Deploying a team of autonomous robots, operating collaboratively towards a common objective within dynamic environments, has the potential to improve the system efficiency across several fields. This paper proposes a distributed comprehensive framework enabling a networked multi-robot system to serve time-varying requests arising from different locations within the environment in a distributed and safe manner, i.e., by guaranteeing no collisions with possible obstacles and preserving connectivity among the robots. To this aim, a two-layer architecture is proposed where the top layer is in charge of distributively assigning new service requests to the robots by resorting to an auction-based algorithm, while the bottom layer is in charge of safely navigating the environment to serve the assigned requests by relying on Control Barrier Functions. However, the presence of connectivity constraints might affect the number of service requests that the multi-robot system can handle simultaneously and might lead to deadlock situations where robots cannot reach the designated locations due to loss of network connectivity. Hence, a distributed strategy based on consensus algorithms to detect and solve deadlocks in a distributed fashion is proposed. The completeness of the approach is proved. Simulation results in an agricultural setting and real-world laboratory experiments are provided to validate the effectiveness of the proposed approach.Note to Practitioners—This paper was inspired by the necessity to coordinate a team of robots to perform tasks within an unstructured agricultural field, including both the decision-making and navigation strategies, with no central control unit as envisioned by the European project CANOPIES. To this aim, a distributed approach is designed where robots only rely on local data and information from neighboring robots to assign and execute tasks effectively in a coordinated manner. In addition, as working under local communication constraints may prevent parallel execution of all tasks, potentially leading to deadlock situations, a distributed strategy is developed to enable each robot to detect and solve such situations. The proposed approach can be employed in several domains where the cooperation of multiple autonomous robots might be beneficial, ranging from logistics settings to search and rescue scenarios up to agricultural environments. Laboratory experiments with three robots demonstrate the effectiveness of the approach.

Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fitting new targets. However, previous works have observed that networks trained under non-stationarity exhibit an inability to continue learning, termed loss of plasticity, and eventually a collapse in performance. For off-policy deep value-based RL methods, this phenomenon has been correlated with a decrease in representation rank and the ability to fit random targets, termed capacity loss. Although this correlation has generally been attributed to neural network learning under non-stationarity, the connection to representation dynamics has not been carefully studied in on-policy policy optimization methods. In this work, we empirically study representation dynamics in Proximal Policy Optimization (PPO) on the Atari and MuJoCo environments, revealing that PPO agents are also affected by feature rank deterioration and capacity loss. We show that this is aggravated by stronger non-stationarity, ultimately driving the actor's performance to collapse, regardless of the performance of the critic. We ask why the trust region, specific to methods like PPO, cannot alleviate or prevent the collapse and find a connection between representation collapse and the degradation of the trust region, one exacerbating the other. Finally, we present Proximal Feature Optimization (PFO), a novel auxiliary loss that, along with other interventions, shows that regularizing the representation dynamics mitigates the performance collapse of PPO agents. Code and run histories are available at https://github.com/CLAIRE-Labo/no-representation-no-trust.

Elliptic Curve Cryptography (ECC) is a popular tool to construct public-key crypto-systems. The security of ECC is based on the hardness of the elliptic curve discrete logarithm problem (ECDLP). Implementing and analyzing the performance of the best known methods to solve the ECDLP is useful to assess the security of ECC and choose security parameters in practice. We present a novel many-core hardware architecture implementing the parallel version of Pollard's rho algorithm to solve the ECDLP. This architecture results in a speed-up of almost 300% compared to the state of the art and we use it to estimate the monetary cost of solving the Certicom ECCp-131 challenge using FPGAs.

This paper presents a novel approach to exploit FPGA dynamic partial reconfiguration to improve the fault tolerance of complex microprocessor-based systems, with no need to statically reserve area to host redundant components. The proposed method not only improves the survivability of the system by allowing the online replacement of defective key parts of the processor, but also provides performance graceful degradation by executing in software the tasks that were executed in hardware before a fault and the subsequent reconfiguration happened. The advantage of the proposed approach is that thanks to a hardware hypervisor, the CPU is totally unaware of the reconfiguration happening in real-time, and there's no dependency on the CPU to perform it. As proof of concept a design using this idea has been developed, using the LEON3 open-source processor, synthesized on a Virtex 4 FPGA.