In this work, we present a Stochastic Gradient Ascent (SGA) algorithm for multi-vehicle information gathering that accounts for limitations on a vehicle's control authority caused by external forces. By representing vehicle paths using a novel action space representation, rather than a state space representation, we remove the need to perform feasibility calculations on the vehicle's path. Our algorithm uses a stochastic optimization scheme by sampling perturbed action sequences around the current best known sequence to estimate the gradient of a state space information function with respect to the action sequence. Additionally, we use sequential greedy allocation to plan for multiple vehicles. Results are shown using a Navy Coastal Ocean Model (NCOM) for the Gulf of Mexico (GoM). SGA shows improvement in the amount of information gained over a greedy baseline. Additionally, we compare to Monte Carlo Tree Search (MCTS) Method, which is able to gather competitive amounts of information but is more computationally intensive than our approach.

Motion planning for multi-target autonomous search requires efficiently gathering as much information over an area as possible with an imperfect sensor. In disaster scenarios and contested environments the spatial connectivity may unexpectedly change (due to aftershock, avalanche, flood, building collapse, adversary movements, etc.) and the flight envelope may evolve as a known function of time to ensure rescue worker safety or to facilitate other mission goals. Algorithms designed to handle both expected and unexpected changes must: (1) reason over a sufficiently long time horizon to respect expected changes, and (2) replan quickly in response to unexpected changes. These ambitions are hindered by the submodularity property of mutual information, which makes optimal solutions NP-hard to compute. We present an algorithm for autonomous search in changing environments that uses a variety of techniques to improve both the speed and time horizon, including using e-admissible heuristics to speed up the search.

Quadruped locomotion offers significant advantages over wheeled locomotion for small mobile robots operating in challenging terrain. Central pattern generators (CPGs), as found in the neural circuitry of many animals, may be used to generate joint trajectories for quadruped robots. However, basic CPG-based trajectories do not explicitly consider ground contact constraints, a particular concern during turning maneuvers when foot slip is most likely to occur. An alternative approach proposed here is to use task-based CPGs such that ground contact constraints are enforced and foot velocities are explicitly controlled, resulting in stable omnidirectional locomotion. Further, incorporating Virtual Model Control with the task-based CPG trajectories improves the stability of the quadruped in hardware experiments.

Unmanned vehicles are emerging as an attractive tool for persistent monitoring tasks of a given area, but need automated planning capabilities for effective unattended deployment. Such an automated planner needs to generate collision-free coverage paths by steering waypoints to locations that both minimize the path length and maximize the amount of information gathered along the path. The approach presented in this paper significantly extends prior work and handles motion uncertainty of an unmanned vehicle and the presence of obstacles by using a Markov Decision Process based approach to generate collision-free paths. Simulation results show that the proposed approach is robust to significant motion uncertainties and reduces the probability of collision with obstacles in the environment.

This paper presents an efficient, compact, and robust distance-only sensor for networked small robotic platforms with wireless communication and signal processing capabilities. The sensor determines inter-robot distances by measuring the Time Difference of Arrival (TDOA) between wireless radio frequency packets and audio pulses. Computational overhead has been reduced by an order of magnitude while the sensor resolution has been improved to 0.27 cm over a range of 75 cm, compared to a previous resolution of 1.1 cm. Error analysis identified timing jitter as the dominant contribution to measurement error and a significant factor in heading estimation error using distance-only measurements. The improvement in distance resolution is shown to improve system performance by reducing the motion planning decision error rate due to measurement uncertainty.

This paper reports a compact, robust distance-only sensor for networked small robotic platforms. Two methods of sensing distance (neglecting heading) between robots are discussed: Received Signal Strength Indicator (RSSI) and Time Difference of Arrival (TDOA). Both implementations make use of a commercially available wireless sensor network board for communication and processing. Although RSSI requires no additional hardware, TDOA requires several additional components including a sound source and microphone. While experimental results indicate that both methods can provide distance sensing within a local neighborhood, TDOA sensing was found to be more robust and accurate, providing 1 cm distance resolution over a range of 80 cm versus 2.4 cm for RSSI. These sensors have been integrated onto mini-robotic platforms by incorporating a heading estimator and controller.