In this paper, we focus on the problem of directing the gaze of a vision system mounted to the body of a high-degree-of-freedom (DOF) legged robot for active perception deployments. In particular, we consider the case where the vision system is rigidly attached to the robot's body (i.e., without any additional DOF between the vision system and robot body) and show how the supernumerary DOFs of the robot can be leveraged to allow independent locomotion and gaze control. Specifically, we augment a workspace central pattern generator (CPG) with omnidirectional capabilities by coupling it with a body pose control mechanism. We leverage the smoothing nature of the CPG framework to allow online adaptation of relevant locomotion parameters, and obtain a stable mid-level controller that translates desired gaze orientation and body velocity directly into joint angles. We validate our approach on an 18-DOF hexapod robot, in a series of indoor and outdoor trials, where the robot inspects an environmental feature or follows a pre-planned path relative to a visually-tracked landmark, demonstrating simultaneous locomotion and directed vision.

This paper investigates how to estimate instantaneous fuel consumption using smartphones and OBD-II (On-board Diagnostics) adapters. Although most of the new cars have instant miles per gallon readout feature, the readings from those dashboard displays are usually proprietary and are difficult to record. Not to mention older cars do not have this feature. In this paper, we describe a system and associated algorithms to monitor fuel consumption of gasoline-powered vehicles in real time at second-level granularity. Specifically, we propose two algorithms: 1) Powertrain-based Model, which is derived from estimating an engine's fuel injection rate, and 2) Vehicle Dynamics-based Model, which considers fuel consumption in terms of the mechanical work applied to a vehicle. They are designed for vehicles with and without OBD-II adaptors respectively. The proposed system is compatible with most of the passenger vehicles and can be easily deployed. We evaluate our system in a field test and show that it can successfully estimate instantaneous fuel consumption, the average difference between estimation results and the ground truth is about 6%.

Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance of such robots in challenging terrain. However, many CPG models to date have been developed exclusively for open-loop gait generation for traversing level terrain. In this paper, we present a novel approach for incorporating inertial feedback into the CPG framework for the control of body posture during legged locomotion on steep, unstructured terrain. That is, we adapt the limit cycle of each leg of the robot with time to simultaneously produce locomotion and body posture control. We experimentally validate our approach on a hexapod robot, locomoting in a variety of steep, challenging terrains (grass, rocky slide, stairs). We show how our approach can be used to level the robot's body, allowing it to locomote at a relatively constant speed, even as terrain steepness and complexity prevents the use of an open-loop control strategy.

We present a distributed control strategy that enables a swarm of autonomous surface vehicles (ASVs) to cooperatively grasp and manipulate a large floating object from an initial position and orientation to a desired final position and orientation. Given an initial set of random robot positions, our control strategy enables the team to synchronize their arrival times around the object. Analytical motion trajectories are then computed to enable the swarm to transport the object to the final desired position. We validate the proposed strategy in simulation and present experimental results to demonstrate the feasibility of the proposed strategy.