Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily disturb the tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), with the following features to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use “adver-sarial” torso motions that would tax the PURE's ability to keep the ballbot idling or below a set speed, i.e., competing objectives between rider and robot. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task, where the commanded speed was saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

Ensuring the robustness of lane detection is essential for the reliability of autonomous vehicles, particularly in diverse weather conditions. While numerous algorithms have been proposed, addressing challenges posed by varying weather remains an ongoing issue. Geometric-based lane detection methods, rooted in the inherent properties of road geometry, provide enhanced generalizability. However, these methods often require manual parameter-tuning to accommodate fluctuating illumination and weather conditions. Conversely, learning-based approaches, trained on pre-labeled datasets, excel in localizing intricate and curved lane configurations but grapple with the absence of diverse weather datasets. This paper introduces a hybrid approach that merges the strengths of both methodologies. A novel adaptive preprocessing method is proposed in this work. Utilizing a fuzzy inference system (FIS), the algorithm dynamically adjusts parameters in geometric-based image processing functions and enhances adaptability to diverse weather conditions. This preprocessing algorithm is designed to seamlessly integrate with all learning-based lane detection models. When implemented with CNN-based models, the hybrid approach demonstrates commendable generalizability across weather and adaptability to complex lane configurations. Rigorous testing on datasets featuring challenging weather showcases the proposed method’s significant improvements over existing models, underscoring its efficacy in addressing persistent challenges associated with lane detection in adverse weather. • This work proposed an Expert System-based image pre-processing method. • The preprocessing method adaptively tuned the parameters with a Fuzzy Inference System. • The preprocessing method was integrated with multiple current CNN-based lane detection models and improved their robustness under rainy weather conditions.

A novel waypoint navigation controller for a skid-steer vehicle is presented, where the controller is a multiple input-multiple output nonlinear angular velocity and linear speed controller. Hierarchical rule-base reduction was used in defining the controller. This entailed selecting inputs/outputs, determining the most globally influential inputs, generating a hierarchy relating inputs, selecting only the rules corresponding to the hierarchy, and, in effect, designing a symmetric rule-base. This dramatically reduced the rule-base size, by 97.7%, while maintaining global operating environment coverage. The stability analysis proved the asymptotic stability of the closed-loop controller-vehicle system. In addition, test courses were used to examine the effects of steering disturbance, phase lag, and overshoot as expressed in root mean square error (RMSE) and max error (ME). Outdoor experimental results for the controller's performance were contrasted with a benchmark waypoint navigation controller, pure pursuit, and a simpler implementation that only output linear speed. The controller was found to outperform the pure pursuit and simpler implementation experimentally by 72% and 50% in RMSE, 71% and 40% in ME, validating the controllers viability.

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, who may have reduced torso motion, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor environments. Each participant adjusted personal sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the effectiveness (number of collisions), efficiency (completion time), comfort (NASA TLX scores except physical demand), and robustness (index of performances) were similar for HF and JS control and between mWCUs and ABUs for all sections. These results suggest that PURE provides an effective method for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion.

This article presents a comprehensive approach to designing and optimizing a hierarchical rule-base reduction-based adaptive-network-based fuzzy inference system (ANFIS) for symmetric linguistic variables. Specifically, the linguistic connected 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 deep deterministic policy gradient (DDPG) 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 mean absolute error (MAE) and root mean squared error (RMSE) of 0.88 and 1.02 m, respectively, to a final MAE and RMSE of 0.087 and 0.10 m. The results highlight the effectiveness of the ANFIS approach for vehicular robotics applications and suggest promising avenues for future research and development.