge- ometric pure pursuit. The fuzzy was found to outperform the pure pursuit experimentally by 52.1 percent in RMSE, 26.8 percent in ME, and 1.07 percent in CCT, on average, validat- ing the viability of the controller.