This paper introduces a novel motion planning algorithm designed for a team of curvature-constrained tethered robots operating on sloped 3D terrains. Our approach addresses the critical issues of tether-terrain interaction, robot stability, and tether entanglement avoidance. The study focuses on a two-robot system, where stability is primarily dependent on tether tension, which is in turn limited by wheel traction. We propose a path-planning method that strategically utilizes terrain features (e.g., rocks) to augment tether tension through additional friction, thereby enhancing overall system stability. Our algorithm employs a modified tangent graph as the underlying structure for a hybrid A* search, incorporating stability constraints throughout the planning process. The proposed method is extensively evaluated through various simulation experiments, demonstrating its effectiveness in planning safe and efficient paths.

In this paper, a variant of hybrid A* is developed to find the shortest path for a curvature-constrained robot, that is tethered at its start position, such that the tether satisfies user-defined winding angle constraints. A variant of tangent graphs is used as an underlying graph for searching a path using A*in order to reduce the overall computation and define appropriate cost metrics to ensure winding angle constraints are satisfied. Conditions are provided under which the proposed algorithm is guaranteed to find a winding angle constrained path. The effectiveness and performance of the proposed algorithm are studied in simulation.

In the absence of a tether attachment mechanism that can provide infinitely large tension to the tethered robots moving on extreme planetary terrains, there is a limit on how much tension can be realistically generated or supported by the tether. In this paper, we consider a team of two robots tethered together moving on extreme terrains. The traction on the wheels of the robot and the friction between the tether and the tether attachment surfaces/objects (e.g., rocks) is the only way to support the tether tension. Given a path for the robots to navigate, we provide a systematic algorithm to check if the robots will be stable along the given path while considering the maximum constraints on the tension generated or supported by the tether. The results are validated via simulation experiments.

This article presents a multimode solution to the problem of defending a circular protected area (target) from a wide range of attacks by swarms ofrisk-takingand/orrisk-averseattacking agents (attackers). The proposed multimode solution combines two defense strategies, namely: 1) an interception strategy for a team of defenders to intercept multiplerisk-takingattackers while ensuring that the defenders do not collide with each other; 2) a herding strategy to herd a swarm ofrisk-averseattackers to a safe area. In particular, we develop mixed integer programs (MIPs) and geometry-inspired heuristics to distribute and assign and/or reassign the defenders to interception and herding tasks under different spatiotemporal behaviors by the attackers such as splitting into smaller swarms to evade defenders easily or high-speed maneuvers by some risk-taking attackers to maximize damage to the protected area. We provide theoretical as well as numerical comparison of the computational costs of these MIPs and the heuristics, and demonstrate the overall approach in simulations.

This article presents a method for herding a swarm of adversarial agents toward a safe area in a 2-D obstacle environment. The team of defending agents (defenders) aims to block the path of a swarm of risk-averse, adversarial agents (attackers) and guide it to a safe area while navigating in an obstacle-populated environment. To achieve this, a closed formation (StringNet) of defenders is formed around the adversarial swarm. A combination of open-loop, near time-optimal controllers (that result in forming the defenders’ formation), and state-feedback controllers with finite-time convergence guarantees under bounded inputs (that guide the formation around attackers and toward the safe area) synthesize the herding strategy. For demonstration purpose, we consider that the attacking swarm moves under a flocking model, which however is unknown to the defenders. Collision-free trajectory generation for the defenders, as well as their convergence to the desired formations, is proved formally, and simulations are provided to demonstrate the efficacy of the proposed approach. An implementation of the proposed approach on quadrotor vehicles simulated in the Gazebo simulator is also provided.